A set of techniques used when variation in several variables has to be studied simultaneously. In statistics, multivariate analysis is interpreted as any analytic method that allows simultaneous study of two or more dependent variables.
A prediction of the probable outcome of a disease based on a individual's condition and the usual course of the disease as seen in similar situations.
Studies used to test etiologic hypotheses in which inferences about an exposure to putative causal factors are derived from data relating to characteristics of persons under study or to events or experiences in their past. The essential feature is that some of the persons under study have the disease or outcome of interest and their characteristics are compared with those of unaffected persons.
An aspect of personal behavior or lifestyle, environmental exposure, or inborn or inherited characteristic, which, on the basis of epidemiologic evidence, is known to be associated with a health-related condition considered important to prevent.
Evaluation undertaken to assess the results or consequences of management and procedures used in combating disease in order to determine the efficacy, effectiveness, safety, and practicability of these interventions in individual cases or series.
A class of statistical procedures for estimating the survival function (function of time, starting with a population 100% well at a given time and providing the percentage of the population still well at later times). The survival analysis is then used for making inferences about the effects of treatments, prognostic factors, exposures, and other covariates on the function.
The proportion of survivors in a group, e.g., of patients, studied and followed over a period, or the proportion of persons in a specified group alive at the beginning of a time interval who survive to the end of the interval. It is often studied using life table methods.
Studies in which individuals or populations are followed to assess the outcome of exposures, procedures, or effects of a characteristic, e.g., occurrence of disease.
Observation of a population for a sufficient number of persons over a sufficient number of years to generate incidence or mortality rates subsequent to the selection of the study group.
In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.
Statistical models used in survival analysis that assert that the effect of the study factors on the hazard rate in the study population is multiplicative and does not change over time.
Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor.
Period after successful treatment in which there is no appearance of the symptoms or effects of the disease.
Methods which attempt to express in replicable terms the extent of the neoplasm in the patient.
A nonparametric method of compiling LIFE TABLES or survival tables. It combines calculated probabilities of survival and estimates to allow for observations occurring beyond a measurement threshold, which are assumed to occur randomly. Time intervals are defined as ending each time an event occurs and are therefore unequal. (From Last, A Dictionary of Epidemiology, 1995)
Elements of limited time intervals, contributing to particular results or situations.
Studies in which subsets of a defined population are identified. These groups may or may not be exposed to factors hypothesized to influence the probability of the occurrence of a particular disease or other outcome. Cohorts are defined populations which, as a whole, are followed in an attempt to determine distinguishing subgroup characteristics.
Age as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or the effect of a circumstance. It is used with human or animal concepts but should be differentiated from AGING, a physiological process, and TIME FACTORS which refers only to the passage of time.
Molecular products metabolized and secreted by neoplastic tissue and characterized biochemically in cells or body fluids. They are indicators of tumor stage and grade as well as useful for monitoring responses to treatment and predicting recurrence. Many chemical groups are represented including hormones, antigens, amino and nucleic acids, enzymes, polyamines, and specific cell membrane proteins and lipids.
Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time.
A statistical technique that isolates and assesses the contributions of categorical independent variables to variation in the mean of a continuous dependent variable.
Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.
The qualitative or quantitative estimation of the likelihood of adverse effects that may result from exposure to specified health hazards or from the absence of beneficial influences. (Last, Dictionary of Epidemiology, 1988)
The ratio of two odds. The exposure-odds ratio for case control data is the ratio of the odds in favor of exposure among cases to the odds in favor of exposure among noncases. The disease-odds ratio for a cohort or cross section is the ratio of the odds in favor of disease among the exposed to the odds in favor of disease among the unexposed. The prevalence-odds ratio refers to an odds ratio derived cross-sectionally from studies of prevalent cases.
The local recurrence of a neoplasm following treatment. It arises from microscopic cells of the original neoplasm that have escaped therapeutic intervention and later become clinically visible at the original site.
Transfer of a neoplasm from its primary site to lymph nodes or to distant parts of the body by way of the lymphatic system.
Studies which start with the identification of persons with a disease of interest and a control (comparison, referent) group without the disease. The relationship of an attribute to the disease is examined by comparing diseased and non-diseased persons with regard to the frequency or levels of the attribute in each group.
The total number of cases of a given disease in a specified population at a designated time. It is differentiated from INCIDENCE, which refers to the number of new cases in the population at a given time.
The return of a sign, symptom, or disease after a remission.
A distribution in which a variable is distributed like the sum of the squares of any given independent random variable, each of which has a normal distribution with mean of zero and variance of one. The chi-square test is a statistical test based on comparison of a test statistic to a chi-square distribution. The oldest of these tests are used to detect whether two or more population distributions differ from one another.
Maleness or femaleness as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or effect of a circumstance. It is used with human or animal concepts but should be differentiated from SEX CHARACTERISTICS, anatomical or physiological manifestations of sex, and from SEX DISTRIBUTION, the number of males and females in given circumstances.
The number of new cases of a given disease during a given period in a specified population. It also is used for the rate at which new events occur in a defined population. It is differentiated from PREVALENCE, which refers to all cases, new or old, in the population at a given time.
Tumors or cancer of the human BREAST.
Levels within a diagnostic group which are established by various measurement criteria applied to the seriousness of a patient's disorder.
Histochemical localization of immunoreactive substances using labeled antibodies as reagents.
Predetermined sets of questions used to collect data - clinical data, social status, occupational group, etc. The term is often applied to a self-completed survey instrument.
The worsening of a disease over time. This concept is most often used for chronic and incurable diseases where the stage of the disease is an important determinant of therapy and prognosis.
The treatment of a disease or condition by several different means simultaneously or sequentially. Chemoimmunotherapy, RADIOIMMUNOTHERAPY, chemoradiotherapy, cryochemotherapy, and SALVAGE THERAPY are seen most frequently, but their combinations with each other and surgery are also used.
Measurable and quantifiable biological parameters (e.g., specific enzyme concentration, specific hormone concentration, specific gene phenotype distribution in a population, presence of biological substances) which serve as indices for health- and physiology-related assessments, such as disease risk, psychiatric disorders, environmental exposure and its effects, disease diagnosis, metabolic processes, substance abuse, pregnancy, cell line development, epidemiologic studies, etc.
Social and economic factors that characterize the individual or group within the social structure.
The use of two or more chemicals simultaneously or sequentially in the drug therapy of neoplasms. The drugs need not be in the same dosage form.
A malignant epithelial tumor with a glandular organization.
Tumors or cancer of the LIVER.
Pathologic processes that affect patients after a surgical procedure. They may or may not be related to the disease for which the surgery was done, and they may or may not be direct results of the surgery.
A graphic means for assessing the ability of a screening test to discriminate between healthy and diseased persons; may also be used in other studies, e.g., distinguishing stimuli responses as to a faint stimuli or nonstimuli.
Statistical models in which the value of a parameter for a given value of a factor is assumed to be equal to a + bx, where a and b are constants. The models predict a linear regression.
Tumors or cancer of the LUNG.
The presence of co-existing or additional diseases with reference to an initial diagnosis or with reference to the index condition that is the subject of study. Comorbidity may affect the ability of affected individuals to function and also their survival; it may be used as a prognostic indicator for length of hospital stay, cost factors, and outcome or survival.
Ability of neoplasms to infiltrate and actively destroy surrounding tissue.
Research techniques that focus on study designs and data gathering methods in human and animal populations.
Inhaling and exhaling the smoke of burning TOBACCO.
Includes the spectrum of human immunodeficiency virus infections that range from asymptomatic seropositivity, thru AIDS-related complex (ARC), to acquired immunodeficiency syndrome (AIDS).
Tumors or cancer of the COLON or the RECTUM or both. Risk factors for colorectal cancer include chronic ULCERATIVE COLITIS; FAMILIAL POLYPOSIS COLI; exposure to ASBESTOS; and irradiation of the CERVIX UTERI.
The probability that an event will occur. It encompasses a variety of measures of the probability of a generally unfavorable outcome.
A country in western Europe bordered by the Atlantic Ocean, the English Channel, the Mediterranean Sea, and the countries of Belgium, Germany, Italy, Spain, Switzerland, the principalities of Andorra and Monaco, and by the duchy of Luxembourg. Its capital is Paris.
An infant during the first month after birth.
Drug therapy given to augment or stimulate some other form of treatment such as surgery or radiation therapy. Adjuvant chemotherapy is commonly used in the therapy of cancer and can be administered before or after the primary treatment.
Mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components.
A carcinoma derived from stratified SQUAMOUS EPITHELIAL CELLS. It may also occur in sites where glandular or columnar epithelium is normally present. (From Stedman, 25th ed)
An indicator of body density as determined by the relationship of BODY WEIGHT to BODY HEIGHT. BMI=weight (kg)/height squared (m2). BMI correlates with body fat (ADIPOSE TISSUE). Their relationship varies with age and gender. For adults, BMI falls into these categories: below 18.5 (underweight); 18.5-24.9 (normal); 25.0-29.9 (overweight); 30.0 and above (obese). (National Center for Health Statistics, Centers for Disease Control and Prevention)
The simultaneous analysis of multiple samples of TISSUES or CELLS from BIOPSY or in vitro culture that have been arranged in an array format on slides or microchips.
Studies in which variables relating to an individual or group of individuals are assessed over a period of time.
A measure of the quality of health care by assessment of unsuccessful results of management and procedures used in combating disease, in individual cases or series.
A primary malignant neoplasm of epithelial liver cells. It ranges from a well-differentiated tumor with EPITHELIAL CELLS indistinguishable from normal HEPATOCYTES to a poorly differentiated neoplasm. The cells may be uniform or markedly pleomorphic, or form GIANT CELLS. Several classification schemes have been suggested.
Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)
A vital statistic measuring or recording the rate of death from any cause in hospitalized populations.
The systems and processes involved in the establishment, support, management, and operation of registers, e.g., disease registers.
Tumors or cancer of the STOMACH.
A CELL CYCLE and tumor growth marker which can be readily detected using IMMUNOCYTOCHEMISTRY methods. Ki-67 is a nuclear antigen present only in the nuclei of cycling cells.
The transfer of a neoplasm from one organ or part of the body to another remote from the primary site.
A range of values for a variable of interest, e.g., a rate, constructed so that this range has a specified probability of including the true value of the variable.
The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.
The study of chance processes or the relative frequency characterizing a chance process.
Methods which attempt to express in replicable terms the level of CELL DIFFERENTIATION in neoplasms as increasing ANAPLASIA correlates with the aggressiveness of the neoplasm.
The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.
A country spanning from central Asia to the Pacific Ocean.
A statistical analytic technique used with discrete dependent variables, concerned with separating sets of observed values and allocating new values. It is sometimes used instead of regression analysis.
Disease having a short and relatively severe course.
Statistical interpretation and description of a population with reference to distribution, composition, or structure.
The frequency of different ages or age groups in a given population. The distribution may refer to either how many or what proportion of the group. The population is usually patients with a specific disease but the concept is not restricted to humans and is not restricted to medicine.
NECROSIS of the MYOCARDIUM caused by an obstruction of the blood supply to the heart (CORONARY CIRCULATION).
Radiotherapy given to augment some other form of treatment such as surgery or chemotherapy. Adjuvant radiotherapy is commonly used in the therapy of cancer and can be administered before or after the primary treatment.
Parliamentary democracy located between France on the northeast and Portugual on the west and bordered by the Atlantic Ocean and the Mediterranean Sea.
Diseases which have one or more of the following characteristics: they are permanent, leave residual disability, are caused by nonreversible pathological alteration, require special training of the patient for rehabilitation, or may be expected to require a long period of supervision, observation, or care. (Dictionary of Health Services Management, 2d ed)
A heterogeneous aggregate of at least three distinct histological types of lung cancer, including SQUAMOUS CELL CARCINOMA; ADENOCARCINOMA; and LARGE CELL CARCINOMA. They are dealt with collectively because of their shared treatment strategy.
Tumors or cancer of the PROSTATE.
A malignant neoplasm made up of epithelial cells tending to infiltrate the surrounding tissues and give rise to metastases. It is a histological type of neoplasm but is often wrongly used as a synonym for "cancer." (From Dorland, 27th ed)
Therapeutic act or process that initiates a response to a complete or partial remission level.
Tumors or cancer of the ESOPHAGUS.
The science and art of collecting, summarizing, and analyzing data that are subject to random variation. The term is also applied to the data themselves and to the summarization of the data.
Excision of all or part of the liver. (Dorland, 28th ed)
Surgical excision of one or more lymph nodes. Its most common use is in cancer surgery. (From Dorland, 28th ed, p966)
A plasma protein that circulates in increased amounts during inflammation and after tissue damage.
Precursor of an alkylating nitrogen mustard antineoplastic and immunosuppressive agent that must be activated in the LIVER to form the active aldophosphamide. It has been used in the treatment of LYMPHOMA and LEUKEMIA. Its side effect, ALOPECIA, has been used for defleecing sheep. Cyclophosphamide may also cause sterility, birth defects, mutations, and cancer.
The period of confinement of a patient to a hospital or other health facility.
The number of males and females in a given population. The distribution may refer to how many men or women or what proportion of either in the group. The population is usually patients with a specific disease but the concept is not restricted to humans and is not restricted to medicine.
The transference of a part of or an entire liver from one human or animal to another.
The status during which female mammals carry their developing young (EMBRYOS or FETUSES) in utero before birth, beginning from FERTILIZATION to BIRTH.
Tomography using x-ray transmission and a computer algorithm to reconstruct the image.
Criteria and standards used for the determination of the appropriateness of the inclusion of patients with specific conditions in proposed treatment plans and the criteria used for the inclusion of subjects in various clinical trials and other research protocols.
Specific proteins found in or on cells of progesterone target tissues that specifically combine with progesterone. The cytosol progesterone-receptor complex then associates with the nucleic acids to initiate protein synthesis. There are two kinds of progesterone receptors, A and B. Both are induced by estrogen and have short half-lives.
Educational attainment or level of education of individuals.
Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.
A systematic collection of factual data pertaining to health and disease in a human population within a given geographic area.
Tumors or cancer of the OVARY. These neoplasms can be benign or malignant. They are classified according to the tissue of origin, such as the surface EPITHELIUM, the stromal endocrine cells, and the totipotent GERM CELLS.
Individuals whose ancestral origins are in the continent of Europe.
The inhabitants of a city or town, including metropolitan areas and suburban areas.
They are oval or bean shaped bodies (1 - 30 mm in diameter) located along the lymphatic system.
A performance measure for rating the ability of a person to perform usual activities, evaluating a patient's progress after a therapeutic procedure, and determining a patient's suitability for therapy. It is used most commonly in the prognosis of cancer therapy, usually after chemotherapy and customarily administered before and after therapy. It was named for Dr. David A. Karnofsky, an American specialist in cancer chemotherapy.
The confinement of a patient in a hospital.
Summarizing techniques used to describe the pattern of mortality and survival in populations. These methods can be applied to the study not only of death, but also of any defined endpoint such as the onset of disease or the occurrence of disease complications.
A group of pathological conditions characterized by sudden, non-convulsive loss of neurological function due to BRAIN ISCHEMIA or INTRACRANIAL HEMORRHAGES. Stroke is classified by the type of tissue NECROSIS, such as the anatomic location, vasculature involved, etiology, age of the affected individual, and hemorrhagic vs. non-hemorrhagic nature. (From Adams et al., Principles of Neurology, 6th ed, pp777-810)
Liver disease in which the normal microcirculation, the gross vascular anatomy, and the hepatic architecture have been variably destroyed and altered with fibrous septa surrounding regenerated or regenerating parenchymal nodules.
A class of statistical methods applicable to a large set of probability distributions used to test for correlation, location, independence, etc. In most nonparametric statistical tests, the original scores or observations are replaced by another variable containing less information. An important class of nonparametric tests employs the ordinal properties of the data. Another class of tests uses information about whether an observation is above or below some fixed value such as the median, and a third class is based on the frequency of the occurrence of runs in the data. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 4th ed, p1284; Corsini, Concise Encyclopedia of Psychology, 1987, p764-5)
Cytoplasmic proteins that bind estrogens and migrate to the nucleus where they regulate DNA transcription. Evaluation of the state of estrogen receptors in breast cancer patients has become clinically important.
Any of the processes by which nuclear, cytoplasmic, or intercellular factors influence the differential control of gene action in neoplastic tissue.
Pathological processes of CORONARY ARTERIES that may derive from a congenital abnormality, atherosclerotic, or non-atherosclerotic cause.
Transfer of HEMATOPOIETIC STEM CELLS from BONE MARROW or BLOOD between individuals within the same species (TRANSPLANTATION, HOMOLOGOUS) or transfer within the same individual (TRANSPLANTATION, AUTOLOGOUS). Hematopoietic stem cell transplantation has been used as an alternative to BONE MARROW TRANSPLANTATION in the treatment of a variety of neoplasms.
The capital is Seoul. The country, established September 9, 1948, is located on the southern part of the Korean Peninsula. Its northern border is shared with the Democratic People's Republic of Korea.
The inhabitants of rural areas or of small towns classified as rural.
The level of health of the individual, group, or population as subjectively assessed by the individual or by more objective measures.
The end-stage of CHRONIC RENAL INSUFFICIENCY. It is characterized by the severe irreversible kidney damage (as measured by the level of PROTEINURIA) and the reduction in GLOMERULAR FILTRATION RATE to less than 15 ml per min (Kidney Foundation: Kidney Disease Outcome Quality Initiative, 2002). These patients generally require HEMODIALYSIS or KIDNEY TRANSPLANTATION.
Neoplasms of the intracranial components of the central nervous system, including the cerebral hemispheres, basal ganglia, hypothalamus, thalamus, brain stem, and cerebellum. Brain neoplasms are subdivided into primary (originating from brain tissue) and secondary (i.e., metastatic) forms. Primary neoplasms are subdivided into benign and malignant forms. In general, brain tumors may also be classified by age of onset, histologic type, or presenting location in the brain.
Care given during the period prior to undergoing surgery when psychological and physical preparations are made according to the special needs of the individual patient. This period spans the time between admission to the hospital to the time the surgery begins. (From Dictionary of Health Services Management, 2d ed)
A cell surface protein-tyrosine kinase receptor that is overexpressed in a variety of ADENOCARCINOMAS. It has extensive homology to and heterodimerizes with the EGF RECEPTOR, the ERBB-3 RECEPTOR, and the ERBB-4 RECEPTOR. Activation of the erbB-2 receptor occurs through heterodimer formation with a ligand-bound erbB receptor family member.
Substances that inhibit or prevent the proliferation of NEOPLASMS.
Persistently high systemic arterial BLOOD PRESSURE. Based on multiple readings (BLOOD PRESSURE DETERMINATION), hypertension is currently defined as when SYSTOLIC PRESSURE is consistently greater than 140 mm Hg or when DIASTOLIC PRESSURE is consistently 90 mm Hg or more.
A generic concept reflecting concern with the modification and enhancement of life attributes, e.g., physical, political, moral and social environment; the overall condition of a human life.
Immunologic techniques based on the use of: (1) enzyme-antibody conjugates; (2) enzyme-antigen conjugates; (3) antienzyme antibody followed by its homologous enzyme; or (4) enzyme-antienzyme complexes. These are used histologically for visualizing or labeling tissue specimens.
Individuals whose ancestral origins are in the southeastern and eastern areas of the Asian continent.
Research aimed at assessing the quality and effectiveness of health care as measured by the attainment of a specified end result or outcome. Measures include parameters such as improved health, lowered morbidity or mortality, and improvement of abnormal states (such as elevated blood pressure).
Tumors or cancers of the KIDNEY.
Persons living in the United States having origins in any of the black groups of Africa.
An antitumor alkaloid isolated from VINCA ROSEA. (Merck, 11th ed.)
Removal and pathologic examination of specimens in the form of small pieces of tissue from the living body.
Preliminary cancer therapy (chemotherapy, radiation therapy, hormone/endocrine therapy, immunotherapy, hyperthermia, etc.) that precedes a necessary second modality of treatment.
Therapy for the insufficient cleansing of the BLOOD by the kidneys based on dialysis and including hemodialysis, PERITONEAL DIALYSIS, and HEMODIAFILTRATION.
A heterogeneous group of disorders characterized by HYPERGLYCEMIA and GLUCOSE INTOLERANCE.
A glycoprotein that is a kallikrein-like serine proteinase and an esterase, produced by epithelial cells of both normal and malignant prostate tissue. It is an important marker for the diagnosis of prostate cancer.
Sexual activities of humans.
Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references.
Transplantation between individuals of the same species. Usually refers to genetically disparate individuals in contradistinction to isogeneic transplantation for genetically identical individuals.
An invasive (infiltrating) CARCINOMA of the mammary ductal system (MAMMARY GLANDS) in the human BREAST.
Radiography of the vascular system of the heart muscle after injection of a contrast medium.
A group of people with a common cultural heritage that sets them apart from others in a variety of social relationships.
Non-invasive method of demonstrating internal anatomy based on the principle that atomic nuclei in a strong magnetic field absorb pulses of radiofrequency energy and emit them as radiowaves which can be reconstructed into computerized images. The concept includes proton spin tomographic techniques.
The survival of a graft in a host, the factors responsible for the survival and the changes occurring within the graft during growth in the host.
A heterogeneous group of sporadic or hereditary carcinoma derived from cells of the KIDNEYS. There are several subtypes including the clear cells, the papillary, the chromophobe, the collecting duct, the spindle cells (sarcomatoid), or mixed cell-type carcinoma.
Factors which produce cessation of all vital bodily functions. They can be analyzed from an epidemiologic viewpoint.
The application of probability and statistical methods to calculate the risk of occurrence of any event, such as onset of illness, recurrent disease, hospitalization, disability, or death. It may include calculation of the anticipated money costs of such events and of the premiums necessary to provide for payment of such costs.
Knowledge, attitudes, and associated behaviors which pertain to health-related topics such as PATHOLOGIC PROCESSES or diseases, their prevention, and treatment. This term refers to non-health workers and health workers (HEALTH PERSONNEL).
Antineoplastic antibiotic obtained from Streptomyces peucetius. It is a hydroxy derivative of DAUNORUBICIN.
The amount of BLOOD pumped out of the HEART per beat, not to be confused with cardiac output (volume/time). It is calculated as the difference between the end-diastolic volume and the end-systolic volume.
Proteins whose abnormal expression (gain or loss) are associated with the development, growth, or progression of NEOPLASMS. Some neoplasm proteins are tumor antigens (ANTIGENS, NEOPLASM), i.e. they induce an immune reaction to their tumor. Many neoplasm proteins have been characterized and are used as tumor markers (BIOMARKERS, TUMOR) when they are detectable in cells and body fluids as monitors for the presence or growth of tumors. Abnormal expression of ONCOGENE PROTEINS is involved in neoplastic transformation, whereas the loss of expression of TUMOR SUPPRESSOR PROTEINS is involved with the loss of growth control and progression of the neoplasm.
The total amount of radiation absorbed by tissues as a result of radiotherapy.
Complete or partial surgical removal of the prostate. Three primary approaches are commonly employed: suprapubic - removal through an incision above the pubis and through the urinary bladder; retropubic - as for suprapubic but without entering the urinary bladder; and transurethral (TRANSURETHRAL RESECTION OF PROSTATE).
An imbalance between myocardial functional requirements and the capacity of the CORONARY VESSELS to supply sufficient blood flow. It is a form of MYOCARDIAL ISCHEMIA (insufficient blood supply to the heart muscle) caused by a decreased capacity of the coronary vessels.
The period following a surgical operation.
Pathological conditions involving the CARDIOVASCULAR SYSTEM including the HEART; the BLOOD VESSELS; or the PERICARDIUM.
Continuance of life or existence especially under adverse conditions; includes methods and philosophy of survival.
PRESSURE of the BLOOD on the ARTERIES and other BLOOD VESSELS.
The presence of viable bacteria circulating in the blood. Fever, chills, tachycardia, and tachypnea are common acute manifestations of bacteremia. The majority of cases are seen in already hospitalized patients, most of whom have underlying diseases or procedures which render their bloodstreams susceptible to invasion.
A heterogeneous condition in which the heart is unable to pump out sufficient blood to meet the metabolic need of the body. Heart failure can be caused by structural defects, functional abnormalities (VENTRICULAR DYSFUNCTION), or a sudden overload beyond its capacity. Chronic heart failure is more common than acute heart failure which results from sudden insult to cardiac function, such as MYOCARDIAL INFARCTION.
A status with BODY WEIGHT that is grossly above the acceptable or desirable weight, usually due to accumulation of excess FATS in the body. The standards may vary with age, sex, genetic or cultural background. In the BODY MASS INDEX, a BMI greater than 30.0 kg/m2 is considered obese, and a BMI greater than 40.0 kg/m2 is considered morbidly obese (MORBID OBESITY).
A set of statistical methods used to group variables or observations into strongly inter-related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health-related phenomenon with well-defined distribution patterns in relation to time or place or both.
Conditions or pathological processes associated with the disease of diabetes mellitus. Due to the impaired control of BLOOD GLUCOSE level in diabetic patients, pathological processes develop in numerous tissues and organs including the EYE, the KIDNEY, the BLOOD VESSELS, and the NERVE TISSUE.
Recording of the moment-to-moment electromotive forces of the HEART as projected onto various sites on the body's surface, delineated as a scalar function of time. The recording is monitored by a tracing on slow moving chart paper or by observing it on a cardioscope, which is a CATHODE RAY TUBE DISPLAY.
A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
Nuclear phosphoprotein encoded by the p53 gene (GENES, P53) whose normal function is to control CELL PROLIFERATION and APOPTOSIS. A mutant or absent p53 protein has been found in LEUKEMIA; OSTEOSARCOMA; LUNG CANCER; and COLORECTAL CANCER.
Persons living in the United States of Mexican (MEXICAN AMERICANS), Puerto Rican, Cuban, Central or South American, or other Spanish culture or origin. The concept does not include Brazilian Americans or Portuguese Americans.
Ultrasonic recording of the size, motion, and composition of the heart and surrounding tissues. The standard approach is transthoracic.
Individuals supplying living tissue, organs, cells, blood or blood components for transfer or transplantation to histocompatible recipients.
The transference of a kidney from one human or animal to another.
Hospital units providing continuous surveillance and care to acutely ill patients.
A pyrimidine analog that is an antineoplastic antimetabolite. It interferes with DNA synthesis by blocking the THYMIDYLATE SYNTHETASE conversion of deoxyuridylic acid to thymidylic acid.
Excision of the whole (total gastrectomy) or part (subtotal gastrectomy, partial gastrectomy, gastric resection) of the stomach. (Dorland, 28th ed)
The total amount (cell number, weight, size or volume) of tumor cells or tissue in the body.
Behaviors associated with the ingesting of alcoholic beverages, including social drinking.
A principle of estimation in which the estimates of a set of parameters in a statistical model are those quantities minimizing the sum of squared differences between the observed values of a dependent variable and the values predicted by the model.
Application of statistical procedures to analyze specific observed or assumed facts from a particular study.
A malignant tumor arising from the epithelium of the BILE DUCTS.
A synthetic anti-inflammatory glucocorticoid derived from CORTISONE. It is biologically inert and converted to PREDNISOLONE in the liver.
A repeat operation for the same condition in the same patient due to disease progression or recurrence, or as followup to failed previous surgery.
Remnant of a tumor or cancer after primary, potentially curative therapy. (Dr. Daniel Masys, written communication)
Tumors or cancer of the RECTUM.
Treatment that combines chemotherapy with radiotherapy.
The use of IONIZING RADIATION to treat malignant NEOPLASMS and some benign conditions.
Systematic gathering of data for a particular purpose from various sources, including questionnaires, interviews, observation, existing records, and electronic devices. The process is usually preliminary to statistical analysis of the data.
The regular and simultaneous occurrence in a single interbreeding population of two or more discontinuous genotypes. The concept includes differences in genotypes ranging in size from a single nucleotide site (POLYMORPHISM, SINGLE NUCLEOTIDE) to large nucleotide sequences visible at a chromosomal level.
A stratum of people with similar position and prestige; includes social stratification. Social class is measured by criteria such as education, occupation, and income.
Neoplasms composed of glandular tissue, an aggregation of epithelial cells that elaborate secretions, and of any type of epithelium itself. The concept does not refer to neoplasms located in the various glands or in epithelial tissue.
Any of a group of malignant tumors of lymphoid tissue that differ from HODGKIN DISEASE, being more heterogeneous with respect to malignant cell lineage, clinical course, prognosis, and therapy. The only common feature among these tumors is the absence of giant REED-STERNBERG CELLS, a characteristic of Hodgkin's disease.
A malignant neoplasm derived from cells that are capable of forming melanin, which may occur in the skin of any part of the body, in the eye, or, rarely, in the mucous membranes of the genitalia, anus, oral cavity, or other sites. It occurs mostly in adults and may originate de novo or from a pigmented nevus or malignant lentigo. Melanomas frequently metastasize widely, and the regional lymph nodes, liver, lungs, and brain are likely to be involved. The incidence of malignant skin melanomas is rising rapidly in all parts of the world. (Stedman, 25th ed; from Rook et al., Textbook of Dermatology, 4th ed, p2445)
Statistical measures of utilization and other aspects of the provision of health care services including hospitalization and ambulatory care.

