###### 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.

###### 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.

###### Risk Factors

###### Linear Models

###### Cross-Sectional Studies

###### Multivariate Analysis

###### Prospective Studies

###### 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.

###### 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.

###### Questionnaires

###### 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.

###### 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.

###### 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.

###### 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.

###### Follow-Up Studies

###### 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.

###### Prognosis

###### Prevalence

###### Treatment Outcome

###### Socioeconomic Factors

###### Risk Assessment

###### Severity of Illness Index

###### Neoplasm Regression, Spontaneous

###### 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)

###### Proportional Hazards Models

###### 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.

###### Longitudinal Studies

###### Pregnancy

###### Incidence

###### 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.

###### ROC Curve

###### 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.

###### Analysis of Variance

###### 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.

###### Confidence Intervals

###### Sensitivity and Specificity

###### Genotype

###### 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.

###### 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).

###### Netherlands

###### Health Status

###### European Continental Ancestry Group

###### Health Surveys

###### Reference Values

###### Epidemiologic Methods

###### Models, Statistical

###### Disease Progression

###### Poisson Distribution

###### Survival Rate

###### Asian Continental Ancestry Group

###### Depression

###### Hypertension

###### Risk

###### Sex Distribution

###### 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)

###### Age Distribution

###### African Americans

###### Demography

###### Alcohol Drinking

###### Quality of Life

###### Anthropometry

###### Diabetes Mellitus, Type 2

A subclass of DIABETES MELLITUS that is not INSULIN-responsive or dependent (NIDDM). It is characterized initially by INSULIN RESISTANCE and HYPERINSULINEMIA; and eventually by GLUCOSE INTOLERANCE; HYPERGLYCEMIA; and overt diabetes. Type II diabetes mellitus is no longer considered a disease exclusively found in adults. Patients seldom develop KETOSIS but often exhibit OBESITY.

###### Registries

###### Body Weight

###### Aging

###### 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)

###### Statistics as Topic

###### Social Class

###### Cardiovascular Diseases

###### Genetic Predisposition to Disease

###### Polymorphism, Single Nucleotide

###### Residence Characteristics

###### Occupational Exposure

###### C-Reactive Protein

###### Gestational Age

###### Regression (Psychology)

###### 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)

###### Republic of Korea

###### 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)

###### Least-Squares Analysis

###### 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.

###### Data Interpretation, Statistical

###### Magnetic Resonance Imaging

###### Coronary Artery Disease

###### Life Style

###### Algorithms

###### Birth Weight

###### Health Knowledge, Attitudes, Practice

###### Hispanic Americans

###### HIV Infections

###### Postoperative Complications

###### 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.

###### Renal Dialysis

###### Diabetes Mellitus

###### Activities of Daily Living

###### Hospital Mortality

###### Self Report

###### Data Collection

###### Forecasting

###### Neoplasm Staging

###### Sex Characteristics

###### African Continental Ancestry Group

###### Health Status Indicators

###### Body Composition

###### Social Support

###### Health Behavior

Behaviors expressed by individuals to protect, maintain or promote their health status. For example, proper diet, and appropriate exercise are activities perceived to influence health status. Life style is closely associated with health behavior and factors influencing life style are socioeconomic, educational, and cultural.

###### Outcome Assessment (Health Care)

###### Environmental Exposure

###### Tomography, X-Ray Computed

###### Body Height

###### Spain

###### Myocardial Infarction

###### Confounding Factors (Epidemiology)

Factors that can cause or prevent the outcome of interest, are not intermediate variables, and are not associated with the factor(s) under investigation. They give rise to situations in which the effects of two processes are not separated, or the contribution of causal factors cannot be separated, or the measure of the effect of exposure or risk is distorted because of its association with other factors influencing the outcome of the study.

###### Exercise

###### Overweight

A status with BODY WEIGHT that is above certain standard of acceptable or desirable weight. In the scale of BODY MASS INDEX, overweight is defined as having a BMI of 25.0-29.9 kg/m2. Overweight may or may not be due to increases in body fat (ADIPOSE TISSUE), hence overweight does not equal "over fat".

###### Seasons

###### Neuropsychological Tests

###### Adaptation, Psychological

###### Cognition Disorders

###### Parity

###### Poverty

###### Neoplasms

###### Coronary Disease

###### Population Surveillance

###### Cholesterol, HDL

###### Disability Evaluation

###### Remission, Spontaneous

###### Parents

###### 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.

###### Interviews as Topic

###### Marital Status

###### Glomerular Filtration Rate

###### Area Under Curve

A statistical means of summarizing information from a series of measurements on one individual. It is frequently used in clinical pharmacology where the AUC from serum levels can be interpreted as the total uptake of whatever has been administered. As a plot of the concentration of a drug against time, after a single dose of medicine, producing a standard shape curve, it is a means of comparing the bioavailability of the same drug made by different companies. (From Winslade, Dictionary of Clinical Research, 1992)

###### Anxiety

###### Psychiatric Status Rating Scales

###### Waist Circumference

###### Hemoglobin A, Glycosylated

Minor hemoglobin components of human erythrocytes designated A1a, A1b, and A1c. Hemoglobin A1c is most important since its sugar moiety is glucose covalently bound to the terminal amino acid of the beta chain. Since normal glycohemoglobin concentrations exclude marked blood glucose fluctuations over the preceding three to four weeks, the concentration of glycosylated hemoglobin A is a more reliable index of the blood sugar average over a long period of time.

###### Cause of Death

###### Canada

###### Metabolic Syndrome X

A cluster of metabolic risk factors for CARDIOVASCULAR DISEASES and TYPE 2 DIABETES MELLITUS. The major components of metabolic syndrome X include excess ABDOMINAL FAT; atherogenic DYSLIPIDEMIA; HYPERTENSION; HYPERGLYCEMIA; INSULIN RESISTANCE; a proinflammatory state; and a prothrombotic (THROMBOSIS) state. (from AHA/NHLBI/ADA Conference Proceedings, Circulation 2004; 109:551-556)

###### Body Weights and Measures

###### Health Services Accessibility

###### Biometry

###### Lipids

A generic term for fats and lipoids, the alcohol-ether-soluble constituents of protoplasm, which are insoluble in water. They comprise the fats, fatty oils, essential oils, waxes, phospholipids, glycolipids, sulfolipids, aminolipids, chromolipids (lipochromes), and fatty acids. (Grant & Hackh's Chemical Dictionary, 5th ed)

###### Insulin Resistance

###### Health Care Surveys

###### Continental Population Groups

###### Causality

The relating of causes to the effects they produce. Causes are termed necessary when they must always precede an effect and sufficient when they initiate or produce an effect. Any of several factors may be associated with the potential disease causation or outcome, including predisposing factors, enabling factors, precipitating factors, reinforcing factors, and risk factors.

###### Alleles

###### Pregnancy Outcome

## Surgery-related factors and local recurrence of Wilms tumor in National Wilms Tumor Study 4. (1/22207)

OBJECTIVE: To assess the prognostic factors for local recurrence in Wilms tumor. SUMMARY BACKGROUND DATA: Current therapy for Wilms tumor has evolved through four studies of the National Wilms Tumor Study Group. As adverse prognostic factors were identified, treatment of children with Wilms tumor has been tailored based on these factors. Two-year relapse-free survival of children in the fourth study (NWTS-4) exceeded 91%. Factors once of prognostic import for local recurrence may lose their significance as more effective therapeutic regimens are devised. METHODS: Children evaluated were drawn from the records of NWTS-4. A total of 2482 randomized or followed patients were identified. Local recurrence, defined as recurrence in the original tumor bed, retroperitoneum, or within the abdominal cavity or pelvis, occurred in 100 children. Using a nested case-control study design, 182 matched controls were selected. Factors were analyzed for their association with local failure. Relative risks and 95% confidence intervals were calculated, taking into account the matching. RESULTS: The largest relative risks for local recurrence were observed in patients with stage III disease, those with unfavorable histology (especially diffuse anaplasia), and those reported to have tumor spillage during surgery. Multiple regression analysis adjusting for the combined effects of histology, lymph node involvement, and age revealed that tumor spillage remained significant. The relative risk of local recurrence from spill was largest in children with stage II disease. The absence of lymph node biopsy was also associated with an increased relative risk of recurrence, which was largest in children with stage I disease. The survival of children after local recurrence is poor, with an average survival rate at 2 years after relapse of 43%. Survival was dependent on initial stage: those who received more therapy before relapse had a worse prognosis. CONCLUSIONS: This study has demonstrated that surgical rupture of the tumor must be prevented by the surgeon, because spills produce an increased risk of local relapse. Both local and diffuse spills produce this risk. Stage II children with local spill appear to require more aggressive therapy than that used in NWTS-4. The continued critical importance of lymph node sampling in conjunction with nephrectomy for Wilms tumor is also established. Absence of lymph node biopsy may result in understaging and inadequate treatment of the child and may produce an increased risk of local recurrence. (+info)## Prolonged mating in prairie voles (Microtus ochrogaster) increases likelihood of ovulation and embryo number. (2/22207)

Prairie voles are induced ovulators that mate frequently in brief bouts over a period of approximately 24 h. We examined 1) impact of mating duration on ovulation and embryo number, 2) incidence of fertilization, 3) temporal pattern of embryo development, 4) embryo progression through the reproductive tract over time, and 5) embryo development in culture. Mating was videotaped to determine first copulation, and the ovaries were examined and the reproductive tracts flushed at 6, 8, 10, 12, 16, 20, and 24 h and 2, 3, and 4 days after first copulation. The number of mature follicles and fresh corpora lutea and the number and developmental stage of embryos were quantified. One, two-, and four-cell embryos were cultured in Whitten's medium. Mature follicles were present at the earliest time examined (6 h). Thirty-eight percent of females that had been paired for < 12 h after the first copulation ovulated, whereas all females paired >/= 12 h after the first copulation ovulated. Virtually all (> 99%) oocytes recovered from females paired for >/= 12 h after first copulation were fertilized. Pairing time after first copulation and mean copulation-bout duration were significant (p < 0.05) determinants of embryo number. Embryos entered the uterine horns and implanted on Days 3 and 4, respectively, after first copulation (Day 0). Embryos cultured in vitro underwent approximately one cell division per day, a rate similar to that in vivo. We conclude that prairie voles ovulate reliably after pairing for >/= 12 h, although some females showed exceptional sensitivity not predicted by the variables quantified. Prolonged mating for longer than 12 h increased the total embryos produced. This mechanism likely has adaptive significance for increasing offspring number. (+info)## Geographic, demographic, and socioeconomic variations in the investigation and management of coronary heart disease in Scotland. (3/22207)

