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.
This exercise is part of the course
Experimental Design in R
Exercise instructions
- Test the outcome
Average_Score_SAT_Mathfromnyc_scoresfor normality usingshapiro.test(). - Set up a 2 by 2 grid for plots and plot the
nyc_scores_factorialmodel object to create the residual plots.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Use shapiro.test() to test the outcome
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# Plot nyc_scores_factorial to examine residuals
par(mfrow = c(2,2))
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