Prevention of collagen-induced arthritis by gene delivery of soluble p75 tumour necrosis factor receptor. (1/22244)

Collagen type II-induced arthritis (CIA) in DBA/1 mice can be passively transferred to SCID mice with spleen B- and T-lymphocytes. In the present study, we show that infection ex vivo of splenocytes from arthritic DBA/1 mice with a retroviral vector, containing cDNA for the soluble form of human p75 receptor of tumour necrosis factor (TNF-R) before transfer, prevents the development of arthritis, bone erosion and joint inflammation in the SCID recipients. Assessment of IgG subclass levels and studies of synovial histology suggest that down-regulating the effector functions of T helper-type 1 (Th1) cells may, at least in part, explain the inhibition of arthritis in the SCID recipients. In contrast, the transfer of splenocytes infected with mouse TNF-alpha gene construct resulted in exacerbated arthritis and enhancement of IgG2a antibody levels. Intriguingly, infection of splenocytes from arthritic DBA/1 mice with a construct for mouse IL-10 had no modulating effect on the transfer of arthritis. The data suggest that manipulation of the immune system with cytokines, or cytokine inhibitors using gene transfer protocols can be an effective approach to ameliorate arthritis.  (+info)

Heart rate and subsequent blood pressure in young adults: the CARDIA study. (2/22244)

The objective of the present study was to examine the hypothesis that baseline heart rate (HR) predicts subsequent blood pressure (BP) independently of baseline BP. In the multicenter longitudinal Coronary Artery Risk Development in Young Adults study of black and white men and women initially aged 18 to 30 years, we studied 4762 participants who were not current users of antihypertensive drugs and had no history of heart problems at the baseline examination (1985-1986). In each race-sex subgroup, we estimated the effect of baseline HR on BP 2, 5, 7, and 10 years later by use of repeated measures regression analysis, adjusting for baseline BP, age, education, body fatness, physical fitness, fasting insulin, parental hypertension, cigarette smoking, alcohol consumption, oral contraceptive use, and change of body mass index from baseline. The association between baseline HR and subsequent systolic BP (SBP) was explained by multivariable adjustment. However, HR was an independent predictor of subsequent diastolic BP (DBP) regardless of initial BP and other confounders in white men, white women, and black men (0.7 mm Hg increase per 10 bpm). We incorporated the part of the association that was already present at baseline by not adjusting for baseline DBP: the mean increase in subsequent DBP was 1.3 mm Hg per 10 bpm in white men, white women, and black men. A high HR may be considered a risk factor for subsequent high DBP in young persons.  (+info)

Antiphospholipid, anti-beta 2-glycoprotein-I and anti-oxidized-low-density-lipoprotein antibodies in antiphospholipid syndrome. (3/22244)

Antiphospholipid antibodies (aPL), anti-beta 2-glycoprotein I (anti-beta 2-GPI) and anti-oxidized-low-density lipoprotein (LDL) antibodies are all implicated in the pathogenesis of antiphospholipid syndrome. To investigate whether different autoantibodies or combinations thereof produced distinct effects related to their antigenic specificities, we examined the frequencies of antiphospholipid syndrome (APS)-related features in the presence of different antibodies [aPL, beta 2-GPI, anti-oxidized low density lipoprotein (LDL)] in 125 patients with APS. Median follow-up was 72 months: 58 patients were diagnosed as primary APS and 67 as APS plus systemic lupus erythematosus (SLE). Anticardiolipin antibodies (aCL), anti-beta 2-GPI and anti-oxidized LDL antibodies were determined by ELISA; lupus anticoagulant (LA) by standard coagulometric methods. Univariate analysis showed that patients positive for anti-beta 2-GPI had a higher risk of recurrent thrombotic events (OR = 3.64, 95% CI, p = 0.01) and pregnancy loss (OR = 2.99, 95% CI, p = 0.004). Patients positive for anti-oxidized LDL antibodies had a 2.24-fold increase in the risk of arterial thrombosis (2.24, 95% CI, p = 0.03) and lower risk of thrombocytopenia (OR = 0.41 95% CI, p = 0.04). Patients positive for aCL antibodies had a higher risk of pregnancy loss (OR = 4.62 95% CI, p = 0.001). When these data were tested by multivariate logistic regression, the association between anti-beta 2-GPI and pregnancy loss and the negative association between anti-oxidized LDL antibodies and thrombocytopenia disappeared.  (+info)

Different factors influencing the expression of Raynaud's phenomenon in men and women. (4/22244)

OBJECTIVE: To determine whether the risk profile for Raynaud's phenomenon (RP) is different between men and women. METHODS: In this cross-sectional study of 800 women and 725 men participating in the Framingham Offspring Study, the association of age, marital status, smoking, alcohol use, diabetes, hypertension, and hypercholesterolemia with prevalent RP was examined in men and women separately, after adjusting for relevant confounders. RESULTS: The prevalence of RP was 9.6% (n = 77) in women and 5.8% (n = 42) in men. In women, marital status and alcohol use were each associated with prevalent RP (for marital status adjusted odds ratio [OR] 2.3, 95% confidence interval [95% CI] 1.4-3.9; for alcohol use OR 2.2, 95% CI 1.0-5.2), whereas these factors were not associated with RP in men (marital status OR 1.4, 95% CI 0.6-3.5; alcohol use OR 1.0, 95% CI 0.2-4.4). In men, older age (OR 2.3, 95% CI 1.0-5.2) and smoking (OR 2.6, 95% CI 1.1-6.3) were associated with prevalent RP; these factors were not associated with RP in women (older age OR 0.8, 95% CI 0.4-1.6; smoking OR 0.7, 95% CI 0.4-1.1). Diabetes, hypertension, and hypercholesterolemia were not associated with RP in either sex. CONCLUSION: The results indicate that risk factors for RP differ between men and women. Age and smoking were associated with RP in men only, while the associations of marital status and alcohol use with RP were observed in women only. These findings suggest that different mechanisms influence the expression of RP in men and women.  (+info)

Evidence of improving survival of patients with rectal cancer in france: a population based study. (5/22244)

