Transcription of AN INTRODUCTION TO MULTIVARIATE STATISTICS
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AN INTRODUCTION TO MULTIVARIATE STATISTICS
An INTRODUCTION to MULTIVARIATE STATISTICS The term MULTIVARIATE STATISTICS is appropriately used to include all STATISTICS where there are more than two variables simultaneously analyzed. You are already familiar with bivariate STATISTICS such as the Pearson product moment correlation coefficient and the independent groups t-test. A one-way ANOVA with 3 or more treatment groups might also be considered a bivariate design, since there are two variables: one independent variable and one dependent variable. Statistically, one could consider the one-way ANOVA as either a bivariate curvilinear regression or as a multiple regression with the K level categorical independent variable dummy coded into K-1 dichotomous variables. Independent vs. Dependent Variables We shall generally continue to make use of the terms independent variable and dependent variable, but shall find the distinction between the two somewhat blurred in MULTIVARIATE designs, especially those observational rather than experimental in nature.
An Introduction to Multivariate Statistics ... When all of the variables are categorical, log-linear analysis is appropriate. When it is reasonable to consider one of the variables as dependent and the others as independent, as in this study, a special type of log-linear analysis called a LOGIT ANALYSIS is employed. In the second experiment
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