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  3. Inferential Statistics
  • 1

    The Basics of R

    This lab is about teaching enough R to start using it for statistical analyses.

  • 2

    Introduction to R continued

    This lab continues with an introduction to R

  • 3

    Comparing two groups

    The current lab treats differences between two independent groups, paired groups and proportions

  • 4

    Categorical Association

    Investigating associations between categorical variables.

  • 5

    Simple Regression

    Predicting a response variable one predictor variable.

  • 6

    Multiple Regression

    Predicting a response variable with more than one predictor variable.

  • 7

    Quantitative associations: ANOVA

    This lab treats the statistical methodology of the analysis of variance

  • 8

    Nonparametric tests

    This lab will treat testing methods that can be employed when the assumptions of other methods are not met

Initializing

Exercise

Explaining the anova function

In the last exercise we saw that both our assumption of normality and the assumption of homogeneity of variance were not met. Usually this would mean that we would perform a non-parametric test. These test will be illustrated during the next week. However, during this lab we will continue to do an analysis of variance and will interpret the output as though our assumptions are met.

In our you can use two functions to perform an analysis of variance: the aov() function and the lm() function. There are very few differences between the two functions. However, the main difference is the output that each of these functions produces. The aov() function produces the more traditional anova output and may seem more familiar if you are coming from a statistical software package like SPSS.

Instructions

100 XP
  • Peform an anova using the aov() function with genre as the independent variable and song duration as the dependent variable. If y is your dependent variable and x is your independent variable, you could perform an anova like so: aov(y ~ x). Store the result of your anova in a variable called fit_aov. Note that our data is available in the song_data dataframe.
  • Use the summary() function on the the fit_aov variable and print the output to the console. You can just provide your fit_aov object as the argument to the summary() function.
  • Do an anova using the lm() function with genre as independent variable and song duration as the dependent variable. Store the result in a variable called fit_lm.
  • Use the summary() function on the variable fit_lm and print the output to the console