BACKGROUND: Over the past 20 years there have been many changes in the management of rectal cancer. Their impact on the overall population is not well known. AIMS: To determine trends in management and prognosis of rectal cancer in two French regions. SUBJECTS: 1978 patients with a rectal carcinoma diagnosed between 1978 and 1993. METHODS: Time trends in treatment, stage at diagnosis, operative mortality, and survival were studied on a four year basis. A non-conditional logistic regression was performed to obtain an odds ratio for each period adjusted for the other variables. To estimate the independent effect of the period a multivariate relative survival analysis was performed. RESULTS: Over the 16 year period resection rates increased from 66.0% to 80.1%; the increase was particularly noticeable for sphincter saving procedures (+30.6% per four years, p=0.03). The percentage of patients receiving adjuvant radiotherapy increased from 24.0% to 40.0% (p=0.02). The proportion of patients with Dukes' type A cancer increased from 17. 7% to 30.6% with a corresponding decrease in those with more advanced disease. Operative mortality decreased by 31.1% per four years (p=0.03). All these improvements have resulted in a dramatic increase in relative survival (from 35.4% for the 1978-1981 period to 57.0% for the 1985-1989 period). CONCLUSIONS: Substantial advances in the management of rectal cancer have been achieved, but there is evidence that further improvements can be made in order to increase survival.  (+info)

Illness behaviour in elite middle and long distance runners. (6/22244)

OBJECTIVES: To examine the illness attitudes and beliefs known to be associated with abnormal illness behaviour (where symptoms are present in excess of objective signs and pathology) in elite middle and long distance runners, in comparison with non-athlete controls. METHODS: A total of 150 athletes were surveyed using the illness behaviour questionnaire as an instrument to explore the psychological attributes associated with abnormal illness behaviour. Subjects also completed the general health questionnaire as a measure of psychiatric morbidity. A control group of 150 subjects, matched for age, sex, and social class, were surveyed using the same instruments. RESULTS: A multivariate analysis of illness behaviour questionnaire responses showed that the athletes' group differed significantly from the control group (Hotelling's T: Exact F = 2.68; p = 0.01). In particular, athletes were more somatically focused (difference between means -0.27; 95% confidence interval -0.50 to -0.03) and more likely to deny the impact of stresses in their life (difference between means 0.78; 95% confidence interval 0.31 to 1.25). Athletes were also higher scorers on the Whiteley Index of Hypochondriasis (difference between means 0.76; 95% confidence interval 0.04 to 1.48). There were no differences in the levels of psychiatric morbidity between the two groups. CONCLUSIONS: The illness attitudes and beliefs of athletes differ from those of a well matched control population. The origin of these psychological attributes is not clear but those who treat athletes need to be aware of them.  (+info)

One-year survival among patients with acute myocardial infarction complicated by cardiogenic shock, and its relation to early revascularization: results from the GUSTO-I trial. (7/22244)

BACKGROUND: Although 30-day survival is increased in patients with acute myocardial infarction complicated by cardiogenic shock who undergo coronary revascularization, the longer-term outcome in such patients and the duration of benefit from revascularization are unknown. METHODS AND RESULTS: We analyzed 30-day survivors of acute myocardial infarction in the Global Utilization of Streptokinase and Tissue-Plasminogen Activator for Occluded Coronary Arteries (GUSTO-I) trial and identified 36 333 who had not had cardiogenic shock (systolic blood pressure <90 mm Hg for >/=1 hour, group 1) and 1321 patients who had shock (group 2). Group 2 patients were older and sicker. At 1 year, 97.4% of group 1 patients were alive versus 88.0% of group 2 (P=0.0001). Among group 2 patients, 578 (44%) had undergone revascularization within 30 days (group 2A) and 728 (56%) had not (group 2B). Revascularization was not required by protocol but was selected by the attending physicians. At 1 year, 91.7% of group 2A patients were alive versus 85.3% of group 2B (P=0.0003). With the use of multivariable logistic regression analysis to adjust for differences in baseline characteristics of shock patients alive at 30 days, revascularization within 30 days was independently associated with reduced 1-year mortality (odds ratio 0.6, [95% confidence interval 0.4, 0.9], P=0.007). CONCLUSIONS: Most patients (88%) with acute myocardial infarction complicated by cardiogenic shock who are alive at 30 days survived at least 1 year. Shock patients who underwent revascularization within 30 days had improved survival at 1 year compared with shock patients who did not receive revascularization, even after adjustment for differences in baseline characteristics between the 2 groups.  (+info)

New-onset sustained ventricular tachycardia after cardiac surgery. (8/22244)

BACKGROUND: The de novo occurrence of sustained ventricular tachycardia (VT) after CABG has been described, but the incidence, mortality rate, long-term follow-up, and mechanism are not well defined. METHODS AND RESULTS: This prospective study enrolled consecutive patients undergoing CABG at a single institution. Patients were followed up for the development of sustained VT, and a detailed analysis of clinical, angiographic, and surgical variables associated with the occurrence of VT was performed. A total of 382 patients participated, and 12 patients (3.1%) experienced >/=1 episode of sustained VT 4.1+/-4.8 days after CABG. In 11 of 12 patients, no postoperative complication explained the VT; 1 patient had a perioperative myocardial infarction. The in-hospital mortality rate was 25%. Patients with VT were more likely to have prior myocardial infarction (92% versus 50%, P<0.01), severe congestive heart failure (56% versus 21%, P<0.01), and ejection fraction <0.40 (70% versus 29%, P<0.01). When all 3 factors were present, the risk of VT was 30%, a 14-fold increase. Patients with VT had more noncollateralized totally occluded vessels on angiogram (1.4+/-0.97 versus 0.54+/-0.7, P<0.01), a bypass graft across a noncollateralized occluded vessel (1.50+/-1.0 versus 0.42+/-0.62, P<0.01), and a bypass graft across a noncollateralized occluded vessel to an infarct zone (1.50+/-1.0 versus 0.17+/-0.38, P<0.01). By multivariate analysis, the number of bypass grafts across a noncollateralized occluded vessel to an infarct zone was the only independent factor predicting VT. CONCLUSIONS: The first presentation of sustained monomorphic VT in the recovery period after CABG is uncommon, but the incidence is high in specific clinical subsets. Placement of a bypass graft across a noncollateralized total occlusion in a vessel supplying an infarct zone was strongly and independently associated with the development of VT.  (+info)