OBJECTIVE: To determine whether age, sex, level of deprivation, and area of residence affect the likelihood of investigation and treatment of patients with coronary heart disease. DESIGN, PATIENTS, AND INTERVENTIONS: Routine discharge data were used to identify patients admitted with acute myocardial infarction (AMI) between 1991 and 1993 inclusive. Record linkage provided the proportion undergoing angiography, percutaneous transluminal coronary angioplasty (PTCA), and coronary artery bypass grafting (CABG) over the following two years. Multiple logistic regression analysis was used to determine whether age, sex, deprivation, and area of residence were independently associated with progression to investigation and revascularisation. SETTING: Mainland Scotland 1991 to 1995 inclusive. MAIN OUTCOME MEASURES: Two year incidence of angiography, PTCA, and CABG. Results-36 838 patients were admitted with AMI. 4831 (13%) underwent angiography, 587 (2%) PTCA, and 1825 (5%) CABG. Women were significantly less likely to undergo angiography (p < 0.001) and CABG (p < 0.001) but more likely to undergo PTCA (p < 0.05). Older patients were less likely to undergo all three procedures (p < 0.001). Socioeconomic deprivation was associated with a reduced likelihood of both angiography and CABG (p < 0.001). There were significant geographic variations in all three modalities (p < 0.001). CONCLUSION: Variations in investigation and management were demonstrated by age, sex, geography, and socioeconomic deprivation. These are unlikely to be accounted for by differences in need; differences in clinical practice are, therefore, likely. (+info)## Regional patterns of myocardial sympathetic denervation in dilated cardiomyopathy: an analysis using carbon-11 hydroxyephedrine and positron emission tomography. (4/22207)

OBJECTIVE: To assess presynaptic function of cardiac autonomic innervation in patients with advanced congestive heart failure using positron emission tomography (PET) and the recently developed radiolabelled catecholamine analogue carbon-11 hydroxyephedrine (HED) as a marker for neuronal catecholamine uptake function. DESIGN AND PATIENTS: 29 patients suffering from dilated cardiomyopathy with moderate to severe heart failure were compared with eight healthy controls. Perfusion scan was followed by HED dynamic PET imaging of cardiac sympathetic innervation. The scintigraphic results were compared with markers of disease severity and the degree of sympathetic dysfunction assessed by means of heart rate variability. RESULTS: In contrast to nearly normal perfusions, mean (SD) HED retention in dilated cardiomyopathy patients was abnormal in 64 (32)% of the left ventricle. Absolute myocardial HED retention was 10.7 (1.0)%/min in controls v 6.2 (1.6)%/min in dilated cardiomyopathy patients (p < 0.001). Moreover, significant regional reduction of HED retention was demonstrated in apical and inferoapical segments. HED retention was significantly correlated with New York Heart Association functional class (r = -0.55, p = 0. 002) and ejection fraction (r = 0.63, p < 0.001), but not, however, with plasma noradrenaline concentrations as well as parameters of heart rate variability. CONCLUSIONS: In this study, using PET in combination with HED in patients with dilated cardiomyopathy, not only global reduction but also regional abnormalities of cardiac sympathetic tracer uptake were demonstrated. The degree of abnormality was positively correlated to markers of severity of heart failure. The pathogenetic mechanisms leading to the regional differences of neuronal damage as well as the prognostic significance of these findings remain to be defined. (+info)## QT dispersion in patients with chronic heart failure: beta blockers are associated with a reduction in QT dispersion. (5/22207)

OBJECTIVE: To compare QT dispersion in patients with impaired left ventricular systolic function and in matched control patients with normal left ventricular systolic function. DESIGN: A retrospective, case-control study with controls matched 4:1 for age, sex, previous myocardial infarction, and diuretic and beta blocker treatment. SETTING: A regional cardiology centre and a university teaching hospital. PATIENTS: 25 patients with impaired left ventricular systolic function and 100 patients with normal left ventricular systolic function. MAIN OUTCOME MEASURES: QT and QTc dispersion measured by three methods: the difference between maximum and minimum QT and QTc intervals, the standard deviation of QT and QTc intervals, and the "lead adjusted" QT and QTc dispersion. RESULTS: All measures of QT/QTc dispersion were closely interrelated (r values 0.86 to 0.99; all p < 0.001). All measures of QT and QTc dispersion were significantly increased in the patients with impaired left ventricular systolic function v controls (p < 0.001): 71.9 (6.5) (mean (SEM)) v 46.9 (1.7) ms for QT dispersion, and 83.6 (7.6) v 54.3 (2.1) ms(-1-2) for QTc dispersion. All six dispersion parameters were reduced in patients taking beta blockers (p < 0.05), regardless of whether left ventricular function was normal or impaired-by 9.4 (4.6) ms for QT dispersion (p < 0.05) and by 13.8 (6. 5) ms(-1-2) for QTc dispersion (p = 0.01). CONCLUSIONS: QT and QTc dispersion are increased in patients with systolic heart failure in comparison with matched controls, regardless of the method of measurement and independently of possible confounding factors. beta Blockers are associated with a reduction in both QT and QTc dispersion, raising the possibility that a reduction in dispersion of ventricular repolarisation may be an important antiarrhythmic mechanism of beta blockade. (+info)## Early death during chemotherapy in patients with small-cell lung cancer: derivation of a prognostic index for toxic death and progression. (6/22207)

Based on an increased frequency of early death (death within the first treatment cycle) in our two latest randomized trials of combination chemotherapy in small-cell lung cancer (SCLC), we wanted to identify patients at risk of early non-toxic death (ENTD) and early toxic death (ETD). Data were stored in a database and logistic regression analyses were performed to identify predictive factors for early death. During the first cycle, 118 out of 937 patients (12.6%) died. In 38 patients (4%), the cause of death was sepsis. Significant risk factors were age, performance status (PS), lactate dehydrogenase (LDH) and treatment with epipodophyllotoxins and platinum in the first cycle (EP). Risk factors for ENTD were age, PS and LDH. Extensive stage had a hazard ratio of 1.9 (P = 0.07). Risk factors for ETD were EP, PS and LDH, whereas age and stage were not. For EP, the hazard ratio was as high as 6.7 (P = 0.0001). We introduced a simple prognostic algorithm including performance status, LDH and age. Using a prognostic algorithm to exclude poor-risk patients from trials, we could minimize early death, improve long-term survival and increase the survival differences between different regimens. We suggest that other groups evaluate our algorithm and exclude poor prognosis patients from trials of dose intensification. (+info)## Microvascular function relates to insulin sensitivity and blood pressure in normal subjects. (7/22207)

BACKGROUND: A strong but presently unexplained inverse association between blood pressure and insulin sensitivity has been reported. Microvascular vasodilator capacity may be a common antecedent linking insulin sensitivity to blood pressure. To test this hypothesis, we studied 18 normotensive and glucose-tolerant subjects showing a wide range in insulin sensitivity as assessed with the hyperinsulinemic, euglycemic clamp technique. METHODS AND RESULTS: Blood pressure was measured by 24-hour ambulatory blood pressure monitoring. Videomicroscopy was used to measure skin capillary density and capillary recruitment after arterial occlusion. Skin blood flow responses after iontophoresis of acetylcholine and sodium nitroprusside were evaluated by laser Doppler flowmetry. Insulin sensitivity correlated with 24-hour systolic blood pressure (24-hour SBP; r=-0.50, P<0.05). Capillary recruitment and acetylcholine-mediated vasodilatation were strongly and positively related to insulin sensitivity (r=0.84, P<0.001; r=0.78, P<0.001, respectively), and capillary recruitment was inversely related to 24-hour SBP (r=-0.53, P<0.05). Waist-to-hip ratio showed strong associations with insulin sensitivity, blood pressure, and the measures of microvascular function but did not confound the associations between these variables. Subsequent regression analysis showed that the association between insulin sensitivity and blood pressure was not independent of the estimates of microvascular function, and part of the variation in both blood pressure (R2=38%) and insulin sensitivity (R2=71%) could be explained by microvascular function. CONCLUSIONS: Insulin sensitivity and blood pressure are associated well within the physiological range. Microvascular function strongly relates to both, consistent with a central role in linking these variables. (+info)## Modeling breathing-zone concentrations of airborne contaminants generated during compressed air spray painting. (8/22207)

This paper presents a mathematical model to predict breathing-zone concentrations of airborne contaminants generated during compressed air spray painting in cross-flow ventilated booths. The model focuses on characterizing the generation and transport of overspray mist. It extends previous work on conventional spray guns to include exposures generated by HVLP guns. Dimensional analysis and scale model wind-tunnel studies are employed using non-volatile oils, instead of paint, to produce empirical equations for estimating exposure to total mass. Results indicate that a dimensionless breathing zone concentration is a nonlinear function of the ratio of momentum flux of air from the spray gun to the momentum flux of air passing through the projected area of the worker's body. The orientation of the spraying operation within the booth is also very significant. The exposure model requires an estimate of the contaminant generation rate, which is approximated by a simple impactor model. The results represent an initial step in the construction of more realistic models capable of predicting exposure as a mathematical function of the governing parameters. (+info)###### Simple Regression Analysis in Public Health | Coursera

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**Regression** **analysis**

Wikimedia Commons has media related to

**Regression****analysis**. "**Regression****analysis**", Encyclopedia of Mathematics, EMS Press, 2001 ...**Regression****analysis**is primarily used for two conceptually distinct purposes. First,**regression****analysis**is widely used for ... Applied**Regression****Analysis**(3rd ed.). John Wiley. ISBN 978-0-471-17082-2. Fox, J. (1997). Applied**Regression****Analysis**, Linear ...**Analysis**of Variance," pp. 541-554. Lindley, D.V. (1987). "**Regression**and correlation**analysis**," New Palgrave: A Dictionary of ...###### Outline of **regression** **analysis**

...

