Factorial Experiment with NYC SAT Scores

Now we want to examine the effect of tutoring programs on the NYC schools' SAT Math score. As noted in the last exercise: the variable Tutoring_Program is simply yes or no, depending on if a school got a tutoring program implemented. For Percent_Black_HL and Percent_Tested_HL, HL stands for high/low. A 1 indicates less than 50% Black students or overall students tested, and a 2 indicates greater than 50% of both.

Remember that because we intend to test all of the possible combinations of factor levels, we need to write the formula like: outcome ~ factor1 * factor2 * factor3.

This exercise is part of the course

Experimental Design in R

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Exercise instructions

Use aov() to create a model to test how Percent_Tested_HL, Percent_Black_HL, and Tutoring_Program affect the outcome Average_Score_SAT_Math.
Save the outcome as a model object, nyc_scores_factorial, and examine this with tidy().

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create nyc_scores_factorial and examine the results
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