Multivariate Analysis
Prognosis
Retrospective Studies
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.
Risk Factors
Treatment Outcome
Survival Analysis
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.
Survival Rate
Follow-Up Studies
Prospective Studies
Predictive Value of Tests
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.
Proportional Hazards Models
Logistic Models
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.
Disease-Free Survival
Neoplasm Staging
Kaplan-Meier Estimate
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)
Cohort Studies
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 Factors
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.
Tumor Markers, Biological
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.
Cross-Sectional Studies
Analysis of Variance
Regression Analysis
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.
Risk Assessment
Odds Ratio
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.
Neoplasm Recurrence, Local
Lymphatic Metastasis
Case-Control Studies
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.
Prevalence
Chi-Square Distribution
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.
Sex Factors
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.
Incidence
Severity of Illness Index
Immunohistochemistry
Questionnaires
Disease Progression
Combined Modality Therapy
Biological Markers
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.
Socioeconomic Factors
Antineoplastic Combined Chemotherapy Protocols
Postoperative Complications
ROC Curve
Linear Models
Comorbidity
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.
Epidemiologic Methods
HIV Infections
Colorectal Neoplasms
Risk
France
Chemotherapy, Adjuvant
Principal Component Analysis
Carcinoma, Squamous Cell
Body Mass Index
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)
Tissue Array Analysis
Longitudinal Studies
Treatment Failure
Carcinoma, Hepatocellular
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.
Sensitivity and Specificity
Hospital Mortality
Registries
Ki-67 Antigen
Neoplasm Metastasis
Confidence Intervals
Reproducibility of Results
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.
Neoplasm Grading
Genotype
Discriminant Analysis
Demography
Age Distribution
Myocardial Infarction
Radiotherapy, Adjuvant
Spain
Chronic Disease
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)
Carcinoma, Non-Small-Cell Lung
Carcinoma
Remission Induction
Statistics as Topic
Lymph Node Excision
C-Reactive Protein
Cyclophosphamide
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.
Sex Distribution
Liver Transplantation
Pregnancy
Tomography, X-Ray Computed
Patient Selection
Receptors, Progesterone
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.
Models, Statistical
Health Surveys
Ovarian Neoplasms
European Continental Ancestry Group
Lymph Nodes
Karnofsky Performance Status
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.
Life Tables
Stroke
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 Cirrhosis
Statistics, Nonparametric
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)
Receptors, Estrogen
Gene Expression Regulation, Neoplastic
Coronary Artery Disease
Hematopoietic Stem Cell Transplantation
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.
Republic of Korea
Health Status
Kidney Failure, Chronic
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.
Brain Neoplasms
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.
Preoperative Care
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)
Receptor, erbB-2
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.
Hypertension
Quality of Life
Immunoenzyme Techniques
Asian Continental Ancestry Group
Outcome Assessment (Health Care)
African Americans
Biopsy
Neoadjuvant Therapy
Renal Dialysis
Diabetes Mellitus
Prostate-Specific Antigen
Databases, Factual
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, Homologous
Carcinoma, Ductal, Breast
Coronary Angiography
Ethnic Groups
Magnetic Resonance Imaging
Graft Survival
Carcinoma, Renal Cell
Cause of Death
Actuarial Analysis
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.
Health Knowledge, Attitudes, Practice
Doxorubicin
Stroke Volume
Neoplasm Proteins
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.
Prostatectomy
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).
Coronary Disease
Cardiovascular Diseases
Survival
Bacteremia
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.
Heart Failure
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.
Obesity
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).
Cluster Analysis
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.
Diabetes Complications
Electrocardiography
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.
Algorithms
Tumor Suppressor Protein p53
Hispanic Americans
Echocardiography
Tissue Donors
Intensive Care Units
Fluorouracil
Gastrectomy
Tumor Burden
Alcohol Drinking
Least-Squares Analysis
Data Interpretation, Statistical
Prednisone
Reoperation
Neoplasm, Residual
Data Collection
Polymorphism, Genetic
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.