**regression**Isotonic**regression**Semiparametric**regression**Local**regression**Total least squares**regression**Deming**regression**... The following outline is provided as an overview of and topical guide to**regression****analysis**:**Regression****analysis**- use of ... Simple linear**regression**Trend estimation Ridge**regression**Polynomial**regression**Segmented**regression**Nonlinear**regression**...**Regression****analysis**Linear**regression**Least squares Linear least squares (mathematics) Non-linear least squares Least absolute ...###### Functional **regression**

... is a version of

**regression****analysis**when responses or covariates include functional data. Functional ... Yao, Müller and Wang (2005). "Functional linear**regression****analysis**for longitudinal data". The Annals of Statistics. 33 (6): ... Functional data**analysis**Functional principal component**analysis**Karhunen-Loève theorem Generalized functional linear model ... analogous to extending linear**regression**to polynomial**regression**. For a scalar response Y {\displaystyle Y} and a functional ...###### Kernel **regression**

Watson, G. S. (1964). "Smooth

**regression****analysis**". Sankhyā: The Indian Journal of Statistics, Series A. 26 (4): 359-372. JSTOR ... ISBN 0-387-94716-7. Scale-adaptive kernel**regression**(with Matlab software). Tutorial of Kernel**regression**using spreadsheet ( ... Stata: npregress, kernreg2 Kernel smoother Local**regression**Nadaraya, E. A. (1964). "On Estimating**Regression**". Theory of ... with Microsoft Excel). An online kernel**regression**demonstration Requires .NET 3.0 or later. Kernel**regression**with automatic ...###### Principal component **regression**

Principal component

**analysis**Partial least squares**regression**Ridge**regression**Canonical correlation Deming**regression**Total ... In statistics, principal component**regression**(PCR) is a**regression****analysis**technique that is based on principal component ... using ordinary least squares**regression**(linear**regression**) to get a vector of estimated**regression**coefficients (with ... The**regression**function is then assumed to be a linear combination of these feature elements. Thus, the underlying**regression**...###### Multinomial logistic **regression**

Logistic

**regression**Multinomial probit Greene, William H. (2012). Econometric**Analysis**(Seventh ed.). Boston: Pearson Education ... doi:10.1111/j.1467-9574.1988.tb01238.x. Menard, Scott (2002). Applied Logistic**Regression****Analysis**. SAGE. p. 91. ISBN ... The formulation of binary logistic**regression**as a log-linear model can be directly extended to multi-way**regression**. That is, ... The article on logistic**regression**presents a number of equivalent formulations of simple logistic**regression**, and many of ...###### Deming **regression**

Koopmans, T. C. (1936). Linear

**regression****analysis**of economic time series. DeErven F. Bohn, Haarlem, Netherlands. Kummell, C. ... The Deming**regression**is only slightly more difficult to compute than the simple linear**regression**. Most statistical software ... Deming**regression**becomes orthogonal**regression**: it minimizes the sum of squared perpendicular distances from the data points ... In statistics, Deming**regression**, named after W. Edwards Deming, is an errors-in-variables model which tries to find the line ...###### Linear **regression**

...

**Analysis**: Theory and Computing, World Scientific, pp. 1-2, ISBN 9789812834119,**Regression****analysis**... is ... Logistic**regression**and probit**regression**for binary data. Multinomial logistic**regression**and multinomial probit**regression**... which is the domain of multivariate**analysis**. Linear**regression**was the first type of**regression****analysis**to be studied ... Mathematics portal**Analysis**of variance Blinder-Oaxaca decomposition Censored**regression**model Cross-sectional**regression**Curve ...**Regression** dilution

Applied

**Regression****Analysis**(3rd ed.). John Wiley. p. 19. ISBN 0-471-17082-8. Riggs, D. S.; Guarnieri, J. A.; et al. (1978). " ...**Regression**dilution, also known as**regression**attenuation, is the biasing of the linear**regression**slope towards zero (the ... Standard methods can fit a**regression**of y on w without bias. There is bias only if we then use the**regression**of y on w as an ... Recall that linear**regression**is not symmetric: the line of best fit for predicting y from x (the usual linear**regression**) is ...###### Poisson **regression**

In statistics, Poisson

**regression**is a generalized linear model form of**regression****analysis**used to model count data and ... Poisson**regression**creates proportional hazards models, one class of survival**analysis**: see proportional hazards models for ... Cameron, A. C.; Trivedi, P. K. (1998).**Regression****analysis**of count data. Cambridge University Press. ISBN 978-0-521-63201-0. ... Negative binomial**regression**is a popular generalization of Poisson**regression**because it loosens the highly restrictive ...###### Partial **regression** plot

Applied

**Regression****Analysis**(3rd ed.). John Wiley. ISBN 0-471-17082-8. Cook and Weisberg (1982). Residuals and Influence in ... Partial**regression**plots are related to, but distinct from, partial residual plots. Partial**regression**plots are most commonly ... On the other hand, for the partial**regression**plot, the x-axis is not Xi. This limits its usefulness in determining the need ... Partial**regression**plots are also referred to as added variable plots, adjusted variable plots, and individual coefficient ...###### DeFries-Fulker **regression**

...

**analysis**is based on the differences in the magnitude of**regression**to the mean in a genetic trait ... also sometimes called DeFries-Fulker extremes**analysis**, is a type of multiple**regression****analysis**designed for estimating the ... "DeFries-Fulker multiple**regression****analysis**". Erik Willcutt website. University of Colorado Boulder. Retrieved 2018-06-20. ... Lazzeroni, Laura C.; Ray, Amrita (2012-12-20). "A Generalized Defries-Fulker**Regression**Framework for the**Analysis**of Twin Data ...###### Factor **regression** model

The factor

**regression**model can be viewed as a combination of factor**analysis**model ( y n = A x n + c + e n {\displaystyle \ ... Within statistical factor**analysis**, the factor**regression**model, or hybrid factor model, is a special multivariate model with ... B {\displaystyle \mathbf {B} } is the (unknown)**regression**coefficients of the design factors. c {\displaystyle \mathbf {c} } ... Open source software to perform factor**regression**is available. Carvalho, Carlos M. (1 December 2008). "High-Dimensional Sparse ...###### Ridge **regression**

Hoerl, Arthur E. (1962). "Application of Ridge

**Analysis**to**Regression**Problems". Chemical Engineering Progress. 58 (3): 54-59. ... Ridge**regression**is a method of estimating the coefficients of multiple-**regression**models in scenarios where the independent ... Ridge**regression**was developed as a possible solution to the imprecision of least square estimators when linear**regression**... L2 regularization is used in many contexts aside from linear**regression**, such as classification with logistic**regression**or ...###### Cross-sectional **regression**

Linear

**regression****Regression****analysis**Andrews, D. W. K. (2005). "Cross-Section**Regression**with Common Shocks" (PDF). ... This type of cross-sectional**analysis**is in contrast to a time-series**regression**or longitudinal**regression**in which the ...**Regression****analysis**, Cross-sectional**analysis**, All stub articles, Statistics stubs). ... doi:10.1111/j.1468-0262.2005.00629.x. Preprint Wooldridge, Jeffrey M. (2009). "Part 1:**Regression****Analysis**with Cross Sectional ...###### Logistic **regression**

In

**regression****analysis**, logistic**regression**(or logit**regression**) is estimating the parameters of a logistic model (the ... Like other forms of**regression****analysis**, logistic**regression**makes use of one or more predictor variables that may be either ... Linear**regression**and logistic**regression**have many similarities. For example, in simple linear**regression**, a set of K data ... To do so, they will want to examine the**regression**coefficients. In linear**regression**, the**regression**coefficients represent ...###### Software **regression**

"Leveraging field data for impact

**analysis**and**regression**testing". ACM SIGSOFT Software Engineering Notes. 28 (5): 128-137. doi ... In order to avoid**regressions**being seen by the end-user after release, developers regularly run**regression**tests after changes ... These tests can include unit tests to catch local**regressions**as well as integration tests to catch remote**regressions**. ... "Configuration selection using code change impact**analysis**for**regression**testing". Proceedings of the International Conference ...###### Ecological **regression**

Kousser, J. Morgan (1973). "Ecological

**Regression**and the**Analysis**of past Politics" (PDF). Journal of Interdisciplinary ... Ecological**regression**is a statistical technique which runs**regression**on aggregates, often used in political science and ... then running a linear**regression**of dependent variable D against independent variable C will give D = a + bC. If the**regression**... Brown, Philip J.; Payne, Clive D. (1986). "Aggregate Data, Ecological**Regression**, and Voting Transitions". Journal of the ...###### Truncated **regression** model

2-4. ISBN 0-8039-5710-6. Amemiya, T. (1973). "

**Regression****Analysis**When the Dependent Variable is Truncated Normal". ... Censored**regression**model Sampling bias Truncated distribution Breen, Richard (1996).**Regression**Models : Censored, Samples ... Breen, Richard (1996). "Sample-Selection Models and the Truncated**Regression**Model".**Regression**Models : Censored, Samples ... Truncated**regression**models are a class of models in which the sample has been truncated for certain ranges of the dependent ...###### Nonhomogeneous Gaussian **regression**

Non-homogeneous Gaussian

**regression**(NGR) is a type of statistical**regression****analysis**used in the atmospheric sciences as a ... Articles with short description, Short description matches Wikidata,**Regression****analysis**). ... It achieves this by generalising the simple linear**regression**model to either: y t ∼ N ( α + β m t , σ = γ + δ s t ) {\ ... The original name 'spread**regression**' has now fallen from use, EMOS is used to refer generally to any method used for the ...###### Haseman-Elston **regression**

