Topics Covered in One-Way ANOVA and Factorial Experiments: Ch. 6 and 9-11.3 - Prof. B. Hab, Study notes of Data Analysis & Statistical Methods

Download Topics Covered in One-Way ANOVA and Factorial Experiments: Ch. 6 and 9-11.3 - Prof. B. Hab and more Study notes Data Analysis & Statistical Methods in PDF only on Docsity! (possibly incomplete list of) Topics Covered in Chapters 6 and 9-11.3 Chapter 6 (and Supplement) – One-Way Analysis of Variance The one-way ANOVA table and notation (including how to make the table) The assumptions for the one-way ANOVA That the modified Levene's test (a.k.a. Brown and Forsythe test) can be used to test the variances are equal What hypothesis the basic ANOVA F-test (the omnibus test, test) tests Family-wise (or experiment-wise) type I error rate, αF, versus comparisonwise type I error rate, α Conservative vs. Liberal - Holm vs. Bonferroni vs. Fisher How the Holm Test works Making a display from the Holm test on all pairs of treatment levels How to construct contrasts How to show contrasts are orthogonal (only works when the ANOVA is balanced) What the estimates and tests corresponding to contrasts tell us Making confidence intervals for contrasts When to use each of the following: the basic ANOVA test, Holm test for each pair of tests, and Contrasts Interpreting the SAS output Topics Not Covered from Chapter 6 Pg. 238-239 - Hartley's F-max test Pgs. 249-252 - Fitting Trends Pgs. 254-267 - Tukey’s HSD, Duncan’s Multiple Range, Scheffe Section 6.8 - Analysis of Means Chapter 9 (and Supplement) - Factorial Experiments What Factorial, Fixed Effect, Balanced, and Replications mean How to use the ANOVA table and how it fits together (but not all the equations) Partitioning the SSB The model equation for the two factor ANOVA table with interactions, and what the terms mean The relationship of the 2 by 2, two factor ANOVA table with interactions to the orthogonal contrasts in a one way ANOVA with A-I, A-II, B-I, B-II (for example). The model for the factorial models with more factors, for example all the different interaction terms that have to be added What to do when there are no replications, and why this is necessary What about the interpretation gets more complicated when there is an interaction Interpreting the SAS output Topics Not Covered from Chapter 9 Section 9.4 - Interaction Contrasts, Polynomial Responses, Lack of Fit Test