Last week we learned about one-way designs. They examine several levels of a single factor. For instance, we might examine the effect of StudyType (independent variable) on LanguagePerformance (dependent variable). Classroom study Self-study Control Perhaps we think that the StudyType manipulation might only affect one gender or the other. We could do a two-way design whether a second factor, G...

To evaluate compound-related effects on the growth of rodents, body weight and food consumption data are commonly collected either weekly or biweekly in toxicology studies. Body weight gain, food consumption relative to body weight, and efficiency of food utilization can be derived from body weight and food consumption for each animal in an attempt to better understand the compound-related effe...

• Although relatively robust to the following, ANOVA assumes • Normal distribution of residuals • Variation in each group is similar • ANOVA is sensitive to outliers, and transforms of the data may be necessary • ANOVA is a test of group differences: do at least two of the means differ from each other? • Two-way ANOVA tests a number of sources of variability • It is important to know which sour...

• Although relatively robust to the following, ANOVA assumes • Normal distribution of residuals • Variation in each group is similar • ANOVA is sensitive to outliers, and transforms of the data may be necessary • ANOVA is a test of group differences: do at least two of the means differ from each other? • Two-way ANOVA tests a number of sources of variability • It is important to know which sour...

Analysis of variance (Anova) is an extremely important method in exploratory and confirmatory data analysis. Unfortunately, in complex problems (for example, splitplot designs), it is not always easy to set up an appropriate Anova. We propose a hierarchical analysis that automatically gives the correct Anova comparisons even in complex scenarios. The inferences for all means and variances are p...

Analysis of variance (ANOVA) is an extremely important method in exploratory and confirmatory data analysis. Unfortunately, in complex problems (e.g., split-plot designs), it is not always easy to set up an appropriate ANOVA. We propose a hierarchical analysis that automatically gives the correct ANOVA comparisons even in complex scenarios. The inferences for all means and variances are perform...

Analysis of variance (ANOVA), the workhorse analysis of experimental designs, consists of F-tests of main effects and interactions. Yet, testing, including traditional ANOVA, has been recently critiqued on a number of theoretical and practical grounds. In light of these critiques, model comparison and model selection serve as an attractive alternative. Model comparison differs from testing in t...