**Regression**

**analysis**, Statistical genetics, Genetic linkage

**analysis**, All stub articles, Genetics stubs). ...

**regression**is a form of statistical

**regression**originally proposed for linkage

**analysis**of quantitative traits for sibling ... Although HE

**regression**"...seems a rusty weapon in the genomics

**analysis**armory of the GWAS era. This is because the HE ... Wang, Tao; Elston, Robert C. (July 2005). "Two-level Haseman-Elston

**regression**for general pedigree data

**analysis**". Genetic ...

###### Binary **regression**

In statistics, specifically

**regression****analysis**, a binary**regression**estimates a relationship between one or more explanatory ... as in linear**regression**. Binary**regression**is usually analyzed as a special case of binomial**regression**, with a single outcome ... logistic**regression**) and the probit model (probit**regression**). Binary**regression**is principally applied either for prediction ( ... In economics, binary**regressions**are used to model binary choice. Binary**regression**models can be interpreted as latent ...###### Nonlinear **regression**

In statistics, nonlinear

**regression**is a form of**regression****analysis**in which observational data are modeled by a function ... Segmented**regression**with confidence**analysis**may yield the result that the dependent or response variable (say Y) behaves ... The nonlinear**regression**statistics are computed and used as in linear**regression**statistics, but using J in place of X in the ... Articles with short description, Short description matches Wikidata,**Regression****analysis**). ...**Regression** discontinuity design

... or kinked

**regression**) can also mean a type of segmented**regression**, which is a different type of**analysis**. Final Considerations ...**Regression**-Discontinuity**Analysis**at Research Methods Knowledge Base (Articles with short description, Short description ... Quasi-experiment Design of quasi-experiments Thistlethwaite, D.; Campbell, D. (1960). "**Regression**-Discontinuity**Analysis**: An ... In contrast to the sharp**regression**discontinuity design, a fuzzy**regression**discontinuity design (FRDD) does not require a ...###### Stepwise **regression**

Draper, N. and Smith, H. (1981) Applied

**Regression****Analysis**, 2d Edition, New York: John Wiley & Sons, Inc. SAS Institute Inc. ( ... Freedman's paradox Logistic**regression**Least-angle**regression**Occam's razor**Regression**validation Lasso (statistics) Efroymson, ... The procedure is used primarily in**regression****analysis**, though the basic approach is applicable in many forms of model ... doi:10.1093/biomet/81.3.425 Mark, Jonathan, & Goldberg, Michael A. (2001). Multiple**regression****analysis**and mass assessment: A ...###### Local **regression**

Cleveland, William S.; Devlin, Susan J. (1988). "Locally-Weighted

**Regression**: An Approach to**Regression****Analysis**by Local ... Local**regression**or local polynomial**regression**, also known as moving**regression**, is a generalization of the moving average and ... and Survival**Analysis**. Springer. ISBN 978-3-319-19425-7. Local**Regression**and Election Modeling Smoothing by Local**Regression**: ... This can make it difficult to transfer the results of an**analysis**to other people. In order to transfer the**regression**function ...###### Least-angle **regression**

High-dimensional statistics Lasso (statistics)

**Regression****analysis**Model selection Efron, Bradley; Hastie, Trevor; Johnstone, ... In statistics, least-angle**regression**(LARS) is an algorithm for fitting linear**regression**models to high-dimensional data, ... The basic steps of the Least-angle**regression**algorithm are: Start with all coefficients β {\displaystyle \beta } equal to zero ... "A simple explanation of the Lasso and Least Angle**Regression**". (Wikipedia articles that are too technical from April 2018, All ...###### Fama-MacBeth **regression**

Capital asset pricing model Standard errors in

**regression****analysis**IHS EViews (2014). "Fama-MacBeth Two-Step**Regression**" (PDF ... The Fama-MacBeth**regression**is a method used to estimate parameters for asset pricing models such as the capital asset pricing ... This means Fama MacBeth**regressions**may be inappropriate to use in many corporate finance settings where project holding ... "EconTerms - Glossary of Economic Research "Fama-MacBeth**Regression**"". Archived from the original on 28 September 2007. ...###### Meta-**regression**

... is defined to be a meta-

**analysis**that uses**regression****analysis**to combine, compare, and synthesize research ... A meta-**regression****analysis**aims to reconcile conflicting studies or corroborate consistent ones; a meta-**regression****analysis**is ... Meta-**analysis**(and meta-**regression**) is often placed at the top of the evidence hierarchy provided that the**analysis**consists of ... A meta-**analysis**with some or all studies having more than two arms is called network meta-**analysis**, indirect meta-**analysis**, or ...###### Quantile **regression**

... is a type of

**regression****analysis**used in statistics and econometrics. Whereas the method of least squares ... Quantile**regression**is an extension of linear**regression**used when the conditions of linear**regression**are not met. One ... Beyond simple linear**regression**, there are several machine learning methods that can be extended to quantile**regression**. A ... "qrnn: Quantile**Regression**Neural Networks". R Project. 2018-06-26. "qgam: Smooth Additive Quantile**Regression**Models". R ...###### James Durbin

His last book, Time Series

**Analysis**by State Space Methods, was published by Oxford University Press in May 2012. His last ... Durbin, J.; Watson, G. S. (1950). "Testing for Serial Correlation in Least Squares**Regression**: I". Biometrika. Biometrika Trust ... Durbin, J. (2012). Time Series**Analysis**by State Space Methods. Oxford Statistical Science Series. Oxford University Press. ... known particularly for his work on time series**analysis**and serial correlation. The son of a greengrocer, Durbin was born in ...###### Uncertainty quantification

In

**regression****analysis**and least squares problems, the standard error of parameter estimates is readily available, which can be ... "Modularization in Bayesian**analysis**, with emphasis on**analysis**of computer models". Bayesian**Analysis**. Institute of ... The targets of uncertainty propagation**analysis**can be: To evaluate low-order moments of the outputs, i.e. mean and variance. ... The probabilistic approach is considered as the most rigorous approach to uncertainty**analysis**in engineering design due to its ...###### Alexander Kluge

"Öffentlichkeit und Erfahrung" has been translated into English as Public Sphere and Experience: Toward an

**Analysis**of the ... Plass, Ulrich (Winter 2009). "Dialectic of**Regression**: Theador W Adorno and Fritz Lang". Telos. 149: 142. "Berlinale 1965: ...**Analysis**of the Bourgeois and Proletarian Public, trans. Peter Labany, Jamie Owen Daniel, and Assenka Oksiloff (Verso Books, ...###### Length of stay

Within

**regression**approaches, linear, log-normal and logistic**regression**approaches have been applied, but have been criticised ... Various**analyses**have sought to model length of stay in different condition contexts. This has usually been done with ... A variation in the calculation of ALOS can be to consider only length of stay during the period under**analysis**. Length of stay ... Carter & Potts (2014) instead recommend use of negative binomial**regression**. Length of stay is commonly used as a quality ...###### Mass shootings in the United States

Partially adjusted

**regression****analyses**produced similar results, as did**analyses**restricted to domestic and non-domestic mass ... In the October 2018 PLOS One study, the Bayesian zero-inflated Poisson**regression**model that included state-level SMI rates as ... In 2018, the FBI Behavioral**Analysis**Unit released a survey of 63 active shooter cases between 2000 and 2013 that found that ... However, the researchers also used a Poisson**regression**model to test if the frequency of online media coverage density and ...###### Calculation of glass properties

In the form of statistical

**analysis**glass modeling can aid with accreditation of new data, experimental procedures, and ... linear**regression**can be applied using common polynomial functions up to the third degree. Below is an example equation of the ... Glass properties and glass behavior during production can be calculated through statistical**analysis**of glass databases such as ... C using statistical**analysis**". Glass Technol. 40 (5): 149-53. Priven A.I. (December 2004). "General Method for Calculating the ...###### Bodhisattva

Over time, a more varied

**analysis**of bodhisattva careers developed focused on one's motivation. This can be seen in the Tibetan ... non-**regression**), (4) Ekajātipratibaddha ("separated by only one lifetime from buddhahood"). Drewes notes that Mahāyāna sūtras ...###### Homoscedasticity and heteroscedasticity

The existence of heteroscedasticity is a major concern in

**regression****analysis**and the**analysis**of variance, as it invalidates ... Thus,**regression****analysis**using heteroscedastic data will still provide an unbiased estimate for the relationship between the ... The econometrician Robert Engle was awarded the 2003 Nobel Memorial Prize for Economics for his studies on**regression****analysis**... Fox, J. (1997). Applied**Regression****Analysis**, Linear Models, and Related Methods. California: Sage Publications. p. 306. (Cited ...###### Total operating characteristic

doi:10.1111/j.1466-8238.2007.00358.x. Fawcett, Tom (2006); An introduction to ROC

**analysis**, Pattern Recognition Letters, 27, ... Dodd, Lori E.; Pepe, Margaret S. (2003). "Partial AUC Estimation and**Regression**". Biometrics. 59 (3): 614-623. doi:10.1111/1541 ... especially for spatially explicit**analyses**. Some features of the AUC that draw criticism include the fact that 1) AUC ignores ... "A suite of tools for ROC**analysis**of spatial models". ISPRS International Journal of Geo-Information. 2 (3): 869-887. doi: ...###### Jtest

Automated testing List of unit testing frameworks List of tools for static code

**analysis****Regression**testing Software testing ... The product includes technology for Data-flow**analysis**, Unit test-case generation and execution, static**analysis**, and more. ... Jtest is an automated Java software testing and static**analysis**product developed by Parasoft. ... Static program**analysis**tools, Unit testing, Unit testing frameworks). ...###### Jana Jurečková

