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.

Cet exercice fait partie du cours

Experimental Design in R

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Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Load ggplot2
___

# Build the boxplot for the tutoring program vs. Math SAT score
ggplot(___,
       aes(___, ___)) + 
    geom_boxplot()

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