Two-Way ANOVA

Two-Way ANOVA - Stats: Two-Way ANOVA The two-way analysis...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
Stats: Two-Way ANOVA The two-way analysis of variance is an extension to the one-way analysis of variance. There are two independent variables (hence the name two-way). Assumptions The populations from which the samples were obtained must be normally or approximately normally distributed. The samples must be independent. The variances of the populations must be equal. The groups must have the same sample size. Hypotheses There are three sets of hypothesis with the two-way ANOVA. The null hypotheses for each of the sets are given below. 1. The population means of the first factor are equal. This is like the one-way ANOVA for the row factor. 2. The population means of the second factor are equal. This is like the one-way ANOVA for the column factor. 3. There is no interaction between the two factors. This is similar to performing a test for independence with contingency tables. Factors The two independent variables in a two-way ANOVA are called factors. The idea is that there are two variables, factors, which affect the dependent variable. Each factor will have two or more levels within it, and the degrees of freedom for each factor is one less than the number of levels. Treatment Groups
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 4

Two-Way ANOVA - Stats: Two-Way ANOVA The two-way analysis...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online