[2] Sharma Subhash, Applied Multivariate Techniques, John Wiley & Sons, Inc. 1996. week Subject Reading #1 Aspects of Multivariate Analysis Matrix Algebra and Random Vectors [1] chapter University of Toronto. Gives readers the knowledge necessary to make proper interpretations and select appropriate Sharma, S, 1996, Applied Multivariate Techniques, John Wiley & Sons, New York Siswadi dan Suharjo, B, 1997, Analisis Eksplorasi Data Peubah 4.1 out of 5 stars 46. Applied Multivariate Statistical Analysis Paperback - March 13, 2015 by Wichern Johnson (Author) 3.9 out of 5 stars 35 ratings. ~~ Book Applied Multivariate Statistical Analysis 6th Edition ~~ Uploaded By Clive Cussler, applied multivariate statistical analysis 6th edition richard a johnson dean w wichern this market leader offers a readable introduction to the statistical analysis of multivariate observations gives readers the knowledge necessary to make proper June 14th, 2010 - Applied Multivariate Statistical Analysis
This text takes a practical approach to multivariate data analysis, with an introductionto the most commonly encountered statistical and multivariate techniques. Using Multivariate Statistics provides practical guidelines for conducting numerous types of multivariate statistical analyses. It gives syntax and output for accomplishing many analyses through the most recent releases of SAS, SPSS, and SYSTAT, some not available in software manuals. The book maintains its practical approach, still focusing on the benefits and limitations of applications of a technique to a data set - when, why, and how to do it. Overall, it provides advanced students with a timely and comprehensive introduction to todays most commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge ofhigher-level mathematics. *A new chapter on survival analysis (Ch. 15) allows students to analyze data where the outcome is time until something happens. This is very popular in biomedical research.
This text takes a practical approach to multivariate data analysis, with an introductionto the most commonly encountered statistical and multivariate techniques. Using Multivariate Statistics provides practical guidelines for conducting numerous types of multivariate statistical analyses. It gives syntax and output for accomplishing many analyses through the most recent releases of SAS, SPSS, and SYSTAT, some not available in software manuals. The book maintains its practical approach, still focusing on the benefits and limitations of applications of a technique to a data set - when, why, and how to do it. Overall, it provides advanced students with a timely and comprehensive introduction to todays most commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge ofhigher-level mathematics. *A new chapter on survival analysis (Ch. 15) allows students to analyze data where the outcome is time until something happens. This is very popular in biomedical research.
0.001264578! Annotated Bibliography of Works on Extensive Reading in ESL. Applied Multivariate Statistical Analysis Homework Solutions And to those students, who donâ t like writing in general, any new writing assignment becomes a struggle. We hope this manual is a useful aid for adopters of our Applied Multivariate Statistical Analysis, 6th edition, text. Subjects in English 2017 2018 University of Ljubljana. Instructors Solutions Manual (Download only) for Applied Multivariate Statistical Analysis, 6th Edition Richard Johnson, University of Wisconsin-Madison Dean W. Wichern, Texas A&M University, College Station Thus, for example, 9.6 refers to the 6th exercise of chapter 9. Erasmus Universiteit Rotterdam. Applied Multivariate Analysis-Outline Homework Constraints Homework should be detailed enough to adequately demonstrate your solution. The book demonstrates the application of simple calculus and basic multivariate methods in real life situations. Our solutions are written by Chegg experts ...
Given the increasing complexity of modern HEP experiments, multivariate analysis techniques have proven indispensable to extract the maximum information content out of the data. At this workshop we will review recent developments on multivariate methods in TMVA (the Toolkit for Multivariate Data Analysis in ROOT), discuss user experience and feedback, and explain the use of TMVA by means of a hands-on tutorial. The workshop directly succeeds Phystat 2011 at CERN. EVO phone connection will be provided for remote participants.. The schedule leaves room for user feedback on TMVA, and ideas for new multivariate approaches or improvements on existing methods.. Please submit talk proposals to [email protected] If you wish to attend the workshop and/or TMVA tutorial please register as soon as possible, so that we can plan for an appropriate location at CERN. ...
The ecological sciences have experienced immense growth over the course of this century, and chances are that they will continue to grow well on into the next millennium. There are some good reasons for this - ecology encompasses some of the most pressing concerns facing humanity. With recent advances in data collection technology and ambitious field research, ecologists are increasingly calling upon multivariate statistics to explore and test for patterns in their data. The goal of FISH 560 (Applied Multivariate Statistics for Ecologists) at the University of Washington is to introduce graduate students to the multivariate statistical techniques necessary to carry out sophisticated analyses and to critically evaluate scientific papers using these approaches. It is a practical, hands-on course emphasizing the analysis and interpretation of multivariate analysis, and covers the majority of approaches in common use by ecologists. To celebrate the hard work of past students, I am pleased to ...
Over recent releases, Statistics and Machine Learning Toolbox has included new functionality for multivariate classification methods, including cross-validation, feature selection, Naïve Bayes, bagged decision trees, ROC performance curves
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Biomarkers discovery is a discipline achieving increasing importance since it provides diagnostic/prognostic markers and may permit to investigate and understand the mech..
Rebecca M. Warners bestselling Applied Statistics: From Bivariate Through Multivariate Techniques has been split into two volumes for ease of use over a two-course sequence. This new multivariate statistics text, Applied Statistics II: Multivariable and Multivariate Techniques, Third Edition is based on chapters from the second half of original book, but with much additional material. This text now provides a distinctive bridge between earlier courses and advanced topics through extensive discussion of statistical control (adding a third variable), a new chapter on the new statistics, a new chapter on outliers and missing values, and a final chapter that provides an introduction to structural equation modeling. This text provides a solid introduction to concepts such as statistical control, mediation, moderation, and path modeling necessary to students taking intermediate and advanced statistics courses across the social sciences. Examples are provided in SPSS with datasets available on an ...
Regression modelling is one of the most widely utilized approaches in epidemiological analyses. It provides a method of identifying statistical associations, from which potential causal associations relevant to disease control may then be investigated. Multivariable regression - a single dependent variable (outcome, usually disease) with multiple independent variables (predictors) - has long been the standard model. Generalizing multivariable regression to multivariate regression - all variables potentially statistically dependent - offers a far richer modelling framework. Through a series of simple illustrative examples we compare and contrast these approaches. The technical methodology used to implement multivariate regression is well established - Bayesian network structure discovery - and while a relative newcomer to the epidemiological literature has a long history in computing science. Applications of multivariate analysis in epidemiological studies can provide a greater understanding of disease
A multivariate study on Ease of Doing Business to predict a country - Sugabalan Sivagnanam - Masterarbeit - Statistik - Publizieren Sie Ihre Hausarbeiten, Referate, Essays, Bachelorarbeit oder Masterarbeit
Significantly revised and expanded, Multivariate Statistical Analysis, Second Edition addresses several added topics related to the properties and characterization of symmetric distributions, elliptically symmetric multivariate distributions, singular…
This course teaches how to apply and interpret a variety of multivariate statistical methods to research and business data. The course emphasizes understanding the results of the analysis and presenting your conclusions with graphs.
This course teaches how to apply and interpret a variety of multivariate statistical methods to research and business data. The course emphasizes understanding the results of the analysis and presenting your conclusions with graphs.
Buy Applied Multivariate Statistical Analysis by Richard A. Johnson online at Alibris. We have new and used copies available, in 8 editions - starting at $1.45. Shop now.
An applied treatment of the key methods and state-of-the-art tools for visualizing and understanding statistical data Smoothing of Multivariate Data provides an illustrative and hands-on approach to the multivariate aspects of density estimation, emphasizing the use of visualization tools. Rather than outlining the theoretical concepts of classification and regression, this book focuses on the procedures for estimating a multivariate distribution via smoothing.. The author first provides an introduction to various visualization tools that can be used to construct representations of multivariate functions, sets, data, and scales of multivariate density estimates. Next, readers are presented with an extensive review of the basic mathematical tools that are needed to asymptotically analyze the behavior of multivariate density estimators, with coverage of density classes, lower bounds, empirical processes, and manipulation of density estimates. The book concludes with an extensive toolbox of ...
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Descriptive statistics multivariate data set are describe such as measure of location, measure of dispersion, sample correlation coefficient and covariance
Two statistical methods for investigating relationships between data samples are bivariate analysis and multivariate analysis. Bivariate analysis looks at whether a relationship exists between two paired data sets. Multivariate analysis looks at whether two or more variables are correlated.
David Calder, Six Sigma Black Belt, Magna International Wayne Levin, President, Predictum Over the years, automotive exterior parts have become more complex and substantially larger, yet are molded at faster cycle times. The transformation in design and challenging manufacturing demands have driven changes in tool design, hot runner design, material formulation and molding machine functionality. With these increasing challenges, we have to ask ourselves if conventional methods of quality control, which are typically univariate, are still effective. The short answer is no. This presentation demonstrates how multivariate analysis extracts pertinent information from large amounts of complex data. It is then able to identify the correlation structure and relationships that exist between multiple process variables and present it visually. Well present a project comparing univariate and multivariate approaches. These methods hold the promise to both reduce the dependency on subjective, visual inspection
This course aims to equip participants with the necessary skills to analyse large survey data sets to answer research and policy questions. Students will be introduced to a number of multivariate statistical methods for analysing numeric, categorical and censored data as well as techniques for analysing event-history data. Methods covered include multinomial logistic regression, survival analysis and cox regression. Participants will learn to use a computer statistical software package such as SPSS to apply the methods to survey data and to interpret and discuss the results of their data analysis. Teaching is provided through lectures, tutorials and computer training sessions. This course will provide students with the skills necessary to undertake, or assess, complex quantitative research. ...
Most of the observable phenomena in the empirical sciences are of a multivariate nature. In financial studies, assets are observed simultaneously and their joint development is analysed to better und
This course will provide students with an introduction to advanced topics in survey data analysis. In Survey Data Analysis DEMO8014 students are introduced to basic multivariate statistical methods for analysing survey data. The present course will provide several important extensions: (1) dealing with sample selection bias and endogeneity bias in survey data (instrumental variable regression and Heckman selection correction); (2) using panel data to control for unobserved heterogeneity (fixed effects and random effects models); (3) modelling the time to an event (survival analysis) and (4) multi-level modelling for hierarchical or clustered data. Participants will gain experience in using the Stata statistical software package to apply these methods to survey data. ...
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself , and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models.. ...
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself , and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models.. ...
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself , and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models.. ...
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself , and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models.. ...
Described are a system and method for identifying a material. A spectrum is received. A barcode is generated from a sign of a second derivative of the spectrum. Multivariate data analysis tools and te
Krzanowski, W. J. (1988) Principles of Multivariate Analysis. A Users Perspective. Oxford. Hand, D. J. and Taylor, C. C. (1987) Multivariate Analysis of Variance and Repeated Measures. Chapman and Hall. ...
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Comments: ALTERNATE EDITION: International Edition. Paperback. Brand New. Different ISBN number and Cover Page but contents similer to US edition , however the end chapter exercises may differ. No CD/Access code with this. Legal to use despite any disclaimer on cover as per US courts. Choose Expedited shipping for 3-5 business days delivery. We can ship to PO Box Addresses ...
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Developing a framework where continuous and noncontinuous variables can be analyzed simultaneously wont be easy. For starters, most of the techniques that statisticians have developed for parallel analysis of three or more variables - a process called multivariate analysis - only work for continuous data.. This network model shows a half-million biomarkers related to the type of brain cancer known as glioblastoma. The lines represent conditionally dependent connections between biomarkers.. It is a multivariate problem, and thats how were approaching it, Allen said. But a proper multivariate distribution does not exist for this, so we have to create one mathematically.. To do this, Allen and her collaborators - co-PIs Zhandong Liu of BCM and Pradeep Ravikumar of UT Austin - are creating a mathematical framework that will allow them to find the conditional dependence relationships between any two variables.. To illustrate how conditional dependence works, Allen suggested considering ...
Presents both theory and practical implementation of multivariate statistical analysis in LISREL Illustrates all methods and procedures on numerous
When should you use multivariate testing, and when is A/B/n testing best? This guide shows you how to run multivariate tests correctly and for best results.
In order to reduce computations involved in multivariate estimation of variance components, a sequence of univariate analyses is suggested based on analysing transformed traits and sums of pairs of transformed traits. The efficiency of the method is investigated ...
function nag_mv_canon_corr_example z = [80, 58.4, 14, 21; 75, 59.2, 15, 27; 78, 60.3, 15, 27; 75, 57.4, 13, 22; 79, 59.5, 14, 26; 78, 58.1, 14.5, 26; 75, 58, 12.5, 23; 64, 55.5, 11, 22; 80, 59.2, 12.5, 22]; isz = [int64(-1);1;1;-1]; nx = int64(2); ny = int64(2); mcv = int64(2); tol = 1e-06; [e, ncv, cvx, cvy, ifail] = nag_mv_canon_corr(z, isz, nx, ny, mcv, tol ...
Hi All, I had a quick question about how to determine the cut off p value in order to include relevant variables from univariate analysis into multivariate regression analysis. For example, in my analysis- I am looking …
IBM® SPSS® Amos is powerful structural equation modeling software that enables you to support your research and theories by extending standard multivariate analysis methods, including regression, factor analysis, correlation, and analysis of variance. With SPSS Amos you can build attitudinal and behavioral models that reflect complex relationships more accurately than with standard multivariate statistics techniques using either an intuitive graphical, or programmatic user interface.. On-screen model to results ...
In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned ...
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Multivariate analysis of dynamical processes 的翻译是:动态过程的多元分析 是什么意思?英文翻译中文,中文翻译英文,怎么说?-我要翻译网
Hello, I have one of the annoying which test? questions. I have trivariate metric data (x1,x2,x3) from a repeated measures design with 2 factors (one with 2 levels, one with 8; both IVs are within participant). 16 participants were tested. I would like to know whether either of the two ...
Multivariate statistical analysis, 7,5 cr Study period 3 Course information The course is given jointly with Göteborg university, MSA200. The course is given every second year. Academic year 08/09 . Examiner: Nanny Wermuth Academic year 10/11 . Examiner: Rebecka Jörnsten Academic year 12/13 . Examin
Dells acquisition of StatSoft reinforces the growing acknowledgement that advanced data analytics are essential in todays complex world.
The complexity of social problems necessitates that social work researchers utilize multivariate statistical methods in their investigations. Having a thorough understanding of basic statistics can ... More. The complexity of social problems necessitates that social work researchers utilize multivariate statistical methods in their investigations. Having a thorough understanding of basic statistics can facilitate this process as multivariate methods have as their foundation many of these basic statistical procedures. In this pocket guide, the authors introduce readers to three of the more frequently used multivariate statistical methods in social work research-multiplelinear regression analysis,analysis of variance and covariance, and path analysis-with an emphasis on the basic statistics as important features of these methods. The primary intention is to help prepare entry level doctoral students and early career social work researchers in the use of multivariate statistical methods by offering ...
The complexity of social problems necessitates that social work researchers utilize multivariate statistical methods in their investigations. Having a thorough understanding of basic statistics can facilitate this process as multivariate methods have as their foundation many of these basic statistical procedures. In this pocket guide, the authors introduce readers to three of the more frequently used multivariate statistical methods in social work research-multiplelinear regression analysis,analysis of variance and covariance, and path analysis-with an emphasis on the basic statistics as important features of these methods. The primary intention is to help prepare entry level doctoral students and early career social work researchers in the use of multivariate statistical methods by offering a straightforward and easy to understand explanation of these methods and the basic statistics that inform them. The pocket guide begins with a review of basic statistics, hypothesis testing with inferential
Multivariate Data Analysis Using the latest multivariate techniques, participants will learn how to interpret complex data quickly and confidently. Discover the secrets of overviewing data tables and also learn how to build robust predictive models that turn data into decisions. The course is composed of lectures, soft
Multivariate Data Analysis Using the latest multivariate techniques, participants will learn how to interpret complex data quickly and confidently. Discover the secrets of overviewing data tables and also learn how to build robust predictive models that turn data into decisions. The course is composed of lectures, soft
TY - JOUR. T1 - Assessing the severity of psoriasis through multivariate analysis of optical images from non-lesional skin. AU - Žurauskas, Mantas. AU - Barkalifa, Ronit. AU - Alex, Aneesh. AU - Marjanovic, Marina. AU - Spillman, Darold R.. AU - Mukherjee, Prabuddha. AU - Neitzel, Craig D.. AU - Lee, Warren. AU - Medler, Jeremy. AU - Arp, Zane. AU - Cleveland, Matthew. AU - Hood, Steve. AU - Boppart, Stephen A.. N1 - Funding Information: This study was funded by GlaxoSmithKline (Carle Foundation Hospital 16090; University of Illinois at Urbana-Champaign 11012). The authors acknowledge and appreciate the logistical assistance from the research staff at Carle Foundation Hospital, and specifically thank Paula Bradley, Ali Moll, Nandini Goswami, and the clinical staff in the Department of Dermatology, Carle Foundation Hospital, for their assistance in this study. Funding Information: S.A.B. receives research grant support from GlaxoSmithKline related to the research described here, and reports ...
Hi everyone, Ive been running Multivariate linear regression models and was wondering if there was a way to extract the full equation used by Spotfire to obtain its predicted response values? Thanks!Hi everyone, Ive been running Multivariate linear regression models and was wondering if there was a way to extract the full equation used by Spotfire to obtain its predicted
Elementary treatment of multivariate normal distributions, classical inferential techniques for multivariate normal data, including Hotellings T and MANOVA. Discussion of analytic techniques such as principal component analysis, canonical correlation analysis, discriminant analysis, and factor analysis ...
Preface. Glossary of Notation and Abbreviations.. 1 Multivariate Normal and Related Distributions.. 1.1 Random Vectors.. 1.1.1 Mean Vector and Covariance Matrix.. 1.1.2 Characteristic Function and Distribution.. 1.2 Multivariate Normal Distribution.. 1.2.1 Bivariate Normal Distribution.. 1.2.2 Definition.. 1.2.3 Some Properties.. 1.3 Spherical and Elliptical Distributions.. 1.4 Multivariate Cumulants.. Problems.. 2 Wishart Distribution.. 2.1 Definition.. 2.2 Some Basic Properties.. 2.3 Functions of Wishart Matrices.. 2.4 Cochrans Theorem.. 2.5 Asymptotic Distributions.. Problems.. 3 Hotellings T2 and Lambda Statistics.. 3.1 Hotellings T2 and Lambda Statistics.. 3.1.1 Distribution of the T2 Statistic.. 3.1.2 Decomposition of T2 and D2.. 3.2 Lambda-Statistic.. 3.2.1 Motivation of Lambda Statistic.. 3.2.2 Distribution of Lambda Statistic.. 3.3 Test for Additional Information.. 3.3.1 Decomposition of Lambda Statistic.. Problems.. 4 Correlation Coefficients.. 4.1 Ordinary Correlation ...
