![]() ![]() But enough about history, let's get to this lesson. In SAS PROC MIXED or in Minitab's General Linear Model, you have the capacity to include covariates and correctly work with random effects. The same sort of process can be seen in Minitab and accounts for the multiple tabs under Stat > ANOVA and Stat > Regression. PROC GLM had problems when it came to random effects and was effectively replaced by PROC MIXED. There is no PROC ANCOVA is SAS but there is PROC MIXED. The general linear model handles both the regression and the categorical variables in the same model. Or, conversely, if you are running a regression and you have a categorical predictor like gender, you could include it into the regression model and it runs. With PROC GLM you could take the continuous regression variable and pop it into the ANOVA model and it runs. Then people asked, "What about the case when you have categorical factors and you want to do an ANOVA but now you have this other variable, a continuous variable, that you can use as a covariate to account for extraneous variability in the response?" So, SAS came out with PROC GLM which is the general linear model. You might find it interesting that when SAS first came out they had PROC ANOVA and PROC REGRESSION and that was it. In the next lesson, we will generalize the ANCOVA model to include the quadratic and cubic effects of the covariate as well. In this lesson we will address the classic case of ANCOVA where the ANOVA model is extended to include the linear effect of a continuous variable, known as the covariate. In ANCOVA, we will combine the concepts applicable to categorical factors learned so far in this course with the principles and foundations of regression, applicable to continuous predictors learned in STAT 501. The analysis of covariance (ANCOVA) procedure is used when the statistical model has both quantitative and qualitative predictors, and is based on the concepts of the General Linear Model (GLM). ![]()
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