Behavioral Research Data Analysis with R by Yuelin Li & Jonathan Baron

Behavioral Research Data Analysis with R by Yuelin Li & Jonathan Baron

Author:Yuelin Li & Jonathan Baron
Language: eng
Format: epub
Publisher: Springer New York, New York, NY


6.2.2 Checking Model Distributional Assumptions

A plot of model residuals against fitted values shows a problem in the model. Figure 6.2 shows that diamonds 116, 131, and 279 are outliers. They are identified automatically in a residual plot. The observed prices of these diamond stones greatly exceed the model’s predicted prices. The model underestimates the observed price of these larger stones with D and E color and VVS1 clarity (see the fitted column below). Diamonds of these characteristics are sold at a premium price, much higher than the fitted values of the model. The values of these residuals clearly depart from what are expected if they are normally-distributed.

Fig. 6.2Plots of model residuals. On the left is a residuals vs. fitted value plot to identify outliers of the model’s prediction, plotted with the plot.lm(lm1, which = 1) command. On the right is a plot of observed quantiles of the residuals against the theoretical quantiles of a standard normal distribution, plotted with plot.lm(lm1, which = 2). A dotted line is added showing where the observed quantiles should be if they follow a standard normal distribution. The two plots identifies outliers of model assumptions



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