Multivariate Analysis Resource at defaultlogic.com | Learn the facts on Multivariate Analysis. Watch videos, join the discussion and find answers on Multivariate Analysis.
18 Make a draftman plot for the car data with the variables X1 X2 X8 X9 = = = = price, mileage, weight, length. Move the brush into the region of heavy cars. What can you say about price, mileage and length? Move the brush onto high fuel economy. S. American cars. 3. 19 What is the form of a scatterplot of two independent random variables X1 and X2 with standard Normal distribution? 20 Rotate a three-dimensional standard normal point cloud in 3D space. Does it almost look the same from all sides? If one element of X is unusual, the corresponding face element significantly changes in shape. 18. Chernoff-Flury faces for observations 101 to 150 of the bank notes. 19. Chernoff-Flury faces for observations 151 to 200 of the bank notes. 6 39 Andrews Curves The basic problem of graphical displays of multivariate data is the dimensionality. Scatterplots work well up to three dimensions (if we use interactive displays). , faces). The idea of coding and representing multivariate data by curves was ...
This study uses multivariate statistics to analyze offence specific crime rates in cities across Canada. Various crime patterns were identified in 600 cities nationwide. Identifying offence specific crime patterns within a city help local police agencies to develop crime specific prevention programs.
TY - BOOK. T1 - Exploratory Analysis of Multivariate Data (Unsupervised Image Segmentation and Data Driven Linear and Nonlinear Decomposition). AU - Hilger, Klaus Baggesen. PY - 2002/3. Y1 - 2002/3. N2 - This work describes different methods that are useful in the analysis of multivariate single and multiset data. The thesis covers selected aspects of relevant data analysis techniques in this context. Methods dedicated to handling data of a spatial nature are of primary interest with focus on data driven exploratory methods for i) clustering, and for both ii) linear and iii) nonlinear decompositioning. New extensions are presented in all three fields.. AB - This work describes different methods that are useful in the analysis of multivariate single and multiset data. The thesis covers selected aspects of relevant data analysis techniques in this context. Methods dedicated to handling data of a spatial nature are of primary interest with focus on data driven exploratory methods for i) clustering, ...
Study of multivariate normal distribution, estimation and tests of hypotheses for multivariate populations, principal components, factor analysis, discriminant analysis. Prerequisite: STA graduate standing or permission of instructor.. Back to top. ...
90448 avhandlingar från svenska högskolor och universitet. Avhandling: Modeling adsorption of organic compounds on activated carbon a multivariate approach.
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Résumé: The present and future of large scale studies of human brain and behavior-in typical and disease populations-is mutli-omics and deep-phenotyping. These studies rely on highly interdisciplinary teams that collect extremely diverse types of data across numerous systems and scales of measurement (e.g., genetics, brain structure, and behavior). Such large, complex, and heterogeneous data requires relatively simple methods that allow for flexibility in analyses without the loss of the inherent properties of various data types. Here we introduce a method specifically designed for these problems: partial least squares-correspondence analysis-regression (PLS-CA-R). PLS-CA-R generalizes PLS regression for use with virtually any data type (e.g., continuous, ordinal, categorical, non-negative values), and more broadly generalizes many of the routine two-table multivariate techniques such as various PLS approaches, canonical correlation analysis, and redundancy analysis (a.k.a. reduced rank ...
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Goes further than most similar textbooks by considering SIR techniques that are not found typically in multivariate textbooks Data sets discussed in the
In CONN both approaches are implemented (for seed-to-voxel or voxel-to-voxel analyses). You can see the differences in the corresponding design matrices and contrasts by clicking the design button in the GUI and then switching in the new window in the bottom dropdown menu between univariate model (SPM) and multivariate model. In the results explorer window, CONN will use the multivariate model approach for non-parametric analyses, and the univariate model (SPM) approach for parametric analyses. Typically, if you have a single dependent variable (or even with multiple dependent variable if you are using a vector between-conditions and between-sources contrasts) then both approaches are actually identical and produce exactly the same statistics, but when you have multiple dependents (e.g. a between-conditions contrast matrix instead of a vector) then the two models will produce (slightly) different results (mostly due to the difference in the assumption regarding spatial homogeneity of ...
The Multivariate Analysis Of Variance (MANOVA) is an ANOVA with two or more continuous outcome (or response) variables.. The one-way MANOVA tests simultaneously statistical differences for multiple response variables by one grouping variables.. For example, we may conduct an experiment where we give two treatments (A and B) to two groups of mice, and we are interested in the weight and height of mice. In that case, the weight and height of mice are our outcome (or dependent) variables, and our hypothesis is that both together are affected by the difference in treatment. A multivariate analysis of variance could be used to test this hypothesis.. The procedure of MANOVA can be summarized as follow:. ...
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself , and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models.. ...
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself , and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models.. ...
1. Podemos construir un biplot de regresiones también a partir de transformaciones no lineales de las variables respuesta: en consecuencia las marcas de los ejes del biplot no estarán a intervalos iguales, debido a que la calibración no es lineal. 2. Los modelos lineales generalizados permiten incluir distintos tipos de relaciones entre las medias condicionales de las variables respuesta y las variables explicativas, así como considerar diferentes distribuciones de la variable respuesta. En cada modelo lineal generalizado, obtenemos las medias condicionales que transformamos mediante funciones de enlace, como funciones lineales de las variables explicativas. 3. Son ejemplos de modelos lineales generalizados la regresión de Poisson (para datos de contaje), en las que la función de enlace es la función logaritmo y suponemos una distribución de Poisson para la variable respuesta, y la regression logística (para respuestas discretas), donde la función de enlace es la función logit, y ...
Question 1 Regression model results can be erroneous if multicollinearity is an issue. What causes multicollinearity? a) A test of homogeneity of variance-covariance matrices is significant. b) The DV is highly.
Some U.S. cities are excellent for cycling, like Portland, and some cities are not so good. This observation raises the question: what are the characterist
MANOVA is a generalized form of univariate analysis of variance (ANOVA),[1] although, unlike univariate ANOVA, it uses the variance-covariance between variables in testing the statistical significance of the mean differences.. Where sums of squares appear in univariate analysis of variance, in multivariate analysis of variance certain positive-definite matrices appear. The diagonal entries are the same kinds of sums of squares that appear in univariate ANOVA. The off-diagonal entries are corresponding sums of products. Under normality assumptions about error distributions, the counterpart of the sum of squares due to error has a Wishart distribution.. MANOVA is based on the product of model variance matrix ...
Download Multivariate Analysis In The Pharmaceutical Industry Books in PDF, EPUB, and Kindle for free. Read Online full Multivariate Analysis In The Pharmaceuti
25. It can be difficult to decide what constitutes a locus and what constitutes a variant reading. Taking the start of the Lords prayer at Luke 11:2 as an example, one might feel that the words form a single sense unit and that this should be counted as a single locus. However, another might decide that the words form several logical units and that there should therefore be several different loci, as in the UBS4 apparatus. Whether variants are coded as one locus or several is one of the subjective elements of statistical analysis.. 26. A locus where more than one variant exists could, of course, be coded as a family of binary variables with an added constraint. It is important at this stage to include a further binary variable to indicate whether the manuscript is extant at this particular locus or whether there is a lacuna; where one of the readings is a known omission from the text, a binary variable should be set up to register the fact. The binary representation should be the same whether ...
The aim of the thesis is to study the possibility of using advanced multivariate statistical methods for statistical analysis of metabolomical data, including the use of appropriate software tools. The main part of the thesis is the application of the methodology of logratio analysis of compositional data ...
The National Physical Laboratory, in partnership with the University of Nottingham, has established NiCE-MSI, a National Centre of Excellence in Mass Spectrometry Imaging. The objectives of the Centre are to research the fundamentals of the principal techniques, develop the metrology for reliable measurement and standardisation and to support the uptake of the techniques in business and academia.. The Centre also has major capability in data analysis and interpretation using multivariate techniques. Strategic partners at the University of Oxford, University of Surrey, St Georges University London and LGC bring together internationally leading capability in Nano SIMS, MeV SIMS, medical biomics and traceable quantification, respectively.. Aims of the National Centre of Excellence in Mass Spectrometry Imaging:. ...
We review and compare statistical methods for the analysis of in vivo tumor growth experiments. The methods most commonly used are deficient in that they have either low power or misleading type I error rates. We propose a set of multivariate statistical modeling methods that correct these problems, illustrating their application with data from a study of the effect of α-difluoromethylornithine on growth of the BT-20 human breast tumor in nude mice. All the methods find significant differences between the α-difluoromethylornithine dose groups, but recommended sample sizes for a subsequent study are much smaller with the multivariate methods. We conclude that the multivariate methods are preferable and present guidelines for their use.. ...
Several standard multivariate methods are provided by Genstat directives. These include methods that analyse data in the form of units-by-variates, and methods that use a similarity or distance matrix.. The following directives carry out standard multivariate analyses:. ...
Analytical and Marine Chemistry. Various paginations. 24 cm. Numerous figures. A little soiling to front cover. Label inside front cover. Top front edge a little creased ...
Hi all,. I would like to covariate for age & gender in a multivariate analysis. I found scripts to covariate but in ordinal data and in univarite analysis.. It would be really helpful for me it any of you could show me an example of a script on how to covariate (by age and gender) in a MV analysis, because I now from my previous univariate analysis that gender could be causing some genetic sex differences in 4 of the 6 traits that Im using.. Thank you very much!!!. ...
In the context of the guarantee liability valuation, the sophisticated fund-of-funds structure, of some Canadian segregated fund products, often requires us to model multiple market indices simultaneously in order to benchmark the return of the underlying fund. In this thesis, we apply multivariate GARCH models with Gaussian and non-Gaussian noise to project the future investment scenarios of the fund. We further conduct a simulation study to investigate the difference, among the proposed multivariate models, in the valuation of the Guaranteed Minimum Maturity Benefit (GMMB) option. Based on the pre-data analysis, the proposed multivariate GARCH models are data driven. The goodness-of-fit for the models is evaluated through formal statistical tests from univariate and multivariate perspectives. The estimation and associated practical issues are discussed in details. The impact from the innovation distributions is addressed. More importantly, we demonstrate an actuarial approach to manage the ...
Time and place: PHD104 Multivariate analysis: Applied factor and regression analysis Apr. 5, 2021 9:00 AM-Apr. 9, 2021 4:00 PM, University of Stavanger ...
MultiVariate SPC seamlessly integrates NWA Quality Analyst and Infometrix Pirouette, unifying multivariate modelling and SPC charting and analysis. More.
MANOVA (Multivariate Analysis of Variance) is used to model a combination of dependent variables. Use MANOVA in Excel with the XLSTAT software.
manova read write with math science /discrim. * * * * * * A n a l y s i s o f V a r i a n c e -- design 1 * * * * * * EFFECT .. WITHIN CELLS Regression Multivariate Tests of Significance (S = 2, M = -1/2, N = 97 ) Test Name Value Approx. F Hypoth. DF Error DF Sig. of F Pillais .59783 41.99694 4.00 394.00 .000 Hotellings 1.48369 72.32964 4.00 390.00 .000 Wilks .40249 56.47060 4.00 392.00 .000 Roys .59728 Note.. F statistic for WILKS Lambda is exact. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - EFFECT .. WITHIN CELLS Regression (Cont.) Univariate F-tests with (2,197) D. F. Variable Sq. Mul. R Adj. R-sq. Hypoth. MS Error MS F READ .51356 .50862 5371.66966 51.65523 103.99081 WRITE .43565 .42992 3894.42594 51.21839 76.03569 Variable Sig. of F READ .000 WRITE .000 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Raw canonical coefficients for DEPENDENT variables Function No. Variable 1 READ .063 WRITE .049 - - - - - - - - - - - - - - - - - - - - - ...
Workshop on Data Analytics for Advanced Research using Multivariate Analysis organized by of Thapar Institute of Engineering and Technology. Visit http://www.thapar.edu/pages/event/ to read out the latest event and news.
CiteSeerX - Scientific documents that cite the following paper: Multivariate Normal Probabilities with Error Bound. Applied Statistics
Bernard Fichet, Domenico Piccolo, Rosanna Verde, Classification and Multivariate Analysis for Complex Data Structures 2011 | pages: 494 | ISBN: 3642133118 | PDF | 4,9 mb Bernard Fichet, Dome
Would you like to study ? All information about Applied Multivariate Analysis in Utrecht: admission and language requirements, prices and grants.
Note: Due to the sheer number of requests for bespoke quant strategies, research projects, and quant consulting services, we have instituted the following pricing for the non-exclusive licensing of our algorithms to institutions ...
16. EnQuireR - EnQuireR focuses on categorical variables and allows to automate the survey process. It includes univariate and multivariate data analyses and offers an easier view of the results by the automatic generation of a report and of a presentation via Sweave ...
18. EnQuireR - EnQuireR focuses on categorical variables and allows to automate the survey process. It includes univariate and multivariate data analyses and offers an easier view of the results by the automatic generation of a report and of a presentation via Sweave ...
Hamerton, I, Herman, H, Mudhar, AK, Chaplin, A and Shaw, SJ (2002) Multivariate analysis of spectra of cyanate ester/bismaleimide blends and correlations with properties ...
Multivariate analysis[edit]. Multivariate Granger causality analysis is usually performed by fitting a vector autoregressive ... dimensional multivariate time series. Granger causality is performed by fitting a VAR model with L. {\displaystyle L}. time ... Lütkepohl, Helmut (2005). New introduction to multiple time series analysis (3 ed.). Berlin: Springer. pp. 41-51. ISBN ... Previous Granger-causality methods could only operate on continuous-valued data so the analysis of neural spike train ...
Eric W. Weisstein, MathWorld Mardia, K. V.; Kent, J. T.; Bibby, J. M. (1979). Multivariate Analysis. Academic Press. ISBN 978-0 ... Johnson, R.A.; Wichern, D.W. (2002). Applied multivariate statistical analysis. 5. Prentice hall. ... The distribution arises in multivariate statistics in undertaking tests of the differences between the (multivariate) means of ... If the vector d {\displaystyle d} is Gaussian multivariate-distributed with zero mean and unit covariance matrix N ( 0 p , I p ...
In multivariate analysis of variance (MANOVA) the following equation applies T = W + B , {\displaystyle \mathbf {T} =\mathbf {W ... For a proof of this in the multivariate OLS case, see partitioning in the general OLS model. In analysis of variance (ANOVA) ... Multivariate Analysis. Academic Press. ISBN 0-12-471252-5. Especially chapters 11 and 12.. ... In statistical data analysis the total sum of squares (TSS or SST) is a quantity that appears as part of a standard way of ...
Bayesian multivariate linear regression K. V. Mardia, J. T. Kent and J. M. Bibby (1979). Multivariate Analysis. Academic Press ... and follow a multivariate normal distribution. If the errors do not follow a multivariate normal distribution, generalized ... In multivariate tests the columns of Y are tested together, whereas in univariate tests the columns of Y are tested ... In the more general multivariate linear regression, there is one equation of the above form for each of m > 1 dependent ...
McIntosh, Anthony R.; Mišić, Bratislav (2013-01-03). "Multivariate Statistical Analyses for Neuroimaging Data". Annual Review ... Multivariate Analysis. New York: Academic Press. pp. 391-420. Wold, Herman (1981). The fix-point approach to interdependent ... The general underlying model of multivariate PLS is X = T P T + E {\displaystyle X=TP^{\mathrm {T} }+E} Y = U Q T + F {\ ... Partial least squares discriminant analysis (PLS-DA) is a variant used when the Y is categorical. PLS is used to find the ...
Multivariate Analysis. Academic Press. ISBN 0-12-471250-9. M. S. Bartlett (1954). "A Note on the Multiplying Factors for ... especially with regard to the likelihood-ratio test and multivariate analysis of variance (MANOVA). Wilks' lambda distribution ... In statistics, Wilks' lambda distribution (named for Samuel S. Wilks), is a probability distribution used in multivariate ... As such it can be regarded as a multivariate generalization of the beta distribution. It follows directly that for a one- ...
Mardia, K. V.; Kent, J. T.; Bibby, J. M. (1979). Multivariate analysis. New York: Academic Press. ISBN 0-12-471250-9.. ... ISBN 978-3-658-11455-8., chapter 3 Mandel, John (1964). The Statistical Analysis of Experimental Data. New York: Interscience. ...
Kanti V. Mardia, J. T. Kent and J. M. Bibby (1979). Multivariate Analysis. Academic Press. ISBN 978-0-12-471250-8. Shahrokh ... The following is based on Press, S. J. (1982) "Applied Multivariate Analysis", 2nd ed. (Dover Publications, New York), after ... Journal of Multivariate Analysis. 10: 51-59. Brennan, L E; Reed, I S (January 1982). "An Adaptive Array Signal Processing ... Journal of Multivariate Analysis. 9 (4): 531-544. doi:10.1016/0047-259x(79)90056-3. Gelman, Andrew; Carlin, John B.; Stern, Hal ...
May, Jude (2018). Multivariate Analysis. London: ETP. ISBN 978-1-78882-072-1. OCLC 1045590874. Kahn, David (1996). The ... May, Jude (2018). Multivariate Analysis. London: ETP. ISBN 978-1-78882-072-1. OCLC 1045590874. Stallings, William (2010). ... In practice, frequency analysis relies as much on linguistic knowledge as it does on statistics, but as ciphers became more ... Frequency analysis relies on a cipher failing to hide these statistics. For example, in a simple substitution cipher (where ...
In contrast to classical multivariate analysis, generalized multivariate analysis refers to research on elliptical ... In statistics, the multivariate normal distribution (of Gauss) is used in classical multivariate analysis, in which most ... Multivariate normal distribution Multivariate t-distribution Symmetric multivariate stable distribution Symmetric multivariate ... In statistics, the normal distribution is used in classical multivariate analysis, while elliptical distributions are used in ...
A multivariate analysis". Cancer. 66 (4): 722-32. doi:10.1002/1097-0142(19900815)66:4. 3.0.co;2-j. PMID 2167141. Korkmaz T, ... a multivariate analysis". Scand J Haematol. 33 (1): 59-67. doi:10.1111/j.1600-0609.1984.tb02211.x. PMID 6379852. Hallek M, ... a multivariate analysis". British Journal of Haematology. 69 (1): 47-53. doi:10.1111/j.1365-2141.1988.tb07601.x. PMID 3289607. ... Leukemias normally do not normally present major diagnostic difficulties, as the microscopic analysis of the cells in blood ...
ISBN 0-471-61760-1. Timm, Neil H. (2002). ""The general MANOVA model (GMANOVA)" (Chapter 3.6.d)". Applied multivariate analysis ... The rANOVA also requires that certain multivariate assumptions be met, because a multivariate test is conducted on difference ... study Growth curve Missing data Mixed models Multivariate analysis Observational study Optimal design Panel analysis Panel data ... It is also common to use the multivariate η2 when the assumption of sphericity has been violated, and the multivariate test ...
Exploratory multivariate analysis of text corpora Since completion of the NECTE project he has been developing a methodology ... Exploratory Multivariate Analysis. In: Lüdeling A, Kytö M, ed. Corpus Linguistics. An International Handbook (Series: ... My specific areas of interest are natural language understanding systems and multivariate analysis of text corpora. BA (McGill ... Moisl HL, Maguire W, Allen W. Phonetic variation in Tyneside : Exploratory multivariate analysis of the Newcastle Electronic ...
Applied Multivariate Statistical Analysis. Pearson. pp. 111-112. ISBN 0131877151. "Basic Concepts for Multivariate Statistics p ... ISBN 0-471-57428-7. (Section 8.1.1) Timm, Neil H. (2007). Applied Multivariate Analysis. Springer Science & Business Media. p. ... ISBN 0-521-81099-X. Box, G. E. P.; Tiao, G. C. (1992) [1973]. Bayesian Inference in Statistical Analysis. New York: John Wiley ... In statistics and in particular in regression analysis, a design matrix, also known as model matrix or regressor matrix and ...
ISBN 978-1-4020-3418-3. R.F. Potthoff and S.N. Roy, "A generalized multivariate analysis of variance model useful especially ... Timm, Neil H. (2002). ""The general MANOVA model (GMANOVA)" (Chapter 3.6.d)". Applied multivariate analysis. Springer Texts in ... The growth curve model in statistics is a specific multivariate linear model, also known as GMANOVA (Generalized Multivariate ... GMANOVA is frequently used for the analysis of surveys, clinical trials, and agricultural data, as well as more recently in the ...
