In this example, we reanalyze the soybean yield data, but treat the explanatory variable as a categorical (ordinal) variable with 4 levels and use one−way anova to test for a significant SO2 effect. We can think of the data as samples from four different populations, corresponding to the SO2 concentration the chambers received, with possibly different means. In each treatment group we have 3 ob...

I. Definition and Applications...............................................................................2 II. Two-Way ANOVA prerequisites.......................................................................2 III. How to use the Two-Way ANOVA tool?...........................................................3 A. Parametric test, assume variances equal. .........................................

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...

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...

Selected hourly surface observations from Madison, Wis. and Minneapolis-St. Paul, Minn. are used as basic data for a series of analyses t o determine the feasibility of establishing weather classifications. Component analysis (factor analysis) is applied to a sample of January data for Madison to reduce the number of variables needed to suitably describe each day meteorologically and to create ...

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...