![]() If the resulting plot is approximately linear, we proceed assuming that the error terms are normally distributed. ![]() Since we are concerned about the normality of the error terms, we create a normal probability plot of the residuals. Here's the basic idea behind any normal probability plot: if the data follow a normal distribution with mean \(\mu\) and variance \(σ^\), then a plot of the theoretical percentiles of the normal distribution versus the observed sample percentiles should be approximately linear. ![]() In this section, we learn how to use a " normal probability plot of the residuals" as a way of learning whether it is reasonable to assume that the error terms are normally distributed. Recall that the third condition - the "N" condition - of the linear regression model is that the error terms are normally distributed.
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