Kharit, Mikhail; Kremer, V. (1983). "Multivariate regression analysis". Informational Bulletin of the State Fund of Algorithms ... This research allowed him to develop a new algorithm and to create a program that uses the equations of the regression analysis ... He developed a new algorithm and created a program that uses the equations of the for the analysis of the collected statistical ... and the Fourier series for the analysis of the collected statistical data. ...
Tabachnick, B.G.; Fidell, L.S. (2001). Using Multivariate Analysis. Boston: Allyn and Bacon. ISBN 978-0-321-05677-1.[page ... Models include: linear mixed-effects models and nonlinear mixed-effects models Hidden Markov models Factor analysis Analysis ... component analysis Partial least squares regression Latent semantic analysis and probabilistic latent semantic analysis EM ... The "Big Five personality traits" have been inferred using factor analysis. extraversion spatial ability wisdom "Two of the ...
... particularly for the statistical analysis of multivariate distributions, especially the multivariate normal distribution and ... ISBN 978-0-471-75156-4. Magnus, Jan R. (October 2010). "On the concept of matrix derivative". Journal of Multivariate Analysis ... Generalized multivariate analysis. Science Press (Beijing) and Springer-Verlag (Berlin). ISBN 3540176519. 9783540176510. Kollo ... It collects the various partial derivatives of a single function with respect to many variables, and/or of a multivariate ...
The thesis described an alternative organization of multivariate data analysis techniques, which formed the basis for the Gifi ... De Leeuw is the originator of the Albert Gifi team that wrote Nonlinear Multivariate Analysis. In Multidimensional Scaling, ... Nonlinear Multivariate Analysis. Multidimensional Scaling, Volume 1. Introducing Multilevel Modeling Handbook of Multilevel ... as well as the former editor-in-chief of the Journal of Multivariate Analysis and the Journal of Educational and Behavioral ...
Multivariate genetic analysis". Behavioural Brain Research. 32 (1): 81-88. doi:10.1016/S0166-4328(89)80075-0. PMID 2930637. ... Some significant papers are: Crusio, W. E.; Genthner-Grimm, G.; Schwegler, H. (2007). "A quantitative-genetic analysis of ... Original publication: Crusio, W. E.; Genthner-Grimm, G.; Schwegler, H. (July 1986). "A quantitative-genetic analysis of ... A quantitative-genetic analysis. Meppel: Krips Repro. pp. viii+146+78. "Curriculum Vitae Wim E. Crusio". Archived from the ...
Journal of Multivariate Analysis. 115: 172-180. doi:10.1016/j.jmva.2012.10.008. Charness, G.; Gneezy, U.; Kuhn, M. A. (2012). " ... The design and analysis of clinical experiments, 1-32. West, S. G.; Thoemmes, F. (2010). "Campbell's and Rubin's perspectives ... In Beyond significance testing: Reforming data analysis methods in behavioral research (pp. 203-243). Washington, DC US: ... In Beyond significance testing: Reforming data analysis methods in behavioral research (pp. 163-202). Washington, DC US: ...
Goodman, N R (1963). "Statistical Analysis Based on a Certain Multivariate Complex Gaussian Distribution: an Introduction". Ann ... Journal of Multivariate Analysis. 10: 51-59. Maiwald, Dirk; Kraus, Dieter (1997). "On Moments of Complex Wishart and Complex ... is the complex multivariate Gamma function C Γ p ( ν ) = π 1 2 p ( p − 1 ) ∏ j = 1 p Γ ( ν − j + 1 ) {\displaystyle {\mathcal { ...
Journal of Multivariate Analysis. 79 (1): 99-113. doi:10.1006/jmva.2000.1960. D. K. Dey and C. Srinivasan (1985). "Estimation ... During his career at the University of Connecticut, Dey has worked on a large number of problems on Bayesian analysis and ... doi:10.1111/j.2517-6161.1994.tb01996.x. M. D. Branco, D. K. Dey (2001). "A general class of multivariate skew-elliptical ... D. Sinha and D. K. Dey (1997). "Semiparametric Bayesian analysis of survival data". Journal of the American Statistical ...
Meila, M. (2007). "Comparing clusterings-an information based distance". Journal of Multivariate Analysis. 98 (5): 873-895. doi ...
MEILA, M (1 May 2007). "Comparing clusterings-an information based distance". Journal of Multivariate Analysis. 98 (5): 873-895 ...
Methods of Multivariate Analysis. John Wiley & Sons. p. 155. ISBN 978-1-118-39167-9. Gere, James M. (2013). Mechanics of ... In 1822, Gauss was able to state that the least-squares approach to regression analysis is optimal in the sense that in a ... The idea of least-squares analysis was also independently formulated by the American Robert Adrain in 1808. In the next two ... The researcher specifies an empirical model in regression analysis. A very common model is the straight-line model, which is ...
Applied Multivariate Statistical Analysis. Prentice Hall. p. 76. ISBN 0-13-187715-1. Goodman, Leo A. (December 1960). "On the ... Variance is an important tool in the sciences, where statistical analysis of data is common. The variance is the square of the ...
Journal of Multivariate Analysis. 100 (5): 817-820. doi:10.1016/j.jmva.2008.07.006. Kac, M. (1939). "On a characterization of ... The results for the special case of the Multivariate normal distribution is originally attributed to Maxwell. This is a family ... Hosking, J.R.M., Wallis, J.R. (1997) Regional frequency analysis: an approach based on L-moments, Cambridge University Press. ... Box, George E. P.; Tiao, George C. (1992). Bayesian Inference in Statistical Analysis. New York: Wiley. ISBN 978-0-471-57428-6 ...
Applied multivariate statistical analysis. 5. Prentice hall. Theil, Henri (1971). "Best Linear Unbiased Estimation and ... The term "spherical errors" will describe the multivariate normal distribution: if Var ⁡ [ ε ∣ X ] = σ 2 I {\displaystyle \ ... ISBN 0-12-576830-3. Davidson, James (2000). "Statistical Analysis of the Regression Model". Econometric Theory. Oxford: ... operatorname {Var} [\,{\boldsymbol {\varepsilon }}\mid \mathbf {X} ]=\sigma ^{2}\mathbf {I} } in the multivariate normal ...
Applications of Multivariate Analysis. PN 1992. Signal Estimation, Multitarget Tracking and Related Areas. 1989. Multivariate ... Other areas he worked in include multivariate analysis, estimation theory, and differential geometry. His other contributions ... Estimation theory Statistical inference and linear models Multivariate analysis Combinatorial design Orthogonal arrays Biometry ... Book Review: Multivariate Statistical Methods, A Primer 2016. (with Lovric, M.) Testing Point Null Hypothesis of a Normal Mean ...
Cluster analysis. *Classification. *Structural equation model *Factor analysis. *Multivariate distributions *Elliptical ... This ensures that subsequent user errors cannot inadvertently perform meaningless analyses (for example correlation analysis ... Mosteller, Frederick (1977). Data analysis and regression : a second course in statistics. Reading, Mass: Addison-Wesley Pub. ... Cliff, N. (1996). Ordinal Methods for Behavioral Data Analysis. Mahwah, NJ: Lawrence Erlbaum. ISBN 0-8058-1333-0 ...
Cluster analysis. *Classification. *Structural equation model *Factor analysis. *Multivariate distributions *Elliptical ... Kruskal-Wallis one-way analysis of variance. Notes[edit]. *^ a b Mann, Henry B.; Whitney, Donald R. (1947). "On a Test of ... Oja, Hannu (2010). Multivariate nonparametric methods with R: An approach based on spatial signs and ranks. Lecture Notes in ... Zar, Jerrold H. (1998). Biostatistical Analysis. New Jersey: Prentice Hall International, INC. p. 147. ISBN 0-13-082390-2.. ...
The usual approach for statistical analysis is mass-univariate (analysis of each voxel separately), but pattern recognition may ... "Multivariate voxel-based morphometry successfully differentiates schizophrenia patients from healthy controls". NeuroImage. 34 ... Tutorial: A Critical Analysis of Voxel Based Morphometry (VBM). *Voxel-Based Morphometry Should Not Be Used with Imperfectly ... Actual statistical analysis by the general linear model, i.e., statistical parametric mapping. ...
A uni-multivariate statistical analysis in 76 surgically treated adult patients". Surgical Neurology. 44 (3): 208-21, ... a systematic analysis for the Global Burden of Disease Study 2015". Lancet. 388 (10053): 1545-1602. doi:10.1016/S0140-6736(16) ... a systematic analysis for the Global Burden of Disease Study 2015". Lancet. 388 (10053): 1459-1544. doi:10.1016/S0140-6736(16) ... and follow-up examination of prepared tissues after immunohistochemical staining or genetic analysis. ...
Maunder M.N. (2003) Paradigm shifts in fisheries stock assessment: from integrated analysis to Bayesian analysis and back again ... multivariate optimization is often used, through the simplex algorithm or its derivatives. ... Maunder, M. N. (2004). "Population viability analysis, based on combining integrated, Bayesian, and hierarchical analyses". ... "Analysis of capture-recapture data". Retrieved 19 Nov 2014.. "Chapman and Hall/CRC Press". Retrieved 19 Nov 2014.. ...
Gruneberg, H., 1938 An analysis of the "pleiotropic" effects of a new lethal mutation in the rat (Mus norvegicus). Proc. R. Soc ... Pleiotropic gene action can limit the rate of multivariate evolution when natural selection, sexual selection or artificial ... Berger, David; Berg, Elena C.; Widegren, William; Arnqvist, Göran; Maklakov, Alexei A. (2014-12-01). "Multivariate intralocus ... evidence from a meta-analysis of twin studies". Archives of General Psychiatry. 60 (12): 1187-1192. doi:10.1001/archpsyc.60.12. ...
A meta-analysis by the Cochrane Collaboration concluded that low glycemic index or low glycemic load diets led to more weight ... with multivariate adjustment) between animal fat intake and coronary heart disease (table 4). A long term study that monitored ... A meta-analysis of randomized controlled trials by the international Cochrane Collaboration in 2002 concluded[31] that fat- ... A more recent meta-analysis that included randomized controlled trials published after the Cochrane review[32][33][34] found ...
Using functional magnetic resonance imaging with specialized multivariate analyses to study the temporal dimension in the ... The first part of their analysis (which the only relevant part to this section) was not meant to discover the types of free ... Without entering into metaphysical speculations, I may perhaps add that an analysis of the very concept of explanation would, ... More sophisticated analyses of compatibilist free will have been offered, as have other critiques.[110] ...
變異數分析或變方分析(Analysis of variance,簡稱ANOVA)為資料分析中常見的統計模型,主要為探討連續型(Continuous)資料型態之因变量(Dependent variable)與類別型資料型態之自变量(Independent ... Tabachnick, Barbara G. & Fidell, Linda S. (2007). Using Multivariate Statistics (5th ed.). Boston: Pearson International ... 在變異數分析的基本運算概念下,依照所感興趣的因子數量而可分
This is normally carried out using one of several data analysis and image classification algorithms, such as multi-variate ... Single particle analysis can be done on both negatively stained and vitreous ice-embedded cryo-EM samples. Single particle ... Single particle analysis is a group of related computerized image processing techniques used to analyze images from ... Filtered back projection is a commonly used method of generating 3D reconstructions in single particle analysis, although many ...
are orthonormal, rather than being redundant, then MP is a form of principal component analysis ... Multivariate statistics. *Signal processing. Hidden categories: *All articles with dead external links ... Applied and Computational Harmonic Analysis. 26 (3): 301-321. arXiv:0803.2392. doi:10.1016/j.acha.2008.07.002.. ... The main disadvantage of Fourier analysis in signal processing is that it extracts only the global features of the signals and ...
Cluster analysis. *Classification. *Structural equation model *Factor analysis. *Multivariate distributions *Elliptical ... In terms of numerical analysis, isotonic regression involves finding a weighted least-squares fit x. ∈. R. n. {\displaystyle x\ ...
"Journal of the Experimental Analysis of Behavior. 89 (2): 145-155. doi:10.1901/jeab.2008-89-145. ISSN 0022-5002. PMC 2251327. ... as well as multivariate source-based morphometry[40] (SBM) to investigate a structural network of loss aversion and univariate ... 2008). "On loss aversion in capuchin monkeys". Journal of the Experimental Analysis of Behavior. 89 (2): 145-155. doi:10.1901/ ... Kahneman, D.; Tversky, A. (1979). "Prospect Theory: An Analysis of Decision under Risk". Econometrica. 47 (2): 263-291. ...
It may be written as Y=XB +U where Y is a matrix with series of multivariate measurements, X is a matrix that might be a design ... Cambridge Brain Analysis a software repository developed at University of Cambridge for functional magnetic resonance imaging ( ... It is frequently used in the analysis of multiple brain scans in scientific experiments where Y contains data from brain ... Analysis of Functional NeuroImages an open-source environment for processing and displaying functional MRI data ...
We consider mainly the case of the multivariate rational fractions. This is not a real restriction, because, as soon as the ... Analysis. *Calculus. *Real analysis. *Complex analysis. *Differential equations. *Functional analysis. *Harmonic analysis ...
Cluster analysis. *Classification. *Structural equation model *Factor analysis. *Multivariate distributions *Elliptical ... multivariate Laplace. multivariate normal. multivariate stable. multivariate t. normal-inverse-gamma. normal-gamma. Matrix- ... Multivariate hypergeometric distribution, similar to the multinomial distribution, but using sampling without replacement; a ... For generalized functions in mathematical analysis, see Distribution (mathematics). For continuous variation in biology, see ...
In most behavioral research endeavors, persons or patients are units of analysis, whereas in GT the unit of analysis is the ... "GT is multivariate. It happens sequentially, subsequently, simultaneously, serendipitously, and scheduled" (Glaser, 1998). ... Anselm L. Strauss: Qualitative Analysis for Social Scientists (1987). *Anselm L. Strauss; Juliet Corbin: Basics of Qualitative ... Charmaz, K. (2000). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Thousand Oaks, CA: Sage ...
History of hypertension, although not associated in the univariate analysis, became significant in the multivariate model ... "Cardiac papillary fibroelastoma: a comprehensive analysis of 725 cases". Division of Cardiology, Long Island College Hospital; ... Koziol JA, Feng AC; Feng (October 2006). "On the analysis and interpretation of outcome measures in stroke clinical trials: ...
More recently, multivariate methods have been proposed that derive ICP by combining the transit times with measured acoustic ... Michaeli D, Rappaport ZH (June 2002). "Tissue resonance analysis; a novel method for noninvasive monitoring of intracranial ... tissue resonance analysis) have 2 options: 1) Qualitative method makes evaluation of mild (10-20mm.Hg), moderate (20-40) and ...
Wingard DL, Berkman LF, Brand RJ (1982). "A multivariate analysis of health-related practices: a nine-year mortality follow-up ... It is concerned with threats to the overall health of a community based on population health analysis. The population in ... "Volunteering is Associated with Delayed Mortality in Older People: Analysis of the Longitudinal Study of Aging" (PDF). Journal ...
Cluster analysis. *Classification. *Structural equation model *Factor analysis. *Multivariate distributions *Elliptical ... James G. Webster, Patricia F. Phalen, Lawrence W. Lichty; Ratings Analysis: The Theory and Practice of Audience Research ... Saris, W. E. and Gallhofer, I. N. (2014). Design, evaluation and analysis of questionnaires for survey research. Second Edition ...
Cluster analysis. *Classification. *Structural equation model *Factor analysis. *Multivariate distributions *Elliptical ... In the 19th century, statistical analytical methods were mainly applied in biological data analysis and it was customary for ... The portmanteau test in time-series analysis, testing for the presence of autocorrelation ... and proposed a method of statistical analysis consisting of using the Pearson distribution to model the observation and ...
Cluster analysis. *Classification. *Structural equation model *Factor analysis. *Multivariate distributions *Elliptical ... Hinkelmann, Klaus and Kempthorne, Oscar (2005). Design and Analysis of Experiments, Volume 2: Advanced Assignment Experimental ... Caliński, Tadeusz & Kageyama, Sanpei (2000). Block designs: A Randomization approach, Volume I: Analysis. Lecture Notes in ... Hinkelmann, Klaus and Kempthorne, Oscar (2008). Design and Analysis of Experiments. I and II (Second ed.). Wiley. ISBN 978-0- ...
... differences among Chinese soy sauce types studied by 13C NMR spectroscopy coupled with multivariate statistical analysis". ... Based on the result of free amino acid analysis, the most abundant amino acids in Chinese soy sauce product are glutamic acid, ... Zhang, Yanfang; Tao, Wenyi (18 February 2009). "Flavor and taste compounds analysis in Chinese solid fermented soy sauce". ...
Cluster analysis. *Classification. *Structural equation model *Factor analysis. *Multivariate distributions *Elliptical ... For a one-sample multivariate test, the hypothesis is that the mean vector (μ) is equal to a given vector (μ0). The test ... Rice, John A. (2006). Mathematical Statistics and Data Analysis (3rd ed.). Duxbury Advanced.. [ISBN missing] ... For a two-sample multivariate test, the hypothesis is that the mean vectors (μ1, μ2) of two samples are equal. The test ...
"The Long‐Run Impact of Foreign Aid in 36 African Countries: Insights from Multivariate Time Series Analysis*." Oxford Bulletin ... Transition: The First Ten Years - Analysis and Lessons for Eastern Europe and the Former Soviet Union, The World Bank, ... Ali, Abdiweli M.; Said Isse, Hodan (2007). "Foreign Aid and Free Trade and their Effect on Income: A Panel Analysis". The ... Collier, Paul (2005). Is Aid Oil? An analysis of whether Africa can absorb more aid. Centre for the study of African Economies ...
Wertheim, Edward G.; Widom, Cathy S.; Wortzel, Lawrence H. (1978). "Multivariate analysis of male and female professional ... A factor analysis found that among the big five personality traits, low agreeableness is the strongest correlate of the dark ... In a meta-analysis of dark triad and workplace outcomes, Jonason and colleagues (2012) found that each of the dark triad traits ... Indeed, based on analyses of responses to the Narcissistic Personality Inventory collected from over 16,000 U.S. undergraduate ...
One analysis of the monotreme Teinolophos suggested that this animal had a pre-mammalian jaw joint formed by the angular and ... Meng Chen, Gregory Philip Wilson, A multivariate approach to infer locomotor modes in Mesozoic mammals, Article in Paleobiology ... Recent analysis of Teinolophos, which lived somewhere between 121 and 112.5 million years ago, suggests that it was a "crown ... Since this group has living members, DNA analysis can be applied in an attempt to explain the evolution of features that do not ...
... a uni-multivariate statistical analysis in 76 surgically treated adult patients". Surg Neurol. 44 (3): 208-21; discussion 221-3 ... "Analysis of prognostic and survival factors related to treatment of low-grade astrocytomas in adults". Oncology. 58 (2): 108- ...
Cluster analysis. *Classification. *Structural equation model *Factor analysis. *Multivariate distributions *Elliptical ... SEM includes confirmatory factor analysis, path analysis, partial least squares path modeling, and latent growth modeling.[2] ... such as applied in the so-called PLS path analysis packages which estimate with OLS; (2) covariance analysis algorithms ... Loehlin, J. C. (2004). Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis. Psychology ...
... Due to the COVID-19 crisis, the information below is subject to change, in particular that ... Part 3: Multivariate analysis models. In this part, students see how to model inter-variable relations: linear models ( ... Part 2: Techniques of multivariate data analysis. In this part, students learn about basic dimension reduction techniques for ... Härdle, W. and L. Simar (2007): Applied Multivariate Statistical Analysis, 2nd Edition, Springer-Verlag, Berlin.. ...
Op basis van multivariate data analyses zijn bijvoorbeeld instelparameters van een productieproces te relateren aan ... gebruikt worden voor de analyse van multivariate data, gerelateerd aan kwaliteitsaspecten van producten en processen.. De ...
It is written for the user and potential user of multivariate techniques as well as for students coming to the subject for the ... This book is an introduction to the principles and methodology of modern multivariate statistical analysis. ... Analysis.html?id=12k-beAwkQcC&utm_source=gb-gplus-sharePrinciples of Multivariate Analysis. ... Principles of multivariate analysis: a users perspective. W. J. Krzanowski. Snippet view - 1988. ...
Compare LEAF MARGIN ANALYSIS. Source for information on Climate-Leaf Analysis Multivariate Program: A Dictionary of Earth ... Climate-Leaf Analysis Multivariate Program (CLIMAP) An approach to the estimation of mean annual temperatures in the past based ... Climate-Leaf Analysis Multivariate Program (CLIMAP) An approach to the estimation of mean annual temperatures in the past based ... Climate-Leaf Analysis Multivariate Program A Dictionary of Earth Sciences © A Dictionary of Earth Sciences 1999, originally ...
... multivariate regression, principal components and factor analysis. In addition discriminant analysis, cluster analysis and ... The course contains descriptive multivariate statistics, multivariate distribution theory such as the multinormal and the ... The course gives an introduction to multivariate statistics with the emphasis on practical applications. The students get ... Furthermore, it contains multivariate t-tests, simultaneous confidence intervals, ...
Purchase Mathematical Tools for Applied Multivariate Analysis - 1st Edition. Print Book & E-Book. ISBN 9780122975509, ... 6.4 The Factor Analysis Problem. 6.5 The Multiple Discriminant Analysis Problem. 6.6 A Parting Look at Multivariate Technique ... Chapter 1 The Nature of Multivariate Data Analysis. 1.1 Introduction. 1.2 Multivariate Methods in Research. 1.3 A ... A.5 Application of the Calculus to Multivariate Analysis. A.6 Summary. Review Questions. Appendix B Linear Equations and ...
... this textbook on tools and concepts of multivariate data analysis features numerous examples and exercises, including high- ... Presents multivariate statistical analysis in a comprehensive way, including the most useful approaches to multi-dimensional ... This textbook presents the tools and concepts used in multivariate data analysis in a style accessible for non-mathematicians ... All chapters include practical exercises that highlight applications in different multivariate data analysis fields, and all ...
Presents both theory and practical implementation of multivariate statistical analysis in LISREL Illustrates all methods and ... multivariate, censored, logistic, and probit), generalized linear models, multilevel analysis, and principal component analysis ... Multivariate Analysis with LISREL. Authors: Jöreskog, Karl G., Olsson, Ulf H., Y. Wallentin, Fan ... This book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice ...
