Evaluating the NYC SAT Scores Factorial Model

We've built our model, so we know what's next: model checking! We need to examine both if our outcome and our model residuals are normally distributed. We'll check the normality assumption using shapiro.test(). A low p-value means we can reject the null hypothesis that the sample came from a normally distributed population.

Let's carry out the requisite model checks for our 2^k factorial model, nyc_scores_factorial, which has been loaded for you.

Diese Übung ist Teil des Kurses

Experimental Design in R

Kurs anzeigen

Anleitung zur Übung

Test the outcome Average_Score_SAT_Math from nyc_scores for normality using shapiro.test().
Set up a 2 by 2 grid for plots and plot the nyc_scores_factorial model object to create the residual plots.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Use shapiro.test() to test the outcome
___

# Plot nyc_scores_factorial to examine residuals
par(mfrow = c(2,2))
___

Code bearbeiten und ausführen