Using linear models and ANOVA to compare multiple groups in multiple variables
Two-way ANOVA is a statistical method used to analyze the effects of two categorical independent variables, also known as factors, on a continuous dependent variable. The two independent variables can be either fixed or random.
The main purpose of two-way ANOVA is to examine whether there is a significant interaction between the two independent variables, as well as to determine the main effects of each independent variable on the dependent variable.
The analysis involves calculating the sum of squares for each of the effects and the interaction and comparing these values to their respective degrees of freedom to obtain F ratios. The F ratios are then compared to critical values from an F-distribution to determine whether the effects are statistically significant.
Like the one-way ANOVA seen in the Using a linear model and ANOVA to compare multiple groups in a single variable recipe, the basis is...
