Note that the * between the two predictor variables indicates that we also want to test for an interaction effect between the two predictor variables. The general syntax to fit a two-way ANOVA model in R is as follows:Īov(response variable ~ predictor_variable1 * predictor_variable2, data = dataset) Next, we’ll fit the two-way ANOVA model to our data to see if these visual differences are actually statistically significant. We can also see that males tend to have higher weight loss values for the intense and light exercise groups compared to females. Right away we can see that the two groups who participated in intense exercise appear to have greater weight loss values. Las = 2 #make x-axis labels perpendicular ![]() Main = "Weight Loss Distribution by Group", We can also create a boxplot for each of the six treatment groups to visualize the distribution of weight loss for each group: #set margins so that axis labels on boxplot don't get cut off The following code creates the data frame we’ll be working with: #make this example reproducible We recruit 30 men and 30 women to participate in an experiment in which we randomly assign 10 of each to follow a program of either no exercise, light exercise, or intense exercise for one month. ![]() We can conduct a two-way ANOVA to determine if exercise and gender impact weight loss and to determine if there is an interaction between exercise and gender on weight loss. In this case, the two factors we’re studying are exercise and gender and the response variable is weight loss, measured in pounds. Suppose we want to determine if exercise intensity and gender impact weight loss. This tutorial explains how to perform a two-way ANOVA in R. A two-way ANOVA (“analysis of variance”) is used to determine whether or not there is a statistically significant difference between the means of three or more independent groups that have been split on two factors.
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