... of Adaptive

**Regression**(with Yadolah Dodge, Springer, 2000), of Robust Statistical Methods with R (with Jan Picek, Chapman & ... Nonparametrics and Robustness in Modern Statistical Inference and Time Series**Analysis**: A Festschrift in honor of Professor ...###### Daniel J. Bauer

He is known for rigorous methodological work on latent variable models and is a proponent of integrative data

**analysis**, a meta- ... doi:10.1207/s15328007sem1204_1 Bauer, D.J. & Curran, P.J. (2005). Probing interactions in fixed and multilevel**regression**: ... Patrick J. Curran Integrative Data**Analysis**and Big Data, National Cancer Institute University teaching awards Conference ... Bauer has published widely in factor**analysis**, multilevel modeling, latent growth curves, mixture models, latent class models, ...###### Virginia A. Clark

Clark is the coauthor of: Applied Statistics:

**Analysis**of Variance and**Regression**(with Olive Jean Dunn, 1974; 3rd ed. with ... with Clark, 2009) Practical Multivariate**Analysis**(with Abdelmomem Afifi and Susanne May, 5th ed., 2012) Clark became a Fellow ... Computer-Aided Multivariate**Analysis**(with Abdelmomem Afifi, 1984; 4th ed. with Susanne May, 2004) Processing Data: The Survey ... Reviews of Computer-Aided Multivariate**Analysis**: Fang, Kai-tai, zbMATH, Zbl 0888.62050{{citation}}: CS1 maint: untitled ...###### Healthgrades

Its

**analysis**is based on approximately 40 million Medicare discharges for the most recent three-year time period available. ... Specifically, most ratings are determined from multivariate logistic**regressions**of medical outcomes at a given healthcare ... Rachel Brand (October 16, 2004). "**Analysis**gives Health Grades flunking marks". Rocky Mountain News. Archived from the original ...###### Democratic backsliding

Therefore, Welzel sees the current autocratization trend as

**regression**to the mean, but expects that it too will reverse in ... A 2018**analysis**by political scientists Yascha Mounk and Jordan Kyle links populism to democratic backsliding, showing that ... Diamond, Larry (15 September 2020). "Democratic**regression**in comparative perspective: scope, methods, and causes". ... democratic**regression**, and democratic deconsolidation. Skaaning, Svend-Erik (2020). "Waves of autocratization and ...###### Person-environment fit

Edwards, J.R. (2002). "Alternatives to difference scores: Polynomial

**regression****analysis**and response surface methodology". In ... The polynomial**regression**equation commonly used in person-environment fit research is as follows: Z = β 0 + β 1 E + β 2 P + β ... Polynomial**regression**involves using measures of the person and environment along with relevant higher-order terms (e.g., the ... Studies using polynomial**regression**have found that the restrictive assumptions underlying difference scores are usually ...###### Learning to rank

... using polynomial

**regression**) had been published by him three years earlier. Bill Cooper proposed logistic**regression**for the ... sentiment**analysis**, and online advertising. A possible architecture of a machine-learned search engine is shown in the ... Ordinal**regression**and classification algorithms can also be used in pointwise approach when they are used to predict the score ... Then the learning-to-rank problem can be approximated by a**regression**problem - given a single query-document pair, predict its ...###### Complementarity plot

A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and

**regressions**. ... "SARAMA: a bioinformatics tool for Structure validation , Protein structure**analysis**". omictools. Group, Professor Emil Alexov ...###### Comparison of Gaussian process software

Leith, D. J.; Zhang, Yunong; Leithead, W. E. (2005). "Time-series Gaussian Process

**Regression**Based on Toeplitz Computation of ... This is a comparison of statistical**analysis**software that allows doing inference with Gaussian processes often using ... Roustant, Olivier; Ginsbourger, David; Deville, Yves (2012). "DiceKriging, DiceOptim: Two R Packages for the**Analysis**of ... Data**Analysis**. 153: 107081. arXiv:1906.07828. doi:10.1016/j.csda.2020.107081. ISSN 0167-9473. S2CID 195068888. Retrieved 1 ...###### Multimedia information retrieval

Support vector

**regression**, support vector machine, linear discriminant**analysis**) Density-based Methods (Bayes nets, Markov ... Frequently used methods for description filtering include factor**analysis**(e.g. by PCA), singular value decomposition (e.g. as ... The list of applicable classifiers includes the following: Metric approaches (Cluster**analysis**, vector space model, Minkowski ... MMIR methods are, therefore, usually reused from other areas such as: Bioinformation**analysis**Biosignal processing Content- ...###### Rescaled range

Matlab code for computing R/S, DFA, periodogram

**regression**and wavelet estimates of the Hurst exponent and their corresponding ... Kamenshchikov, S. (2014). "Transport Catastrophe**Analysis**as an Alternative to a Monofractal Description: Theory and ... Many physical phenomena that have a long time series suitable for**analysis**exhibit a Hurst exponent greater than 1/2. For ...###### SAT

There are also two cross-test scores that each range from 10 to 40 points:

**Analysis**in History/Social Studies and**Analysis**in ... Domigue, Ben; Briggs, Derek C. (2009). "Using Linear**Regression**and Propensity Score Matching to Estimate the Effect of ... In its**analysis**of the incident, the Princeton Review supported the idea of curving grades, but pointed out that the test was ... An early meta-**analysis**(from 1983) found similar results and noted "the size of the coaching effect estimated from the matched ...###### Exploratory causal **analysis**

The potential outcomes and

**regression****analysis**techniques handle such queries when data is collected using designed experiments ... research in the same way exploratory data**analysis**often precedes statistical hypothesis testing in data**analysis**Data**analysis**... Exploratory causal**analysis**(ECA), also known as data causality or causal discovery is the use of statistical algorithms to ... Causal**analysis**is the field of experimental design and statistics pertaining to establishing cause and effect. ...###### Spontaneous remission

... of all breast cancer cases underwent spontaneous

**regression**. Everson and Cole offered as explanation for spontaneous**regression**... in a meta-**analysis**, investigated about 1000 cases Turner, in a qualitative research study, conducted interviews with 20 ... The spontaneous**regression**and remission from cancer was defined by Everson and Cole in their 1966 book as "the partial or ... For one, not all cases of spontaneous**regression**can be apprehended, either because the case was not well documented or the ...###### Internet of things

According to an FTC

**analysis**, the existing framework, consisting of the FTC Act, the Fair Credit Reporting Act, and the ... A wide variety of machine learning techniques have been used in IoT domain ranging from traditional methods such as**regression**... The connectivity enables health practitioners to capture patient's data and applying complex algorithms in health data**analysis**... data collection and**analysis**for research, and monitoring. The IoMT has been referenced as "Smart Healthcare", as the ...###### Olive oil acidity

... such as Principal Component

**Analysis**(PCA) or Partial Least Squares**regression**(PLS), to estimate the oil acidity. The ... Free acidity is a defect of olive oil that is tasteless and odorless, thus can not be detected by sensory**analysis**. Since ... The main advantage of NIR spectroscopy is the possibility to carry out the**analysis**on raw olive oil samples, without any ... Many commercial spectrophotometers exist that can be used for**analysis**of different quality parameters in olive oil. ...###### Disparate impact

... but

**regression****analysis**allows us to account for variables that may explain differences. For example, suppose that we are ... Jurimetrics Office of Fair Housing and Equal Opportunity**Regression****analysis**Simpson's paradox#UC Berkeley gender bias EEOC v. ... Then we may construct a multiple**regression**model for pay y {\displaystyle y} as: y = β 0 ⏟ Intercept + ∑ i = 1 p β i x i ⏟ ... After correction for the potentially confounding variables in a**regression**model, we should be able to tell if there is still ...###### Moon landing conspiracy theories in popular culture

The 2012 documentary Room 237 featured film

**analysis**by fans of Stanley Kubrick's 1980 film The Shining, connecting Kubrick's ... "**Regression**and Debasement of Science - on the Apollo Moon Landings". Archived from the original on April 17, 2015. Retrieved ...###### Abstinence pledge

Rosenbaum's study was innovative for using Rubin causal model matching, instead of relying on

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...

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###### Simple Regression1

- Methods of simple regression and linear regressions were clearly explained in the works of Waner S who also brought up the Regression Calculator. (supremeessays.com)

###### Coefficients8

- Most least squares regression programs are designed to fit models that are linear in the coefficients. (statgraphics.com)
- large sample properties did not hold for variance estimates from the proportional hazards model, and the Z statistics used to test the significance of the regression coefficients lost validity under the null hypothesis. (nih.gov)
- A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. (ametsoc.org)
- However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. (ametsoc.org)
- It illustrates how regression coefficients are estimated, interpreted, and used in a variety of settings within the social sciences, business, law, and public policy. (sagepub.com)
- Packed with applied examples and using few equations, the book walks readers through elementary material using a verbal, intuitive interpretation of regression coefficients, associated statistics, and hypothesis tests. (sagepub.com)
- Because of this endogeneity, significant correlation can exist between the unobserved factors contributing to both the endogenous independent variable and the dependent variable, which results in biased estimators (incorrect regression coefficients) ( 2 ). (cdc.gov)
- There was found a good correlation between the measured data and the model results with regression coefficients of 0.9. (who.int)

###### Multivariate logistic regression5

- Multivariate logistic regression analysis on the association between anthropometric indicators of under-five children in Nigeria: NDHS 2018. (bvsalud.org)
- Multivariate logistic regression model was used to determine the association between stunting , underweight and wasting given that of the estimated effect of other determinants. (bvsalud.org)
- Next, we conducted a sex-specific analysis for obesity and its associated factors using backward elimination multivariate logistic regression models. (cdc.gov)
- In a multivariate logistic regression analysis, the type of AMI was classified based on electrocardiography findings [odds ratio 5.18, 95% confidence interval: 1.69-15.91, P=0.004] and was independently associated with a long prehospital delay time, indicating that patients with ST segment elevation MI would seek early medical care. (who.int)
- The association between childhood sexual violence and several potential demographic and social risk factors was explored through bivariate and multivariate logistic regression. (bvsalud.org)

