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Simple linear regression is a statistical method used to model the linear relationship between an independent variable and a dependent variable.
In statistics, bivariate data is data on each of two variables, where each value of one of the variables is paired with a value of the other variable. [1] It is a specific but very common case of multivariate data.
In statistics, the Cochran–Mantel–Haenszel test (CMH) is a test used in the analysis of stratified or matched categorical data.It allows an investigator to test the association between a binary predictor or treatment and a binary outcome such as case or control status while taking into account the stratification. [1]
The numbers of the males, females, and right- and left-handed individuals are called marginal totals.The grand total (the total number of individuals represented in the contingency table) is the number in the bottom right corner.
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There are several key assumptions that underlie the use of ANCOVA and affect interpretation of the results. [2] The standard linear regression assumptions hold; further we assume that the slope of the covariate is equal across all treatment groups (homogeneity of regression slopes).
Multivariate analysis (MVA) is based on the principles of multivariate statistics.Typically, MVA is used to address situations where multiple measurements are made on each experimental unit and the relations among these measurements and their structures are important. [1]
Nonparametric statistics is a type of statistical analysis that makes minimal assumptions about the underlying distribution of the data being studied. Often these models are infinite-dimensional, rather than finite dimensional, as in parametric statistics. [1]