>/ IAnaIysis of Variance (ANOVA) evaluates whether the means of two or more groups are statistically different from each other. 9.1 Overview You have learned about inferential statistics, the steps in hypothesis testing and the role of probability in measuring or quantifying uncertainty as a way of drawing conclusions about a particular population from information available from only a sample. In the next three chapters, you will be introduced to specic inferential tools that allow us to use inferential principals to evaluate associations between different types of variables (i.e., those measured categorically and/or quantitatively). Analysis of Variance (ANOVA) evaluates whether the means of two or more groups are statistically different from each other. This test is appropriate whenever you want to compare the means (quantitative response variable) of groups (categorical explanatory variable). 9.2 Lesson Learn about the ANOVA F test. Consider the null and alternative hypothesis. See how variation within the sample means and variation between the groups plays a role in our evaluation of statistical signicance. Learn how to run an ANOVA test and interpret results within the context of real data. Understand the critical role of post hoc tests for interpreting ANOVA results when our explanatory variable includes more than two groups or levels. See how post hoc tests allow us to avoid increasing the likelihood of rejecting the null hypothesis when the null hypothesis should be accepted, a problem known as Type 1 error. U IVllnuteS, JO DECOHCIS 9.3 Syntax SAS proc anova; class CategxelanatoryVar; model QuantResponseVar=CategxelanatoryVar ; means CategexalanatoryVar ; *with Duncan post hoc test; proc anova; class CategxelanatoryVar; model QuantResponseVar=CategxelanatoryVar; means CategexalanatoryVar / duncan; D Question 1 1 pts This test is appropriate whenever you want to compare the: means [ Choose ] (categorical explanatory variable). (quantitative response variable) of groups [ Choose ]