###### Variance5

- The course covers non-experimental research design, simple linear regression, multiple regression, analysis of variance, non-linear functional forms, heteroskedasticity, complex survey designs, and real-world statistical applications in nutrition science and policy. (tufts.edu)
- The data are preprocessed by subtracting off a linear regression fit, followed by normalization of all features to unit variance. (mathworks.com)
- The quadratic regression resulted in variance explanations of greater magnitude when compared to the linear model. (bvsalud.org)
- In addition you also should understand that extreme values already have more weight with variance-based analysis methods (i.e. regression analysis, Anova, factor analysis, etc.) since since distances are computed as squares. (unige.ch)
- The project team used the Analysis of Variance (ANOVA) tools in Minitab's Assistant menu to investigate the relationship between revenue and the roles of different sales team members. (minitab.com)

###### Proportional hazards regression analyses2

- Below this value for EPV, the results of proportional hazards regression analyses should be interpreted with caution because the statistical model may not be valid. (nih.gov)
- Methods The authors conducted Cox proportional hazards regression analyses of lung cancer risk with cumulative, mean and maximum 'daily weighted average' (DWA) exposure among 5436 workers, using age-based risk sets. (bmj.com)

###### Predictors7

- The user may include all predictor variables in the fit or ask the program to use a stepwise regression to select a subset containing only significant predictors. (statgraphics.com)
- If the number of predictors is not excessive, it is possible to fit regression models involving all combinations of 1 predictor, 2 predictors, 3 predictors, etc, and sort the models according to a goodness-of fit statistic. (statgraphics.com)
- Partial Least Squares is designed to construct a statistical model relating multiple independent variables X to multiple dependent variables Y. The procedure is most helpful when there are many predictors and the primary goal of the analysis is prediction of the response variables. (statgraphics.com)
- The innovative approach of Bayesian additive regression trees (BART) is an elegant way to overcome these limitations because it does not specify a parametric form for the relationship between the outcome and its predictors. (diw.de)
- We present a BART event history analysis that allows identifying predictors for different types of nonresponse to anticipate response rates for upcoming survey waves. (diw.de)
- You will also be asked to interpret regression output to understand overall model performance and importance of different predictors, as well as make predictions using the appropriate regression model. (coursera.org)
- The practice of choosing predictors for a regression model, called model building , is an area of real craft. (theanalysisfactor.com)

###### Univariate6

- We learn how to do univariate analysis and bivariate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation, and correlation. (tutorialspoint.com)
- In Episode Two of this series, Prof. Røislien will talk about two types of regression analysis - univariate regression analysis and multivariable regression analysis. (editage.com)
- Topic: Know thy data (Episode 2) - Looking at univariate and multivariable regression analyses. (editage.com)
- Univariate meta-regression showed an association between retinopathy and average HbA1c reduction during the overall follow-up (slope=0.77, p=0.007), but no relationship for SBP or weight. (figshare.com)
- Univariate and multivariate logistic regressions were performed to identify variables associated with postoperative neurological deficits and a DWI signal. (thejns.org)
- Univariate and multiple linear regression analyses were used to answer the three research questions. (umd.edu)

###### Solved using the linear regr1

- Identify the business problem which can be solved using the linear regression technique of Machine Learning. (tutorialspoint.com)

###### Residuals5

- 1.) D. Linear regression is all about the residuals. (talkstats.com)
- The module also introduces the notion of errors, residuals and R-square in a regression model. (coursera.org)
- Errors, Residuals and R-square WEEK 2 Module 2: Regression Analysis: Hypothesis Testing and Goodness of Fit This module presents different hypothesis tests you could do using the Regression output. (coursera.org)
- The distinction is most important in regression analysis , where the concepts are sometimes called the regression errors and regression residuals and where they lead to the concept of studentized residuals . (wikipedia.org)
- In other words: regression analysis tries to find a line that will maximize prediction and minimize residuals. (unige.ch)

###### Correlation6

- If you find yourself wondering if there is a correlation or relationship between variables, then regression analysis might be worth exploring. (ecapitaladvisors.com)
- Additionally, the correlation between the dependent variables can create significant multicollinearity, which violates the assumptions of standard regression models and results in inefficient estimators. (cdc.gov)
- The "red" student who uses the computer for very long hours will lead to a positive correlation and positive regression rate, whereas the "black" ones alone in the data suggest an inexistent correlation. (unige.ch)
- Correlation and regression analysis was performed. (nel.edu)
- A total of 99 candidate CNVs were identified using Illumina BovineSNP50 array data, and association tests for each production trait were performed using a linear regression analysis with PCA correlation. (usda.gov)
- Pearson correlation test and stepwise regression analysis were used to analyze. (who.int)

###### Multivariable logistic regression2

- Multivariable logistic regression was undertaken to examine associations between exposure to Cannabis dust (classified as low, medium, and high) and health symptoms. (cdc.gov)
- Multivariable logistic regression models were used to identify socioeconomic, psychosocial, and treatment factors associated with =5% weight gain over 2-year follow-up. (cdc.gov)

###### Mortality4

- We used joinpoint regression analysis to estimate the slope of mortality trends. (cdc.gov)
- Undergoing elective surgery on the weekend was associated with a 1.96 times higher odds of 30-day mortality than weekday surgery (95% confidence interval, 1.36-2.84) in a propensity-matched analysis. (ices.on.ca)
- According to a recent large cohort analysis presented at the American College of Chest Physicians 2022 Annual Meeting, an association was found between exposure to probiotics in the ICU and a measurable increase in bacteremia and bacteremia-related mortality. (medscape.com)
- Charvat H, Belot A. mexhaz: an R package for fitting flexible hazard-based regression models for overall and excess mortality with a random effect. (who.int)

###### Data53

- GeoDa is a free software package that conducts spatial data analysis, geovisualization, spatial autocorrelation and spatial modeling. (linuxlinks.com)
- It's designed to offer new insights from data analysis by exploring and modeling spatial patterns. (linuxlinks.com)
- GeoDa has powerful capabilities to perform spatial analysis, multivariate exploratory data analysis, and global and local spatial autocorrelation. (linuxlinks.com)
- The authors combine theory and practice to make sophisticated methods of analysis accessible to researchers and practitioners working with widely different types of data and software in areas such as applied statistics, econometrics, marketing, operations research, actuarial studies, demography, biostatistics and quantitative social sciences. (repec.org)
- The new material includes new theoretical topics, an updated and expanded treatment of cross-section models, coverage of bootstrap-based and simulation-based inference, expanded treatment of time series, multivariate and panel data, expanded treatment of endogenous regressors, coverage of quantile count regression, and a new chapter on Bayesian methods. (repec.org)
- Regression Analysis of Count Data ," Cambridge Books , Cambridge University Press, number 9781107014169. (repec.org)
- This repository includes data for snap analyses of the 2018 Midterm Elections using unofficial election returns data. (github.com)
- As a formal matter, conventional regression analysis does nothing more than produce from a data set a collection of conditional means and conditional variances. (ebooks.com)
- Regression is most useful for data reduction, leading to relatively simple but rich and precise descriptions of patterns in a data set. (ebooks.com)
- Based on a special data analysis methodology developed for non-direct multivariate experiments, we present the expected accuracies of the KASCADE experiment on the elemental composition and primary energy estimation. (harvard.edu)
- In this short course we will provide an introduction to linear regression and how to utilize it in R. We will cover the theory of linear regression as well as demonstrating how to use R to make and interpret test statistics and data plots for a built in R data set. (colorado.edu)
- We will have time at the end of the course to answer any specific questions about linear regressions for participants' data sets. (colorado.edu)
- Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. (tutorialspoint.com)
- In this section, you will learn what actions you need to take step by step to get the data and then prepare it for the analysis these steps are very important. (tutorialspoint.com)
- Quantile regression was used to analyze the data. (elsevier.com)
- The course provides learners with exposure to essential tools including exploratory data analysis, as well as regression methods that can be used to investigate the impact of marketing activity on aggregate data (e.g., sales) and on individual-level choice data (e.g., brand choices). (coursera.org)
- In this module, you will be asked to determine the appropriate type of regression for different types of marketing data and will perform regression analysis to assess the impact of marketing actions on outcomes of interest, such as sales, traffic, and brand choices. (coursera.org)
- We applied regression analysis to time-series data on vehicles, population and traffic fatalities in the United Arab Emirates (UAE), Jordan and Qatar. (who.int)
- Design Interrupted time-series analysis of repeated cross-sectional time-series data. (bmj.com)
- In this retrospective analysis, we used data from eight randomised clinical trials with metastatic castration-resistant prostate cancer to estimate the growth (g) and regression (d) rates of disease burden over time. (urotoday.com)
- A simulated sample size analysis, in which g was used as the endpoint, compared docetaxel data with mitoxantrone data and showed that small sample sizes were sufficient to achieve 80% power (16, 47, and 25 patients, respectively, in the three docetaxel comparator groups). (urotoday.com)
- The application of mathematical models to existing clinical data allowed estimation of rates of growth and regression that provided new insights in metastatic castration-resistant prostate cancer. (urotoday.com)
- As was discussed earlier, regression analysis requires that your data meet specific assumptions. (psu.edu)
- Often, researchers get confused between different methods of data analysis and how they can go about using them. (editage.com)
- The selected model fits the data well except for very few discrepant or outlying data values, which may have greatly influenced the choice of the regression line. (talkstats.com)
- Regression analysis is useful in statistics as it allows us to identify trends in the data. (ecapitaladvisors.com)
- In my example of regression analysis basics, I will be using Excel and Tableau to briefly walk you through how easy it is to perform linear regression on a sample data set that has recorded the average snowfall from 2004 to 2017, as well as how you can use linear regression to predict the average snowfall for future years. (ecapitaladvisors.com)
- Essentially, regression is your "best guess" at predicting what might happen in the future by using the data available to you. (ecapitaladvisors.com)
- The purpose of this page is to show how to use various data analysis commands. (ucla.edu)
- In particular, it does not cover data cleaning and checking, verification of assumptions, model diagnostics and potential follow-up analyses. (ucla.edu)
- For our data analysis below, we are going to expand on Example 3 about applying to graduate school. (ucla.edu)
- Regression is the engine behind a multitude of data analytics applications used for many forms of forecasting and prediction. (coursera.org)
- Note: This course uses the 'Data Analysis' tool box which is standard with the Windows version of Microsoft Excel. (coursera.org)
- At the end of this module, you'll be able to determine what kinds of predictions you can make to create future strategies, understand the most powerful techniques for predictive models including regression analysis, and be prepared to take full advantage of analytics to create effective data-driven business decisions. (coursera.org)
- To accommodate such heterogeneity, we propose a concave fusion approach to identifying the subgroup structures and estimating the treatment effiects for a semiparametric linear regression with censored data. (edu.hk)
- Hierarchical linear models : applications and data analysis methods / Stephen W. Raudenbush, Anthony S. Bryk. (who.int)
- Estudios Clínicos Latino América (ECLA) Foundation (Rosario, Argentina) covered all the costs related to the data collection, statistical analyses and writing of the manuscript. (figshare.com)
- The results evolved from the neural network training were compared with the results of regression model and experimental data. (who.int)
- The output from Neural Network approach had greater consistency with the experimental data than the output from conventional regression analysis. (who.int)
- In addition to their data types, many statistical analysis types only work for given sets of data distributions and relations between variables. (unige.ch)
- In practical terms this means that not only you have to adapt your analysis techniques to types of measures but you also (roughly) should respect other data assumptions. (unige.ch)
- The goal of statistical analysis is quite simple: find structure in the data. (unige.ch)
- Data was collected using employee engagement inventory, psychological empowerment, and commitment to change inventory, and was analysed using descriptive analysis and SEM. (researchgate.net)
- In regression analysis , the coefficient of determination is a measure of goodness-of-fit (i.e. how well or tightly the data fit the estimated model). (statistics.com)
- This course will teach you how multiple linear regression models are derived, the use software to implement them, what assumptions underlie the models, how to test whether your data meet those assumptions and what can be done when those assumptions are not met, and develop strategies for building and understanding useful models. (statistics.com)
- Methods: Structured additive regression models using Bayesian inference based on Markov chain Monte Carlo (MCMC) simulation techniques were fitted using age standardized FBCM rates and county level attributes data obtained from Surveillance Epidemiology & End Results (SEER) program for the years 1990 to 2014. (cdc.gov)
- Statistical Analysis and Data Mining, 7(4): 272-281 (2014). (msu.edu)
- With the data in Minitab Statistical Software, the team used regression analysis to understand the relationship between sales operations and revenue. (minitab.com)
- After their initial analysis revealed that optimal revenue could be achieved by combining a higher transfer rate with a lower turnover in contractors and services, the team began digging deeper into their sales operation data to determine where process changes could be made. (minitab.com)
- Research Design - A retrospective, propensity score-matched cohort analysis of linked population-based health administrative data was carried out. (ices.on.ca)
- We were also unable to replicate some of the data used in the meta-analysis. (bmj.com)
- First-line ovulation induction for polycystic ovary syndrome: an individual participant data meta-analysis. (bmj.com)
- Subsequent analysis of that data has observation that animals in the house can decrease the risk of shown that the significant difference in the response to cat asthma. (cdc.gov)