... of determining the properties of a sample from measured spectral data collected from the sample by performing a multivariate ... spectral analysis. The method can include: generating a two-dimens ... Detailed discussion of a parallel multivariate spectral analysis program can be found in "Multivariate Image Analysis Tools for ... illustrates a multivariate spectral analysis system 7. for determining the properties of a sample from measured spectral data, ...
Predictors of Job Satisfaction and Intent to Leave among Home Health Workers: An Analysis of the National Home Health Aide ... This analysis tests two models: model I excludes variables based on worker perceptions that were suspected to be endogenous ...
International students are recommended to arrive at least a few days before the start of the regular term, preferably prior to the Orientation. If you fail to arrive on time, you have to contact the study counselor as soon as possible, but not later than one week ahead of the course start, or you may not be able to register for the programme.. If you will have housing through the International Housing Office you will receive information about your arrival in your offer of housing.. Dont forget - Umeå has cold winters, so pack accordingly with a warm jacket, warm shoes, gloves and hat.. Close ...
Most of the observable phenomena in the empirical sciences are of a multivariate nature. In financial studies, assets are ... Cluster Analysis Conjoint Measurement Analysis Discriminant Analysis Hypothesis Testing Multyvariate Analysis Projection ... Focusing on applications this book presents the tools and concepts of multivariate data analysis in a way that is ... The book surveys the basic principles of multivariate statistical data analysis and emphasizes both exploratory and inferential ...
We consider multivariate regression analysis with missing data in the outcome variables, when the nonresponse mechanism depends ... "Analysis of multivariate missing data with nonignorable nonresponse," Biometrika, Biometrika Trust, vol. 90(4), pages 747-764, ... "Theory and inference for regression models with missing responses and covariates," Journal of Multivariate Analysis, Elsevier, ... We consider multivariate regression analysis with missing data in the outcome variables, when the nonresponse mechanism depends ...
Unlike the traditional impulse response analysis, this approach does not require orthogonalisation of shocks and is invariant ... impulse response analysis for unrestricted vector autoregressive (VAR) and cointegrated VAR models. ... "Generalised Impulse Response Analysis in Linear Multivariate Models," Cambridge Working Papers in Economics 9710, Faculty of ... "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, ...
... it introduces the powerful concept of parameter sensitivity analysis within a linear systems root/vector framework. The ... combining statistical theory with applied data analysis. Considers important methodological principles underlying statistical ... Multivariate analysis: methods and applications. Wiley series in probability and mathematical statistics: Applied probability ... 2 KSI LAMBDA latent class model maximum likelihood mean measures method multiple multiple discriminant analysis multivariate ...
This volume contains the Proceedings of the Advanced Symposium on Multivariate Modeling and Data Analysis held at the 64th ... Multivariate Statistical Modeling and Data Analysis. Proceedings of the Advanced Symposium on Multivariate Modeling and Data ... Clustering Factor analysis Likelihood cluster analysis correlation data analysis multidimensional scaling principal component ... The areas covered included topics in the analysis of repeated measurements, cluster analysis, discriminant analysis, canonical ...
Introduction to Multivariate Analysis. To view this video please enable JavaScript, and consider upgrading to a web browser ... Covering the tools and techniques of both multivariate and geographical analysis, this course provides hands-on experience ... And really theres two main ways of presenting multivariate datasets, directly through a table. And so again, we can think ... In this lecture, we want to talk about attributes of a multivariate data visualization. In previous modules, weve discussed ...
Subsequent sections describe bivarate and multiple linear regression analyses, one-way and two-way analysis of variance (ANOVA ... analysis of variance and covariance, and path analysis-with an emphasis on the basic statistics as important features of these ... In this pocket guide, the authors introduce readers to three of the more frequently used multivariate statistical methods in ... Having a thorough understanding of basic statistics can facilitate this process as multivariate methods have as their ...
Rencher, A. C. and Christensen, W. F. (2012) Matrix Algebra, in Methods of Multivariate Analysis, Third Edition, John Wiley & ...
Multivariate analysis of cognitive tests in Alzheimers disease identifies five distinct groups of Alzheimers disease patients ... Multivariate analysis improves on cognitive testing in Alzheimers disease Multivariate analysis of cognitive tests in ... Multivariate analysis improves on cognitive testing in Alzheimers disease. Future Science Group ... multivariate-analysis-improves-on-cognitive-testing-in-alzheimers-disease/. Related Journal Article. http://dx.. doi.. org/. 10 ...
Selection from Statistical Analysis in Forensic Science: Evidential Values of Multivariate Physicochemical Data [Book] ... Statistical Analysis in Forensic Science: Evidential Values of Multivariate Physicochemical Data. by Agnieszka Martyna, ... Statistical Analysis in Forensic Science will provide an invaluable practical guide for forensic experts and practitioners, ... Detailed description of likelihood ratio models for determining the evidential value of multivariate physicochemical data. ...
SPICE: exploration and analysis of post-cytometric complex multivariate datasets.. Roederer M1, Nozzi JL, Nason MC. ... Polychromatic flow cytometry results in complex, multivariate datasets. To date, tools for the aggregate analysis of these ... While originally developed to support the analysis of T cell functional profiles, these techniques are amenable to a broad ... for further analysis or display of this distribution, those values can be set to zero. This does not introduce a systematic ...
Statistical Analysis in Forensic Science will provide an invaluable practical guide for forensic experts and practitioners, ... Key features include: Description of the physicochemical analysis of forensic trace evidence. Detailed description of ... likelihood ratio models for determining the evidential value of multivariate physicochemical data. Detailed description of ... Statistical Analysis in Forensic Science: Evidential Value of Multivariate Physicochemical Data. Grzegorz Zadora, Agnieszka ...
View our complete catalog of authoritative Regression Analysis and Multivariate Statistics related book titles and textbooks ... Data Analysis. A Model Comparison Approach To Regression, ANOVA, and Beyond, Third Edition By Charles M. Judd. , Gary H. ... Event History Analysis With Stata. 2nd Edition By Hans-Peter Blossfeld. , Gotz Rohwer. , Thorsten Schneider. ... Regression Analysis and Multivariate Statistics Titles. Showing:. Published Titles × Regression Analysis and Multivariate ...
Multivariate Analysis 34. Unit aims. To present various aspects of multivariate analysis, covering data exploration, modeling ... Multivariate analysis is a branch of statistics involving the consideration of objects on each of which are observed the values ... A wide range of methods is used for the analysis of multivariate data, both unstructured and structured, and this course will ... Krzanowski, W. J. and Marriott, F. H. C. Multivariate Analysis, Parts I and II. Edward Arnold. 1994. ...
Multivariate Statistical Analysis, Second Edition addresses several added topics related to the properties and characterization ... of symmetric distributions, elliptically symmetric multivariate distributions, singular… ... Significantly revised and expanded, Multivariate Statistical Analysis, Second Edition addresses several added topics related to ... Its discussions and examples draw on a wide range of multivariate data, from biometry, agriculture, biomedical science, ...
Home » Quality Software & Analysis: Multivariate Quality Control. Quality Software & Analysis: Multivariate Quality Control. ... Quality Software & Analysis: Control the Data, Control the Costs. Quality Software & Analysis: Keep Manufacturing in Control. ... Quality Software & Analysis: The Power of Document Control Software & Analysis: The Gate to 3-D Quality Control. ... It tracks the distance of mean vectors, or samples, from an ideal point in this multivariate space, a multivariate centerline ...
Cox regression analysis of multivariate failure time data: the marginal approach.. Lin DY1. ... Multivariate failure time data are commonly encountered in scientific investigations because each study subject may experience ... This approach formulates the marginal distributions of multivariate failure times with the familiar Cox proportional hazards ... which is analogous to that of Liang and Zeger for longitudinal data analysis. ...
Chapter 14Multivariate Statistical Analysis - I Package(s): ICSNP, scatterplot3d, aplpack, mvtnorm, foreign Dataset(s): cardata ... Chapter 14Multivariate Statistical Analysis - I. Package(s): ICSNP. , scatterplot3d. , aplpack. , mvtnorm. , foreign. ... Thus, we need a set of tools to handle this type of data, and this is provided by Multivariate Statistical Analysis (MSA), a ... Multivariate Analysis of Variance, abbreviated as MANOVA, tools are introduced and illustrated in Section 14.6 and some tests ...
... cluster analysis and principal component analysis, and factor analysis and cluster analysis. The author has undoubtedly taken ... 6. Cluster analysis. 7. Factor analysis. 8. Multidimensional scaling. Part III. Non-Gaussian Analysis:. 9. Towards non- ... 2. Principal component analysis. 3. Canonical correlation analysis. 4. Discriminant analysis. Part II. Factors and Groupings:. ... It] will also be useful for working statisticians who are interested in analysis of multivariate or high-dimensional data. ...
  • In this part, students see how to model inter-variable relations: linear models (including variance and variance-covariance analysis) which make it possible to use explanatory variables to explain response variable variation. (uclouvain.be)
  • Subsequent sections describe bivarate and multiple linear regression analyses, one-way and two-way analysis of variance (ANOVA) and covariance (ANCOVA), and path analysis. (oxfordscholarship.com)
  • Statistical inference for the mean of a multivariate normal with known variance, and when the variance matrix is estimated. (bristol.ac.uk)
  • Sections 14.4 and 14.5 deal with the inference problem related to the mean vectors of multivariate data, whereas inference related with the variance-covariance matrix are performed in Sections 14.7 and 14.8 . (oreilly.com)
  • Multivariate Analysis of Variance, abbreviated as MANOVA, tools are introduced and illustrated in Section 14.6 and some tests for independence of sub-vectors are addressed in Section 14.9 . (oreilly.com)
  • In statistics, multivariate analysis of variance (MANOVA) is a procedure for comparing multivariate sample means. (wikipedia.org)
  • MANOVA is a generalized form of univariate analysis of variance (ANOVA), although, unlike univariate ANOVA, it uses the covariance between outcome variables in testing the statistical significance of the mean differences. (wikipedia.org)
  • Where sums of squares appear in univariate analysis of variance, in multivariate analysis of variance certain positive-definite matrices appear. (wikipedia.org)
  • Multivariate Analysis of Variance (MANOVA): I. Theory" (PDF). (wikipedia.org)
  • Camo http://www.camo.com/multivariate_analysis.html Chiani, M. (2016), "Distribution of the largest root of a matrix for Roy's test in multivariate analysis of variance", Journal of Multivariate Analysis, 143: 467-471, arXiv:1401.3987v3, doi:10.1016/j.jmva.2015.10.007 I.M. Johnstone, B. Nadler "Roy's largest root test under rank-one alternatives" arXiv preprint arXiv:1310.6581 (2013) Frane, Andrew (2015). (wikipedia.org)
  • A commonly used multivariate version of the ANOVA F-statistic is Wilks' Lambda (Λ), which represents the ratio between the error variance (or covariance) and the effect variance (or covariance). (wikipedia.org)
  • Multiple analysis of variance (MANOVA) or multiple analysis of covariance (MANCOVA). (wikipedia.org)
  • The additive genetic variance-covariance matrix (G) summarizes the multivariate genetic relationships among a set of traits. (nih.gov)
  • The geometry of G describes the distribution of multivariate genetic variance, and generates genetic constraints that bias the direction of evolution. (nih.gov)
  • Determining if and how the multivariate genetic variance evolves has been limited by a number of analytical challenges in comparing G-matrices. (nih.gov)
  • Multivariate ANalysis of VAriance ( MANOVA ) uses the same conceptual framework as ANOVA . (kovcomp.co.uk)
  • Analysis of Variance (ANOVA) and Covariance (ACOVA)7. (indigo.ca)
  • The following example employs multivariate analysis of variance (MANOVA) to measure differences in the chemical characteristics of ancient pottery found at four kiln sites in Great Britain. (sas.com)
  • Organized into six chapters, this book begins with an overview of the three problems in multiple regression, principal components analysis, and multiple discriminant analysis. (elsevier.com)
  • Among techniques covered are correspondence analysis, principal components analysis, canonical correlation analysis and various ordination techniques. (powells.com)
  • Practical image analysis with Principal Components Analysis (PCA) demonstrates how information from hyperspectral images can be compressed and displayed, and how classification tools can be used to identify chemically similar areas. (eigenvector.com)
  • The unit will cover the commonly used multivariate data analyses of principal components analysis, cluster analysis, discriminant functions analysis and non-metric multidimensional scaling, as well as parametric and permutational hypothesis testing techniques. (edu.au)
  • Methods of Multivariate Analysis was among those chosen. (ebooks.com)
  • The Second Edition of Alvin Rencher's Methods of Multivariate Analysis provides students of all statistical backgrounds with both the fundamental and more sophisticated skills necessary to master the discipline. (ebooks.com)
  • Methods of Multivariate Analysis provides an authoritative reference for statistics students as well as for practicing scientists and clinicians. (ebooks.com)
  • Presents the concepts and methods of multivariate analysis at a level that is readily understandable by readers who have taken two or more statistics courses. (pearson.com)
  • Detailed description of likelihood ratio models for determining the evidential value of multivariate physicochemical data. (oreilly.com)
  • Discriminant function analysis ANCOVA MANOVA [1] Statsoft Textbook, ANOVA/MANOVA. (wikipedia.org)
  • One of the main application of the MANOVA is multivariate comparison testing where parameters for the various categories of a factor are tested to be significantly different or not. (kovcomp.co.uk)
  • When a k-way MANOVA is applied and the corresponding option is enabled, the results of the multivariate tests are displayed. (kovcomp.co.uk)
  • Examples include: Two-way MANOVA for plastic film data (Example 6.11), Principal component analysis of turtle carapace data (Example 8.4), Factor analysis of consumer preference data (Example 9.9), Discriminant analysis of business school admission data (Example 11.11) and others. (pearson.com)
  • In many cases multivariate data are converted/transformed into feature vectors which can then be applied in data mining, machine learning, and information retrieval algorithms. (tu-darmstadt.de)
  • To present various aspects of multivariate analysis, covering data exploration, modeling and inference. (bristol.ac.uk)
  • Classification Analysis: Allocation of Observations to Groups. (ebooks.com)
  • and identifying, by a computer, the material from results of the multivariate data analysis including the identification or classification of the grouping of the spectrum based on the barcode. (freepatentsonline.com)
  • 13. The system of claim 12, wherein the clustering processor performs a multivariate data analysis classification process based on the sign of the second derivative of the spectrum. (freepatentsonline.com)
  • The Unscrambler® X Prediction Engine and Unscrambler® X Classification Engine version 10.2 use the advanced multivariate prediction and classification models from the latest version of The Unscrambler® X. (prweb.com)
  • The use of multivariate statistics for the analysis and classification of New Testament manuscripts is examined, the problem of coding the manuscripts for statistical analysis is considered, and various coding schemes are presented. (reltech.org)
  • Multivariate normal distribution theory, correlation and dependence analysis, regression and prediction, dimension-reduction methods, sampling distributions and related inference problems, selected applications in classification theory, multivariate process control, and pattern recognition. (gatech.edu)
  • Cluster analysis and artificial neural networks multivariate classification of onion varieties. (biomedsearch.com)
  • The advantage of these approaches is reflected in the fact that the analysis, classification and ranking can be done for all countries, based on all indicators of external indebtedness at the same time. (srce.hr)
  • Examples include: Simultaneous confidence region and intervals in Section 6.2, Multivariate linear regression model in Section 7.7, Sample principal components and their properties in Section 8.3, Classification rules in Section 11.3 and others. (pearson.com)
  • This hands-on course considers methods for visualization, pattern recognition, classification, curve resolution, chemical mapping, regression and analysis of particles and textures. (eigenvector.com)
  • Significantly revised and expanded, Multivariate Statistical Analysis, Second Edition addresses several added topics related to the properties and characterization of symmetric distributions, elliptically symmetric multivariate distributions, singular symmetric distributions, estimation of covariance matrices, tests of mean against one-sided alternatives, and correlations in symmetrical distributions. (routledge.com)
  • Results from the DDA showed that the PAHs distributions give the best discrimination capacity within the data set, whereas the PAH distribution in intermediate particle fractions incorporates noise in the statistical analysis. (igi-global.com)
  • We do not however have any assurance that all data are well modelled by these distributions as (1) the space of multivariate extreme-value copulas is infinite dimensional, (2) data on ozone levels are prone to outliers.The talk addresses the robust methods required for a proper analysis. (gerad.ca)
  • The technique takes into account multiple variables simultaneously, enabling the study of complex data sets that are beyond the capabilities of traditional univariate analysis. (appliedmaterials.com)
  • The univariate analysis identifies differentially abundant microbiome taxa subject to multiple correction under certain statistical error measurements such as false discovery rate, which is typically complicated by the high-dimensionality of taxa and complex correlation structure among taxa. (frontiersin.org)
  • The same data connections are used for model building and ongoing multivariate and univariate SPC analysis. (adeptscience.co.uk)
  • The material is identified from results of the multivariate data analysis based on the barcode. (freepatentsonline.com)
  • In addition discriminant analysis, cluster analysis and correspondence analysis are mentioned. (uib.no)
  • For this new edition, the book has been updated and extensively revised and now includes an extended chapter on cluster analysis. (springer.com)
  • Three appendixes contain some of the required mathematical and statistical detail plus heuristic examples of three multivariate techniques--component, cluster, and discriminant analysis. (cdc.gov)
  • Multiway analysis is a specialized branch of the larger field of multivariate statistics that extends the standard methods for two-way data, such as component analysis, factor analysis, cluster analysis, correspondence analysis, and multidimensional scaling to multiway data. (wiley.com)
  • A major advantage and important feature of this book is that it illustrates the interconnection of various techniques, such as the connection between discriminant analysis and principal component analysis, cluster analysis and principal component analysis, and factor analysis and cluster analysis. (cambridge.org)
  • 1.1 This guide covers the applications of multivariate data analysis (MVDA) to support pharmaceutical development and manufacturing activities. (astm.org)
  • Emphasizes the applications of multivariate methods and, consequently, they have made the mathematics as palatable as possible. (pearson.com)
  • Emphasis on applications of multivariate methods. (pearson.com)
  • The Advanced Symposium was the first to address the new innovative approaches in multi- variate analysis to develop modern analytical and yet practical procedures to meet the needs of researchers and the societal need of statistics. (springer.com)
  • Among the most powerful mathematical tools for organizing and helping to structure complex, heterogeneous and noisy data are the tools provided by multivariate statistical analysis (MSA) approaches. (scirp.org)
  • These multivariate statistical approaches are more suitable for the complex pattern of brain activity related to nociceptive processing and pain modulation, both in health and disease (for a review, see Ref. 15 ). (lww.com)
  • In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. (frontiersin.org)
  • Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. (frontiersin.org)
  • To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. (frontiersin.org)
  • The schedule leaves room for user feedback on T MVA, and ideas for new multivariate approaches or improvements on existing methods. (cern.ch)
  • In this mini-review, recent developments in multivariate calibration methods for NIR spectroscopic analysis, including non-linear approaches and ensemble techniques, are briefly summarized. (rsc.org)
  • New techniques were based on existing multivariate approaches, Partial Least Squares (PLS) and Non-negative Matrix Factorization (NMF) and validated using existing data sets. (queensu.ca)
  • Part 2: Techniques of multivariate data analysis. (uclouvain.be)
  • In this part, students learn about basic dimension reduction techniques for continuous and qualitative variables (principal components, correspondence analysis). (uclouvain.be)
  • It is written for the user and potential user of multivariate techniques as well as for students coming to the subject for the first time. (google.com)
  • Other chapters consider the topics of eigenstructures and linear transformations that are important to the understanding of multivariate techniques. (elsevier.com)
  • Multivariate Analysis Techniques With Application in Mining. (cdc.gov)
