Factorial models and analysis of variance have been a central pillar of statistical thinking and practice for at least 70 years, so the opportunity to revisit these topics to look for signs of new life is welcome. Given its lengthy history, Anova is an unpromising area for new development, but recent advances in computational techniques have altered the outlook regarding spatial factors, and co...

Modern society generates an incredible amount of data about individuals, and releasing summary statistics about this data in a manner that provably protects individual privacy would offer a valuable resource for researchers in many fields. We present the first algorithm for analysis of variance (ANOVA) that preserves differential privacy, allowing this important statistical test to be conducted...

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), 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...