###### Linear Regression Analysis2

- Linear regression analysis is the statistical confirmation of these logical assumptions. (metastock.com)
- Multiple linear regression analysis is a method for estimating the effects of several factors concurrently. (sagepub.com)

###### Statistical Analyses1

- We used SAS 9.3 for the statistical analyses and to account for the complex sampling design. (cdc.gov)

###### Meta-analyses5

- We performed random-effects meta-analyses and meta-regressions of adjusted relative risk (RR) estimates and formally explored between-study heterogeneity. (bris.ac.uk)
- We performed random-effects meta-analyses and meta-regressions of adjusted relative risk (RR) estimates and formally explored between-study heterogeneity.RESULTS: We included 19 studies on 589,649 participants (2040 incident dementia cases) followed up for up to 42 years. (bris.ac.uk)
- All meta-analyses were carried out using STATA V.8 software. (bmj.com)
- Random-effects meta-analyses examined perinatal outcomes associated with preconception and antenatal ART initiation as well as according to trimesters of antenatal initiation. (medscape.com)
- This systematic review and meta-analyses is registered with PROSPERO, number CRD42021248987. (medscape.com)

###### Multinomial2

- Multinomial logistic regression: This is similar to doing ordered logistic regression, except that it is assumed that there is no order to the categories of the outcome variable (i.e., the categories are nominal). (ucla.edu)
- Multinomial regression assessed the associations between maternal metabolic parameters and offspring's BMI trajectories. (rti.org)

###### Assumptions3

- This idea gets back to one of the assumptions of regression, which is that the errors of prediction are normally distributed (homoscedasticity). (psu.edu)
- OLS regression: This analysis is problematic because the assumptions of OLS are violated when it is used with a non-interval outcome variable. (ucla.edu)
- A review of surveys conducted to date reveals considerable variation in both the choice and use of survey methods and in the assumptions made in the analysis and interpretation of findings. (who.int)

###### Estimate3

- Censored quantile regressions will be used in order to estimate in a more flexible and robust way the effect of covariates on the conditional distribution of duration. (ssrn.com)
- The mathematical technique used to estimate this trend-regression model is the Kaiman filter. (ametsoc.org)
- We will build a regression model and estimate it using Excel. (coursera.org)

###### Methods6

- Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. (repec.org)
- In a two-part series, Prof. Jo Røislien, professor of medical statistics at the Faculty of Health Sciences at the University of Stavanger, will take you through some common methods of statistical analysis. (editage.com)
- Join this webinar to understand how and when you can use both methods of statistical analysis. (editage.com)
- abstract = "INTRODUCTION: We conducted a meta-analysis of the conflicting epidemiologic evidence on the association between midlife body mass index (BMI) and dementia.METHODS: We searched standard databases to identify prospective, population-based studies of dementia risk by midlife underweight, overweight, and obesity. (bris.ac.uk)
- Below is a list of some analysis methods you may have encountered. (ucla.edu)
- Historically, the majority of the clinical screening methods consisted of surveys developed using logistic regression analyses to predict diabetes [ 8 - 13 ]. (hindawi.com)

###### Models17

- The simplest regression models involve a single response variable Y and a single predictor variable X. STATGRAPHICS will fit a variety of functional forms, listing the models in decreasing order of R-squared. (statgraphics.com)
- In STATGRAPHICS, the Regression Model Selection procedure implements such a scheme, selecting the models which give the best values of the adjusted R-Squared or of Mallows' Cp statistic. (statgraphics.com)
- A cross-validation and comparison with logistic regression models with least absolute shrinkage and selection operator penalization underline the advantages of the approach. (diw.de)
- In all models and subgroup analyses for smoking attitudes, an increased rate of decline was observed after 2010 (OR 0.88, CI 0.86 to 0.90). (bmj.com)
- Models were robust to sensitivity analyses. (bmj.com)
- Multicollinearity in Regression Models WEEK 4 Module 4: Regression Analysis: Various Extensions The module extends your understanding of the Linear Regression, introducing techniques such as mean-centering of variables and building confidence bounds for predictions using the Regression model. (coursera.org)
- We also study the transformation of variables in a regression and in that context introduce the log-log and the semi-log regression models. (coursera.org)
- The lecture are very exciting and detailed, though little hard and too straight forward sometimes, but Youtube helped in Regression models. (coursera.org)
- Spline regression models / Lawrence C. Marsh, David R. Cormier. (who.int)
- Considerations regarding the selection of regression models in the academic context and in organizational practice are provided. (bvsalud.org)
- The dataset is relatively small, and the authors use stepwise logistic regression models to detect small differences. (cdc.gov)
- An often overlooked problem in building statistical models is that of endogeneity, a term arising from econometric analysis, in which the value of one independent variable is dependent on the value of other predictor variables. (cdc.gov)
- Because the strain is in part determined by the presence of these toxins, including both strain and genotype in the model means that the standard errors for variables for the Shiga-containing strains and bloody diarrhea symptom are likely to be too high, and hence the significance levels (p values) obtained from the regression models are higher than the true probability because of a type I error. (cdc.gov)
- Mathematical models for the surface area of secondary clarifier were developed for wastewater generated from a dairy industry and from domestic sources, by correlating the parameters namely, surface area per unit flow rate (A/Q), influent concentration (C(O)), underflow concentration (C(U)), recycling ratio (r) and Mean Cell Residence Time (theta C) using multiple regression analysis. (who.int)
- We compared the performance of our models with that of a screening score model based on logistic regression analysis for prediabetes that had been developed previously. (hindawi.com)
- 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. (bvsalud.org)
- 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. (bvsalud.org)

