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When treating categorical variables such as ethnic groups and experimental treatments as independent variables in moderated regression, one needs to code the variables so that each code variable represents a specific setting of the categorical variable. There are three basic ways of coding: dummy-variable coding, contrast coding and effects coding.
In scientific experimental settings, researchers often change the state of one variable (the independent variable) to see what effect it has on a second variable (the dependent variable). [3] For example, a researcher might manipulate the dosage of a particular drug between different groups of people to see what effect it has on health.
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. The association can be studied via a tabular or graphical display, or via sample statistics which might be used for inference.
Bivariate analysis is one of the simplest forms of quantitative (statistical) analysis. [1] It involves the analysis of two variables (often denoted as X, Y), for the purpose of determining the empirical relationship between them. [1] Bivariate analysis can be helpful in testing simple hypotheses of association.
It is possible to have multiple independent variables or multiple dependent variables. For instance, in multivariable calculus, one often encounters functions of the form z = f(x,y), where z is a dependent variable and x and y are independent variables. [8] Functions with multiple outputs are often referred to as vector-valued functions.
In statistics, the two-way analysis of variance (ANOVA) is an extension of the one-way ANOVA that examines the influence of two different categorical independent variables on one continuous dependent variable. The two-way ANOVA not only aims at assessing the main effect of each independent variable but also if there is any interaction between them.
In this design setup, there are multiple variables, some classified as within-subject variables, and some classified as between-group variables. [3] One example study combined both variables. This enabled the experimenter to analyze reasons for depression among specific individuals through the within-subject variable, and also determine the ...
The extracted variables are known as latent variables or factors; each one may be supposed to account for covariation in a group of observed variables. Canonical correlation analysis finds linear relationships among two sets of variables; it is the generalised (i.e. canonical) version of bivariate [3] correlation.