![]() ![]() The dependent variables are quiz_1, quiz_2. You can see that it is categorical (not continuous), having five distinct levels. The independent variable here is the ethnicity. Suppose we are asked to determine if the mean scores on several types of quizzes are different between five ethnic groups of students: White, Black, Native, Asian and Hispanic. The explanation that follows gives a conceptual feel for what multivariate analysis of variance is attempting to accomplish. The conclusions from the t-test and the one-way two-group ANOVA are exactly the same. Further on, the degenerate case of ANOVA comparing the means between only two groups is algebraically equivalent to t-test. Univariate analysis of variance ( ANOVA) is the degenerate case of MANOVA having only one dependent variable. Two independent variables is the case of two-way MANOVA, three variables is the case of there-way MANOVA, and so on. If there is one independent variable, whose levels define the groups, then the analysis is called one-way MANOVA. There can be one or more independent variables. The different groups are observations with different values of certain categorical factors, which are called independent variables. ![]() The random vector is composed of several random variables, which are called dependent variables. Multivariate Analysis of Variance (MANOVA) is a procedure used for determining if the expected value of a given random vector is different in different groups. ![]()
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