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  3. Experimental Design in R

Exercise

NYC SAT Scores Factorial EDA

Let's do some more EDA before we dive into the analysis of our factorial experiment.

Let's test the effect of Percent_Black_HL, Percent_Tested_HL, and Tutoring_Program on the outcome, Average_Score_SAT_Math. The HL stands for high-low, where a 1 indicates respectively that less than 50% of Black students or that less than 50% of all students in an entire school were tested, and a 2 indicates that greater than 50% of either were tested.

Build a boxplot of each factor vs. the outcome to have an idea of which have a difference in median by factor level (ultimately, mean difference is what's tested.) The nyc_scores dataset has been loaded for you.

Instructions 1/3

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  • 1
    • Load ggplot2. Create a boxplot of the outcome versus Tutoring_Program.
  • 2
    • Using ggplot2, create a boxplot of the outcome versus Percent_Black_HL.
  • 3
    • Using ggplot2, create a boxplot of the outcome versus Percent_Tested_HL.