Social Class
Neoplasms, Glandular and Epithelial
Lymphoma, Non-Hodgkin
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.
Melanoma
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)
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)
applied multivariate statistical analysis johnson wichern pdf
Using Multivariate Statistics - Barbara G. Tabachnick, Linda S. Fidell - Google Books
Using Multivariate Statistics - Barbara G. Tabachnick, Linda S. Fidell - Google Books
applied multivariate statistical analysis homework solutions
Progress on Multivariate Data Analysis with TMVA (21 January 2011) · Indico
Electronic Journal of Applied Multivariate Statistics
Applying Multivariate Classification in the Life Sciences with Statistics Toolbox - Video - MATLAB
Online An Introduction To Multivariate Statistical Analysis (Wiley Series In Probability And Statistics) 2003
Biomarkers Discovery through Multivariate Statistical Methods: A Review of
Recently Developed Methods and Applications in...
Applied Statistics II | SAGE Publications Ltd
Improving epidemiologic data analyses through multivariate regression modelling | Emerging Themes in Epidemiology | Full Text
A multivariate study on Ease of Doing Business to predict a country | Hausarbeiten publizieren
Multivariate Statistical Analysis: Revised And Expanded, 2nd Edition (Hardback) - Routledge
SAS Training in the United States -- Multivariate Statistics for Understanding Complex Data
SAS Training in United Kingdom -- Multivariate Statistics for Understanding Complex Data
Applied Multivariate Statistical Analysis by Richard A. Johnson - Alibris
Wiley: Smoothing of Multivariate Data: Density Estimation and Visualization - Jussi Sakari Klemel
Time Series Analysis: Univariate and Multivariate Methods》.(21745306259071)|露天拍賣|台灣NO.1 拍賣網站
Descriptive Statistics Multivariate Data Set | http://itfeature.com
The Difference Between Bivariate & Multivariate Analyses | Sciencing
Multivariate Analysis Overcomes Complexities in Injection Molding - JMP User Community
Survey Data Analysis - ANU
Applied Multivariate Statistical Analysis | SpringerLink
Survey Data Analysis II - ANU
Cambridge Admissions Office - Doing Multivariate Analysis (DMA Intensive) - Fri 14 Feb 2020
Cambridge Centre for Teaching and Learning - Doing Multivariate Analysis (DMA Intensive) - Fri 14 Feb 2020
Researcher Development Programme (RDP) - Doing Multivariate Analysis (DMA Intensive) - Fri 14 Feb 2020
University of Cambridge training - Doing Multivariate Analysis (DMA Intensive) - Mon 28 Jan 2019
Systems and methods for identifying materials utilizing multivariate analysis techniques - ZIEGLER LAWRENCE D.
R: Multivariate Analysis of Variance
Multivariate Analysis Assignment Help - Upto 50% OFF
Introduction to Multivariate Statistical Analysis 3rd Edition | Rent 9780471360919 | 0471360910
Application of Methods of Multivariate Statistical Analysis for Study of Individual Human Sensitivity to Zero Magnetic Fields.
...
New statistical tools being developed for mining cancer data - Healthcanal.com : Healthcanal.com
Multivariate Analysis with LISREL | Karl G. Jöreskog | Springer
Multivariate Testing: When to Do It Instead of A/B/n Tests
Univariate REML analyses for multivariate data with the animal model. | WCGALP Archive
g03ad:: Multivariate Methods (NAG Toolbox)
How to determine the p value cutoff for unvariate regression analysis to be included in multivariate analysis - modeling...
IBM® SPSS® Amos GradPack 25 for Windows (12-Mo Rental) | Carleton University - Bachelor of Information Technology | Academic...