###### Model19

- It will also be a useful foil for conventional texts for the teaching of the regression model. (ebooks.com)
- Regression analysis is used to model the relationship between a response variable and one or more predictor variables. (statgraphics.com)
- आप एक पूर्ण Linear Regression course की तलाश कर रहे हैं जो आपको वह सब कुछ सिखाता है जो आपको Python में Linear Regression model बनाने के लिए चाहिए, है ना? (tutorialspoint.com)
- Create a linear regression model in Python and analyze its result. (tutorialspoint.com)
- And after running an analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. (tutorialspoint.com)
- This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems. (tutorialspoint.com)
- So what we have to do instead is make the assumption that that zero, one outcome comes from a Bernoulli distribution governed by a particular probability and we're going to use logistic regression to develop this particular model. (coursera.org)
- Respiratory resistance (Rrs) and elastance (Ers) are commonly measured in artificially-ventilated patients or animals by multiple linear regression of airway opening pressure (Pao) versus flow (V') and volume (V), according to the first order model: Pao = P0 + Ers.V + Rrs.V', where P0 is the static recoil pressure at end-expiration. (ersjournals.com)
- 5) Consider a linear regression model with the predictor variables X1, X2, and X3. (talkstats.com)
- Linear regression is an approach to modeling the association between a numeric dependent variable y and one or more independent variables denoted X. The case of one explanatory variable in regression model is called simple linear regression. (bibliomed.org)
- For more than one explanatory variable, then the model is called multiple linear regression. (bibliomed.org)
- It is recommended at least 10 times as many cases as the number of independent variables in regression model. (bibliomed.org)
- This factor regression tool supports factor regression analysis of individual assets or a portfolio of assets using the given risk factor model. (portfoliovisualizer.com)
- WEEK 1 Module 1: Regression Analysis: An Introduction In this module you will get introduced to the Linear Regression Model. (coursera.org)
- nag_correg_mixeff_reml ( g02ja ) fits a linear mixed effects regression model using restricted maximum likelihood (REML). (nag.com)
- Fit the neighborhood component analysis model for regression. (mathworks.com)
- This study presents comparative analysis of multiple linear regression model and quadratic regression. (bvsalud.org)
- To identify factors associated with the development of Pulmonary embolism, a multivariable Binary Logistic Regres- sion model with sensitivity analysis was run. (who.int)
- Meta-analysis of the natural logarithm of age-standardised M:F ratio (logSR) estimates was performed using a DerSimonian and Laird random effects model. (bmj.com)

###### Subgroup2

- In particular, the treatment effiects are subject-dependent and subgroup-specific, and our concave fusion penalized method conducts the subgroup analysis without needing to know the individual subgroup memberships in advance. (edu.hk)
- We performed quality assessments and subgroup and sensitivity analyses, and assessed the effect of adjustment for confounders. (medscape.com)

###### Systematic review and meta-analysis3

- Therefore, in this study, our aim is to provide a systematic review and meta-analysis of both categorical and continuous risk factors for suicidal ideation, suicide attempts, and suicide. (medscape.com)
- The prevalence and phenotypic features of polycystic ovary syndrome: a systematic review and meta-analysis. (bmj.com)
- Li M, Sun Y, Yang J, de Martel C, Charvat H, Clifford GM, Vaccarella S, Wang L. Time trends and other sources of variation in Helicobacter pylori infection in mainland China: A systematic review and meta-analysis. (who.int)

###### Bayesian1

- The Neural Networks classification, the Bayesian Decision Making and the Nonparametric Regression approaches are used and compared. (harvard.edu)

###### Dependent variable3

- The dependent variable should be a numeric variable in linear regression. (bibliomed.org)
- This blog focuses on simple linear regression, which is when you have one independent variable (x-variable) for a single dependent variable (y-variable). (ecapitaladvisors.com)
- In multiple regression, the dependent variable is considered to depend on more than a single independent variable. (bvsalud.org)

###### Retrospective1

- Of 51 874 unique citations, 25 studies (eight prospective and 17 retrospective cohort studies) were eligible for analysis, including 40 920 women living with HIV. (medscape.com)

###### Multiple9

- In the field of statistics regression analysis uses the techniques of modeling and analyzing multiple variables focusing on the relationships between dependent and independent variables helping the analyst to understand how the change of criterion in one independent variable affects the criterion of other dependent variables. (supremeessays.com)
- Regression Analysis In the field of statistics regression analysis refers to the techniques for modeling as well as analyzing multiple variables. (supremeessays.com)
- There are more advanced techniques like multiple linear regression, which is when you have multiple dependent variables for a single independent variable. (ecapitaladvisors.com)
- The multiple linear regression indicates how well the returns of the given assets or a portfolio are explained by the risk factor exposures. (portfoliovisualizer.com)
- There are numerous occasions where the use of multiple regression analysis is appropriate. (sagepub.com)
- An index based on multiple logistic regression that combined optic disc variables with axial length was also explored with the aim of improving diagnostic accuracy of disc variables. (elsevier.com)
- Axial length adjustments to disc variables in the form of multiple logistic regression indices led to a slight but insignificant improvement in diagnostic accuracy. (elsevier.com)
- Multiple and generalized nonparametric regression / John Fox. (who.int)
- Besides, a new factor (brain metastasis) was identified by 1:1 PSM -based multiple Cox regression, apart from the above prognostic factors for OS. (jcancer.org)

###### Prediction2

- Used widely for prediction and forecasting, regression analysis is also used for exploring relationships. (supremeessays.com)
- So, instead of relying on common sense or our gut instinct to make a prediction, we can utilize regression analysis. (ecapitaladvisors.com)

###### Estimates3

- Unlike other regression procedures, estimates can be derived even in the case where the number of predictor variables outnumbers the observations. (statgraphics.com)
- A large number of variables, many of which not yet well considered in regional frequency analysis (RFA), have a significant impact on hydrological dynamics and consequently on flood quantile estimates. (inrs.ca)
- Bonjour M, Charvat H, Franco EL, Piñeros M, Clifford GM, Bray F, Baussano I. Global estimates of expected and preventable cervical cancers among girls born between 2005 and 2014: a birth cohort analysis. (who.int)

###### Equation4

- This means that the regression equation for standardized variables is:Y' = rX. (supremeessays.com)
- First, compute the predicted values of y using the regression equation and store them as a new variable in Poverty_Regress called Predicted. (psu.edu)
- If you wanted to be more accurate, simply plug in and x value (the year) into the regression equation. (ecapitaladvisors.com)
- If you hover your mouse over the trend line you are presented with summary statistics such as the regression equation, R-squared value and the p-value. (ecapitaladvisors.com)

###### Results5

- The results of linear and nonlinear dose-response analysis indicated that high serum/plasma selenium and toenail selenium had the efficacy on cancer prevention. (researchgate.net)
- The 5% flow offset did not modify the results of Fourier analysis, but increased Rrs and Ers from linear regression by 15.8 +/- 4.6% and 4.55 +/- 0.64%, respectively. (ersjournals.com)
- The study presents the preliminary results of the analysis on the roles of non-profit organisations in the marketing of settlements regarding their performance in terms of creativity, innovativeness and competitiveness. (vu.nl)
- Provide a brief interpretation and analysis of the results of the regression for the given table. (essayknights.com)
- Results of search for 'ccl=su:{Regression analysis. (who.int)

###### Categorical4

- Towards the end of module we introduce the 'Dummy variable regression' which is used to incorporate categorical variables in a regression. (coursera.org)
- Dummy variable Regression (using Categorical variables in a Regression) WEEK 3 Module 3: Regression Analysis: Dummy Variables, Multicollinearity This module continues with the application of Dummy variable Regression. (coursera.org)
- You get to understand the interpretation of Regression output in the presence of categorical variables. (coursera.org)
- REGRESSION coefficient in ORDERED categorical LOGISTIC analysis. (eur.nl)

###### Nonparametric1

- Several techniques have been evolved including linear regression, ordinary least square regression, and nonparametric regression. (supremeessays.com)

###### Cohort2

- We also performed a cohort study meta-analysis as a supplemental analysis. (medscape.com)
- The first two were also significant in the cohort meta-analysis. (medscape.com)

###### Predictive2

- g differentiated docetaxel (a US Food and Drug Administration-approved therapy) from prednisone and mitoxantrone and was predictive of overall survival in a landmark analysis at 8 months. (urotoday.com)
- Future studies may combine the above-mentioned variables by using multivariate predictive analysis techniques to objectively stratify suicidality in schizophrenia. (medscape.com)

###### Slope2

- In the process he also displays the best fitting straight line with a slope of 0.481 and Y intercept of 15.8468 and the regression line can be used for predicting. (supremeessays.com)
- is the slope of the regression line. (essaysbank.com)

###### Least squares5

- Two-Stage least squares (2SLS) regression analysis is a statistical technique that is used in the analysis of structural equations . (statisticssolutions.com)
- Select two-stage least squares (2SLS) regression analysis from the regression option. (statisticssolutions.com)
- A Linear Regression trendline uses the least squares method to plot a straight line through prices so as to minimize the distances between the prices and the resulting trendline. (metastock.com)
- A Linear Regression trendline is simply a trendline drawn between two points using the least squares fit method. (metastock.com)
- This study reports a combined use of ordinary Fourier transform infrared spectroscopy (FT-IR) in conjunction with partial-least-squares (PLS) multivariate regression for accurate determination of the percent compositions of four essential oils (EOs) (wintergreen, tea tree, rosemary, and lemon eucalyptus oils) that were adulterated either with lemongrass essential oil or peppermint essential oil. (spectroscopyonline.com)

###### Procedure1

- The procedure innovatively combines the use of well-known regression analysis techniques, the two-parameter Gamma continuous cumulative probability distribution function and the Monte Carlo method. (mdpi.com)

###### Risk Factors1

- The current meta-analysis aims to determine risk factors associated with suicidality in subjects with schizophrenia. (medscape.com)

###### Sensitivity analyses1

- Sensitivity analyses for HbA1c showed a relationship at 3 months (p=0.006) and 1 year (p=0.002). (figshare.com)

###### Statistics1

- Regression Analysis is perhaps the single most important Business Statistics tool used in the industry. (coursera.org)

###### Examples1

- A powerful regression extension known as 'Interaction variables' is introduced and explained using examples. (coursera.org)

###### Spatial4

- It built upon a long history of software development for spatial analysis. (linuxlinks.com)
- The search was car- ried out in October 2021, using the descriptors Geographic Information Systems AND Covid-19 OR SARS-CoV-2 AND Epidemiology AND Spatial Analysis, in Virtual Health Library, Scopus, Web of Science, Portal CAPES. (bvsalud.org)
- Spatial Analysis. (bvsalud.org)
- Geographic Information Systems AND Covid-19 OR SARS-CoV-2 AND Epidemiology AND Spatial Analysis, na Biblio- teca Virtual em Saúde, Scopus, Web of Science, Portal CAPES. (bvsalud.org)