From the course: R Essential Training Part 2: Modeling Data
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Comparing multiple means: One-factor analysis of variance
From the course: R Essential Training Part 2: Modeling Data
Comparing multiple means: One-factor analysis of variance
- [Instructor] Sometimes you can get away with comparing just two groups, but sometimes you got a lot more than that going on. If you have three groups, or four or five, you can't really use a t-test anymore, because you get an inflated false positive rate. Instead, it's more common to use the analysis of variance or specifically, the one way or one factor analysis of variance, which lets you compare the means of several groups simultaneously. To demonstrate this, I'm going to load a few packages, including the psych package that's going to have some functions I'm going to use in this demonstration. And then I'm going to load the state data and I'm going to save it into df for data frame. This is the information about the state by state Google search term popularity, as well as some information about the personality classifications of states. I'm going to save it as a table, I'm one just select a few different…
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Comparing proportions8m 3s
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Comparing one mean to a population: One-sample t-test6m 20s
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Comparing paired means: Paired samples t-test9m 53s
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Comparing two means: Independent samples t-test8m 30s
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Comparing multiple means: One-factor analysis of variance11m 16s
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Comparing means with multiple categorical predictors: Factorial analysis of variance8m 47s
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