An Introduction to Applied Multivariate Analysis with R - Web Links - STHDA
Topics in Applied Multivariate Analysis by D. M. Hawkins (9780521090704)
Multivariate analysis of dynamical processes动态过程的多元分析
multivariate data | Statistics Help @ Talk Stats Forum
TMS041
| Chalmers
Why Dell Acquired StatSoft: A Multivariate Perspective - ProSensus - Multivariate Analysis Consulting for Manufacturing
Search Results - University Press Scholarship Online - University Press Scholarship
Basic Statistics in Multivariate Analysis - Oxford Scholarship
Multivariate Data Analysis - 2 days course - Switzerland - Umetrics
Multivariate Data Analysis - 2 days course - Asia - Umetrics
Assessing the severity of psoriasis through multivariate analysis of optical images from non-lesional skin<...
everyone, Ive been running Multivariate linear regression models and was wondering if there was a way to extract the full...
STAT S640 29105 Multivariate Data Analysis
Multivariate Statistics : High-Dimensional and Large-Sample Approximations | Multivariate Analysis | Data Analysis | General &...
Multivariate Analysis Resource | Learn About, Share and Discuss Multivariate Analysis At Defaultlogic.com
Applied Multivariate Statistical Analysis by A. Satorra, Risto D.H. Heijmans, D.S.G. Pollock, Albert - ASgraphic |Antonio Books
Patterns of Crime in Canadian Cities : A Multivariate Statistical Analysis
Exploratory Analysis of Multivariate Data (Unsupervised Image Segmentation and Data Driven Linear and Nonlinear Decomposition)<...
STA 667 An Introduction to Multivariate Statistical Analysis (3)
AVHANDLINGAR.SE: Modeling adsorption of organic compounds on activated carbon a multivariate approach
Survival analysis Part III: multivariate data analysis -- choosing a model and assessing its adequacy and fit. - Oxford...
Cedric]
Epub Shearlets Multiscale Analysis For Multivariate Data 2012
Multivariate Statistics - Exercises and Solutions | Wolfgang Karl Härdle | Springer
NITRC: CONN : functional connectivity toolbox: RE: Clarification on contrasts in CONN 2nd-level multivariate analysis
One-Way MANOVA in R
Graduate School of Life Sciences Training - Doing Multivariate Analysis (DMA-1) - Fri 17 Jan 2020
Bioinformatics Training - Doing Multivariate Analysis (DMA-1) - Fri 17 Jan 2020
Multivariate Statistics
Multivariate Statistics
Exploring good cycling cities using multivariate statistics | SpringerLink
Multivariate analysis of variance - Infogalactic: the planetary knowledge core
Multivariate Analysis In The Pharmaceutical Industry | Download eBook
Thorpe, Multivariate Statistical Analysis for Manuscript
Classification
Gardlo Alžběta Ph.D. | Institute of Molecular and Translational Medicine
About the Centre
Plus it
Multivariate and cluster analysis • Genstat Knowledge Base
Vibrational Spectroscopy and Multivariate Methods in Chemical Analysis
- Attic Books
Age&Gender Covariates; Continuous data; Multivariate Analysis | OpenMx
Multivariate Time Series Analysis of the Investment Guarantee in Canadian Segregated Fund Products
PHD104 Multivariate analysis: Applied factor and regression analysis
- PhD Courses in the Social Sciences
MultiVariate SPC: Multivariate Analysis module - Adept Scientific
Análisis Multivariado de la Varianza (MANOVA) | Software estadístico Excel
What statistical analysis should I use? Statistical analyses using SPSS - IDRE Stats
Workshop on Data Analytics for Advanced Research using Multivariate Analysis - Thapar Institute of Engineering and Technology
CiteSeerX - Citation Query Multivariate Normal Probabilities with Error Bound. Applied Statistics
Classification and Multivariate Analysis for Complex Data Structures » Tonitop.org
Applied Multivariate Analysis - Utrecht Summer School
Trend Following risk factors versus buy and hold - Traders Roundtable
R-Forge: Software Map
R-Forge: Software Map
Multivariate analysis of spectra of cyanate ester/bismaleimide blends and correlations with properties - Surrey Research...
Granger causality
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 ...
Hotelling's T-squared distribution
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 ...
Total sum of squares
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 ...