  • This Bureau of Mines report analyzes data on the physical properties of quartzite rock from the Crescent Mine, Coeur d'Alene Mining District, Kellogg, Idaho, to demonstrate the use of multivariate techniques. (cdc.gov)
  • Covering the tools and techniques of both multivariate and geographical analysis, this course provides hands-on experience visualizing data that represents multiple variables. (coursera.org)
  • While originally developed to support the analysis of T cell functional profiles, these techniques are amenable to a broad range of datatypes. (nih.gov)
  • Multivariate techniques are used across the whole range of fields of statistical application: in medicine, physical and biological sciences, economics and social science, and of course in many industrial and commercial applications. (bristol.ac.uk)
  • However, extracting useful insights from that information or leveraging that information to implement effective process monitoring and control systems often requires the application of lesser known multivariate data analysis and data mining techniques. (qualitymag.com)
  • An understanding of linear algebra, statistics, and principal component analyses and related techniques is assumed, though the author makes an effort to keep the presentation at a conceptual, rather than mathematical, level wherever possible. (wiley.com)
  • Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. (cambridge.org)
  • Fully integrated model building combines Quality Analyst's exceptional database connectivity capabilities with Pirouette's comprehensive multivariate modeling techniques. (adeptscience.co.uk)
  • The presentation integrates both theory and practice including both the analysis of formal linear multivariate models and exploratory data analysis techniques. (cmu.ac.th)
  • Multivariate data analysis tools and techniques are applied based on the barcode. (freepatentsonline.com)
  • Given the increasing complexity of modern HEP experiments, multivariate analysis techniques have proven indispensable to extract the maximum information content out of the data. (cern.ch)
  • Presents a system of multivariate analysis techniques in cases where statistical data may be of different measurement levels such as nominal, ordinal or interval. (powells.com)
  • It covers methods of studying the stability of these techniques, including resampling by the bootstrap and jackknife and discusses sensitivity analysis through first-order approximations. (powells.com)
  • When appropriate statistical techniques are used, these features are an automatic result of the analysis. (reltech.org)
  • More specifically, this project will be focused on hybrid modeling, meaning integration of different modeling techniques, for example how multivariate analysis technology can be used to train and fine-tune deep learning models and vice-versa, how deep learning can be used as pretreatment tool for multivariate analysis. (kdnuggets.com)
  • Calibration, however, is one of the important techniques needed to ensure the quality and practicability of the analyses. (rsc.org)
  • The new techniques developed, PCA-PLS-LDA (Principal Component Analysis - Partial Least Squares - Linear Discriminant Analysis), PCA-PLS-MLR (Principal Component Analysis - Partial Least Squares -Multiple Linear Regression) and Waveform Similarity (based on NMF) were developed to address the challenging characteristics of biomechanical data, variability and correlation. (queensu.ca)
  • Multivariate Data Analysis (MVA) and Design of Experiments (DoE) are advanced analysis techniques which enable biopharmaceutical companies to improve their data analysis and ultimately accelerate development, reduce process-related costs and shorten the time to market.Given the large number and complexity of variables in biological production processes, MVA has significant advantages over traditional statistical methods. (eppendorf.com)
  • While our work primarily focuses on the Dob encryption scheme, we also believe that the presented techniques will be of particular interest to the analysis of other big-field schemes. (iacr.org)
  • Its primary goal is to impart the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. (pearson.com)
  • A clear and insightful explanation of multivariate techniques. (pearson.com)
  • In this thesis technical nonionic surfactants are studied using multivariate techniques. (dissertations.se)
  • This approach has been dictated by the increasing availability of computing hardware and software, a technological innovation that has had the twofold consequence of (1) increasing the user base for statistical analysis, and (2) increasing the variety and power of readily available analytical techniques. (abebooks.com)
  • By using advanced analytics, like bivariate and multivariate analysis, your business can add significant value to your market research and make sense of a chaotic business environment. (outsource2india.com)
  • Bivariate analysis can help you effortlessly evaluate the relationship between any two variables. (outsource2india.com)
  • The basic purpose of both multivariate regression analysis and bivariate analysis is to find patterns and exceptions in data. (outsource2india.com)
  • Bivariate and Multivariate Linear Regression Analysis6. (indigo.ca)
  • An approach to increase the efficiency of mapping quantitative trait loci (QTL) was proposed earlier by the authors on the basis of bivariate analysis of correlated traits. (genetics.org)
  • Mathematical symbols are reserved for introducing the basic univariate and bivariate concepts, while multivariate topics are treated conceptually as extensions of the bivariate relation. (abebooks.com)
  • books.google.com - This book is an introduction to the principles and methodology of modern multivariate statistical analysis. (google.com)
  • The book surveys the basic principles of multivariate statistical data analysis and emphasizes both exploratory and inferential statistics. (springer.com)
  • Nonlinear Multivariate Analysis: Principles and Possibilities. (powells.com)
  • We focus on the underlying principles you need to explore multivariate data sets and test hypotheses. (edu.au)
  • Introduction to Hyperspectral/Multivariate Image Analysis (MIA) shows how to apply multivariate and machine learning methods to these data cubes to extract maximum information. (eigenvector.com)
  • Jeremy J. Andrew and Thomas M. Hancewicz, "Rapid Analysis of Raman Image Data Using Two-Way Multivariate Curve Resolution," Applied Spectroscopy. (freepatentsonline.com)
  • In this study, we have developed a Raman spectroscopy based molecular imaging method and combined multivariate curve resolution analysis to enable detection and visualization of multiple polysaccharide components simultaneously at the single cell level. (nature.com)
  • Multivariate Curve Resolution (MCR) on images is presented and it is demonstrated how it can be used to create chemical maps. (eigenvector.com)
  • Climate-Leaf Analysis Multivariate Program (CLIMAP) An approach to the estimation of mean annual temperatures in the past based on a suite of 29 characters found in the leaves of dicotyledonous plants known to have been present at the site. (encyclopedia.com)
  • D. M. Hawkins and D. J. Olive, "Improved feasible solution algorithms for high breakdown estimation", Elsevier Computational Statistics & Data Analysis 30 (1999) pp. 1-11. (freepatentsonline.com)
  • On the identifiability and estimation of generalized linear models with parametric nonignorable missing data mechanism ," Computational Statistics & Data Analysis , Elsevier, pages 64-80. (repec.org)
  • This course develops the elements introduced in the basic Probability and Statistics courses within a multivariate framework, the aim being to equip students with the instruments they need to analyse multidimensional data sets. (uclouvain.be)
  • The course contains descriptive multivariate statistics, multivariate distribution theory such as the multinormal and the Wishart distribution. (uib.no)
  • The course gives an introduction to multivariate statistics with the emphasis on practical applications. (uib.no)
  • His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. (springer.com)
  • He has taught mathematical statistics, multivariate analysis, bootstrap methods in statistics and econometrics at several European universities. (springer.com)
  • The practical exercises include solutions that can be found in Härdle, W. and Hlavka, Z., Multivariate Statistics: Exercises and Solutions. (springer.com)
  • Pierre Trebbia and Noel Bonnet, "EELS elemental mapping with unconventional methods I Theoretical basis: image analysis with multivariate statistics and entropy concepts" Ultramicroscopy vol. 34 (1990) pp. 165-178. (freepatentsonline.com)
  • This is one of the best books on Multivariate Statistics thta I have ever read. (google.com)
  • vii viii PREFACE Papers presented at the Symposium by e1l11lJinent researchers in the field were geared not Just for specialists in statistics, but an attempt has been made to achieve a well balanced and uniform coverage of different areas in multi- variate modeling and data analysis. (springer.com)
  • Having a thorough understanding of basic statistics can facilitate this process as multivariate methods have as their foundation many of these basic statistical procedures. (oxfordscholarship.com)
  • The primary intention is to help prepare entry level doctoral students and early career social work researchers in the use of multivariate statistical methods by offering a straightforward and easy to understand explanation of these methods and the basic statistics that inform them. (oxfordscholarship.com)
  • Multivariate analysis is a branch of statistics involving the consideration of objects on each of which are observed the values of a number of variables. (bristol.ac.uk)
  • I must highly commend the author for writing an excellent comprehensive review of multivariate and high dimensional statistics … The lucid treatment and thoughtful presentation are two additional attractive features … Without any hesitation and with admiration, I would give the author a 10 out of 10 … The feat she has accomplished successfully for this difficult area of statistics is something very few could accomplish. (cambridge.org)
  • Applied multivariate statistics for the social sciences. (wikipedia.org)
  • Multivariate statistics typically use algorithms to learn patterns of activity related to different states (machine learning) to make predictions of brain states. (lww.com)
  • He is the author of Linear Models in Statistics and Multivariate Statistical Inference and Applications, both available from Wiley. (ebooks.com)
  • In this pocket guide, the authors introduce readers to three of the more frequently used multivariate methods in social work research withan emphasis on basic statistics. (indigo.ca)
  • The primary aim is to prepare entry-level doctoral students and early career social work researchers in the use of multivariate methods by providing an easy-to-understand presentation, building on the basic statistics that inform them. (indigo.ca)
  • Treats all the basic and important topics in multivariate statistics. (isbn.nu)
  • For courses in Multivariate Statistics, Marketing Research, Intermediate Business Statistics, Statistics in Education, and graduate-level courses in Experimental Design and Statistics. (pearson.com)
  • Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analyzing multivariate data, the text assumes two or more statistics courses as a prerequisite. (pearson.com)
  • In so doing, the unit provides you with an understanding of how multivariate research is designed, analysed and interpreted using statistics. (edu.au)
  • Op basis van multivariate data analyses zijn bijvoorbeeld instelparameters van een productieproces te relateren aan kwaliteitskenmerken van het resulterende product. (tue.nl)
  • By doing this unit, you will be able to use multivariate data analyses using a wide-range of data and present in a format for publication. (edu.au)
  • Univariate extreme value methods do not allow for the required inference and multivariate methods exploiting the joint dependence of the data are necessary. (gerad.ca)
  • Also, the position is taken that the methods of statistical analysis fill one or more of three basic objectives, including data reduction, inference, and the identification of associations among variables. (abebooks.com)
  • Multivariate analysis of covariance (MANCOVA) is an extension of analysis of covariance (ANCOVA) methods to cover cases where there is more than one dependent variable and where the control of concomitant continuous independent variables - covariates - is required. (wikipedia.org)
  • We describe the application of random skewers, common subspace analysis, the 4th-order genetic covariance tensor and the decomposition of the multivariate breeders equation, all within a Bayesian framework. (nih.gov)
  • We propose Multivariate Tobit models with a factor structure on the covariance matrix. (ssrn.com)
  • The proposed multivariate analogue of QTL contribution to the broad-sense heritability based on interval-specific calculation of eigenvalues and eigenvectors of the residual covariance matrix allows prediction of the expected QTL detection power and mapping resolution for any subset of the initial multivariate trait complex. (genetics.org)
  • Such models are particularly useful in the exploratory analysis of multivariate censored data and the identification of latent variables from behavioral data. (ssrn.com)
  • Multivariate Analysis: Factor and Discriminant Analyses. (igi-global.com)
  • The complexity of social problems necessitates that social work researchers utilize multivariate statistical methods in their investigations. (oxfordscholarship.com)
  • Mathematical Tools for Applied Multivariate Analysis provides information pertinent to the aspects of transformational geometry, matrix algebra, and the calculus that are most relevant for the study of multivariate analysis. (elsevier.com)
  • Focusing on applications this book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who need to analyze statistical data. (springer.com)
  • And with multivariate data, what we're interested in is really any statistical technique used to analyze data from more than one variable. (coursera.org)
  • Choosing Analyze:Multivariate ( Y X ) gives you access to a variety of multivariate analyses . (sas.com)
  • Multivariate Data Analysis refers to any statistical technique used to analyze data that arises from more than one variable. (omicsonline.org)
  • SPICE: exploration and analysis of post-cytometric complex multivariate datasets. (nih.gov)
  • Polychromatic flow cytometry results in complex, multivariate datasets. (nih.gov)
  • To date, tools for the aggregate analysis of these datasets across multiple specimens grouped by different categorical variables, such as demographic information, have not been optimized. (nih.gov)
  • Heel, M. , Portugal, R. and Schatz, M. (2016) Multivariate Statistical Analysis of Large Datasets: Single Particle Electron Microscopy. (scirp.org)
  • PyMVPA eases statistical learning analyses of large datasets. (ascl.net)
  • All chapters include practical exercises that highlight applications in different multivariate data analysis fields, and all the examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. (springer.com)
  • All chapters include practical exercises that highlight applications in different multivariate data analysis fields. (springer.com)
  • The multivariate chapters conclude with brief step-by-step instructions for conducting multiple regression analysis and one-way ANOVA in Statistical Package for the Social Sciences (SPSS), and path analysis inAmos, using data from the National Educational Longitudinal Study of 1988 (NELS: 88). (indigo.ca)
  • Contains the methodological 'tools' of multivariate analysis in chapters 5 through 12. (pearson.com)
  • A multivariate logistic regression analysis of patient symptoms and signs and laboratory findings associated with the diagnosis of acute pelvic inflammatory disease was performed with use of data from 628 women who were clinically diagnosed as having the disease for the first time at the University of Lund, Sweden. (nih.gov)
  • Principal component analysis denotes a popular algorithmic technique to dimension reduction and factor extraction. (hu-berlin.de)
  • The gametic disequilibria between all possible pairs of loci were examined for a set of eight codominant loci in each of fifty Yanomama villages, using a multivariate correlation analysis which reduces the results to a single measure of departure from multiple-locus-gametic equilibrium. (genetics.org)
  • Discriminant function analysis Repeated measures design Canonical correlation analysis Warne, R. T. (2014). (wikipedia.org)
  • You can use principal component analysis to examine relationships among several variables, canonical correlation analysis and maximum redundancy analysis to examine relationships between two sets of interval variables, and canonical discriminant analysis to examine relationships between a nominal variable and a set of interval variables. (sas.com)
  • This work outlines the theoretical advantages of multivariate methods in biomechanical data, validates the proposed methods and outlines new clinical findings relating to knee osteoarthritis that were made possible by this approach. (queensu.ca)
  • Multidimensional Scaling and Correspondence Analysis. (powells.com)
  • In this lecture, we want to talk about attributes of a multivariate data visualization. (coursera.org)
  • With the growing relevance of visualization and interactive interfaces in todays' data science workflows, Information visualization and visual analytics play an important role in the analysis of multivariate data. (tu-darmstadt.de)
  • Part 3: Multivariate analysis models. (uclouvain.be)
  • Models adapted to categorical response variable are also introduced, log-linear models for contingency tables, the logit model and discrimination analysis models. (uclouvain.be)
  • This analysis tests two models: model I excludes variables based on worker perceptions that were suspected to be endogenous with job satisfaction ( HHA feels valued by organization, Aide feels involved in challenging work, Aide feels trusted with patient care decisions, Aide feels confident in ability to do job, Time for ADLs, Satisfaction with hours, Aide feels respected by supervisor, Aide feels respected by patients ), while model II includes these variables. (hhs.gov)
  • Building on Koop, Pesaran and Potter (1996), the authors propose the `generalised' impulse response analysis for unrestricted vector autoregressive (VAR) and cointegrated VAR models. (repec.org)
  • Generalised Impulse Response Analysis in Linear Multivariate Models ," Cambridge Working Papers in Economics 9710, Faculty of Economics, University of Cambridge. (repec.org)
  • Linear Statistical Models and Related Methods With Applications to Social Research John Fox A comprehensive, modern treatment of linear models and their variants and extensions, combining statistical theory with applied data analysis. (google.com)
  • As with units MATH30013 (Linear and Generalised Linear Models) and MATH 33800 (Time Series Analysis), this course is concerned with developing statistical methodology for a particular class of problems. (bristol.ac.uk)
  • Multivariate data analysis shifts the focus from individual factors to relationships among variables, identifies root causes and indirect effects, and allows for the use of predefined models and data templates to speed the analysis. (appliedmaterials.com)
  • This course develops the foundations of ordinary least squares (OLS) regression analysis and teaches students how to specify, estimate, and interpret multivariate regression models. (du.edu)
  • Special topics also covered include regression models that include dummy variables, log-linear models, fixed effects models, a brief discussion of instrumental variables, and an introduction to time-series analysis and forecasting. (du.edu)
  • Real-time analysis is provided by NWA Focus EMI™ using the same database connections and multivariate models, simplifying the transition from off-line to real-time multivariate SPC. (adeptscience.co.uk)
  • Multivariate models are incorporated into the Quality Analyst Data Set definitions. (adeptscience.co.uk)
  • Models as Gauges for the Analysis of Binary Data. (powells.com)
  • Research analysts use multivariate models to forecast investment outcomes to understand the possibilities surrounding their investment exposures and to better mitigate risks. (trade2win.com)
  • Multivariate Models Multivariate models can be thought of as complex, "What if? (trade2win.com)
  • Multivariate Analysis deals with observations on more than one variable where there is some inherent interdependence between the variables. (nii.ac.jp)
  • In the present study, statistical methods based on multivariate analyses such as the Descriptive Discriminant Analysis (DDA) and Principal Component Analysis (PCA) were applied to determine relationships between particle sizes and the composition of the associated semi-volatile compounds, in addition to evaluating these observations in relation to the emission sources, study areas, sampling campaigns and season. (igi-global.com)
  • Appropriate for experimental scientists in a variety of disciplines, this market-leading text offers a readable introduction to the statistical analysis of multivariate observations. (pearson.com)
  • Anderson, T. W. is the author of 'Introduction to Multivariate Statistical Analysis', published 2003 under ISBN 9780471360919 and ISBN 0471360910. (valorebooks.com)
  • Introduction to Hyperspectral/Multivariate Image Analysis (MIA) starts with a brief review of sources of multivariate images and tools for viewing and investigating them. (eigenvector.com)
  • Nonlinear Generalized Canonical Analysis. (powells.com)
  • The underlying data structure of these and many other quantitative studies of applied sciences is multivariate. (springer.com)
  • Near infrared (NIR) spectroscopy has been demonstrated as a powerful technique for both qualitative and quantitative analysis of complex systems in various fields. (rsc.org)
  • THE detection power and mapping resolution of marker analysis of quantitative traits are the major factors affecting practical applications of quantitative trait loci (QTL) mapping. (genetics.org)
  • We recently generalized the formerly alignment-dependent multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) method through the application of the discrete Fourier transform (DFT), allowing for its application to noncongruent and structurally diverse chemical compound data sets. (chemie.de)
  • This book discusses the mathematical foundations of applied multivariate analysis. (elsevier.com)
  • Co-pending US patent application claims "Apparatus and System for Multivariate Spectral Analysis", M. R. Keenan, et al, commonly assigned to Sandia Corporation, Albuquerque, New Mexico. (freepatentsonline.com)
  • Next, the process of setting up, carrying out, and evaluating multiway analyses is discussed along with commonly encountered issues, such as preprocessing, missing data, model and dimensionality selection, postprocessing, and transformation, as well as robustness and stability issues. (wiley.com)
  • This thesis deals with sampling, analysis and identification of such compounds, produced by microorganisms commonly found in buildings. (dissertations.se)