General linear model
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 ...
Partial least squares regression
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 ...
Wilks's lambda distribution
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- ...
Weighted least squares
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. ...
Inverse-Wishart distribution
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 ...
Cryptanalysis
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 ...
Elliptical distribution
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 ...
Thymidine kinase in clinical chemistry
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 ...
Repeated measures design
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 ...
Hermann Moisl
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 ...
Design matrix
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 ...
Growth curve (statistics)
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 ...
Mikhail Kharit
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. ...
Latent variable
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 ...
Matrix calculus
... 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 ...
Jan de Leeuw
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 ...
Wim Crusio
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 ...
Experimental psychology
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: ...
Complex inverse Wishart distribution
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 { ...
Dipak K. Dey
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 ...
Adjusted mutual information
Meila, M. (2007). "Comparing clusterings-an information based distance". Journal of Multivariate Analysis. 98 (5): 873-895. doi ...
Fowlkes-Mallows index
MEILA, M (1 May 2007). "Comparing clusterings-an information based distance". Journal of Multivariate Analysis. 98 (5): 873-895 ...
Least squares
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 ...
Variance
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 ...
Generalized normal distribution
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 ...
Gauss-Markov theorem
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 ...
C. R. Rao
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 ...
Level of measurement
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 ...
Mann-Whitney U test
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.. ...
Voxel-based morphometry
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. ...
Brain tumor
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. ...
Mark and recapture
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.. ...
Pleiotropy
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. ...
Dieting
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 ...
Free will
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] ...
方差分析 - 维基百科,自由的百科全书
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
Single particle analysis
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 ...
Matching pursuit
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 ...
Isotonic regression
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\ ...
Loss aversion
"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. ...
Outline of brain mapping
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 ...
Computer algebra
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 ...
Probability distribution
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 ...
Grounded theory
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 ...
Strok bahasa Indonesia, ensiklopedia bebas
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: ...
Non-invasive intracranial pressure measurement methods
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 ...
Health
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 ...
Opinion poll
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 ...
Chi-squared test
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 ...
Random assignment
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- ...
Soy sauce
... 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". ...
Student's t-test
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 ...
Aid
"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 ...
Dark triad
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 ...
Evolution of mammals
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 ...
ಮೆದುಳಿನ ಕ್ಯಾನ್ಸರ್ ಗೆಡ್ದೆ(ಊತ) - ವಿಕಿಪೀಡಿಯ
... 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- ...
Structural equation modeling
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 ...
Multivariate Statistical Analysis
... 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.. ...
Course: Multivariate Data Analysis
Principles of Multivariate Analysis - Wojtek Krzanowski - Google Books
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. ...
Climate-Leaf Analysis Multivariate Program | Encyclopedia.com
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 Analysis | University of Bergen
... 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, ...
Mathematical Tools for Applied Multivariate Analysis - 1st Edition
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 ...
Applied Multivariate Statistical Analysis | Wolfgang Karl Härdle | Springer
... 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 ...
Multivariate Analysis with LISREL | Karl G. Jöreskog | Springer
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 ...
Method of multivariate spectral analysis - Sandia Corporation
... 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, ...
Multivariate Analyses: Job Satisfaction | ASPE
Welcome letter, Multivariate Data Analysis
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 ...
Applied Multivariate Statistical Analysis | SpringerLink
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 ...
Analysis of multivariate missing data with nonignorable nonresponse
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 ...
Generalized impulse response analysis in linear multivariate models
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, ...
Multivariate analysis: methods and applications - William R. Dillon, Matthew Goldstein - Google Books
... 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 ...
Multivariate Statistical Modeling and Data Analysis | SpringerLink
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 - Getting Started | Coursera
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 ...
Basic Statistics in Multivariate Analysis - Oxford Scholarship
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 ...
Matrix Algebra - Methods of Multivariate Analysis, Third Edition - Rencher - Wiley Online Library
Multivariate analysis improves on cognitive testing in Alzheimer's disease | EurekAlert! Science News
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 ...
Statistical Analysis in Forensic Science: Evidential Values of Multivariate Physicochemical Data [Book]
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. - PubMed - NCBI
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: Evidential Value of Multivariate Physicochemical Data | Wiley
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 ...
Routledge and CRC Press Regression Analysis and Multivariate Statistics Books
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 MATHM0510 | School of Mathematics | University of Bristol
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: Revised And Expanded, 2nd Edition (Hardback) - Routledge
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, ...
Quality Software & Analysis: Multivariate Quality Control
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. - PubMed - NCBI
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 14: Multivariate Statistical Analysis - I - A Course in Statistics with R [Book]
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 ...
Analysis multivariate and high dimensional data | Statistical theory and methods | Cambridge University Press
... 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. ...
VariancePrincipal componenMethods of Multivariate AnalysisEvidential value of multivariate physicochemical dataMANOVAVectorsASPECTS OF MULTIVARIATE ANALYSISClassificationDistributionsUnivariate analysisModel building and ongResults of the multivariateClusterApplications of multivariateApproachesTechniquesBivariatePrinciplesApply multivariateCurve ResolutionEstimationStatisticsData analysesInferenceCovarianceExploratoryBehavioral ResearchDiscriminant analysesStatistical methods in their investigationsMatrix AlgebraAnalyzeDatasetsChaptersLogistic regressionFactorCorrelation analysisTheoreticalMultidimensional ScalingVisualizationModelsObservationsIntroductionNonlinearQuantitativeMathematicalCommonly
Variance18
- 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)
Principal componen4
- 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 Analysis4
- 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)
Evidential value of multivariate physicochemical data1
- Detailed description of likelihood ratio models for determining the evidential value of multivariate physicochemical data. (oreilly.com)
MANOVA4
- 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)
Vectors1
- 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)
ASPECTS OF MULTIVARIATE ANALYSIS1
- To present various aspects of multivariate analysis, covering data exploration, modeling and inference. (bristol.ac.uk)
Classification10
- 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)
Distributions3
- 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)
Univariate analysis2
- 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)
Model building and ong1
- The same data connections are used for model building and ongoing multivariate and univariate SPC analysis. (adeptscience.co.uk)
Results of the multivariate1
- The material is identified from results of the multivariate data analysis based on the barcode. (freepatentsonline.com)
Cluster5
- 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)
Applications of multivariate3
- 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)
Approaches9
- 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)
Techniques28
- 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)
Bivariate6
- 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)
Principles4
- 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)
Apply multivariate1
- 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)
Curve Resolution3
- 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)
Estimation3
- 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)
Statistics22
- 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)
Data analyses2
- 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)
Inference2
- 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)
Covariance4
- 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)
Exploratory1
- Such models are particularly useful in the exploratory analysis of multivariate censored data and the identification of latent variables from behavioral data. (ssrn.com)
Behavioral Research1
- Multivariate Behavioral Research. (wikipedia.org)
Discriminant analyses1
- Multivariate Analysis: Factor and Discriminant Analyses. (igi-global.com)
Statistical methods in their investigations1
- The complexity of social problems necessitates that social work researchers utilize multivariate statistical methods in their investigations. (oxfordscholarship.com)
Matrix Algebra1
- 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)
Analyze4
- 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)
Datasets5
- 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)
Chapters4
- 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)
Logistic regression1
- 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)
Factor1
- Principal component analysis denotes a popular algorithmic technique to dimension reduction and factor extraction. (hu-berlin.de)
Correlation analysis3
- 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)
Theoretical1
- 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 Scaling1
- Multidimensional Scaling and Correspondence Analysis. (powells.com)
Visualization2
- 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)
Models15
- 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)
Observations3
- 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)
Introduction2
- 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)
Nonlinear1
- Nonlinear Generalized Canonical Analysis. (powells.com)
Quantitative4
- 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)
Mathematical1
- This book discusses the mathematical foundations of applied multivariate analysis. (elsevier.com)
Commonly3
- 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)