Statistics using R programming - Assessing Normality Assumption for Linear Regression in R

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In linear regression, If the dependent variable is normally distributed for a fixed set of predictor values, then the residual values should be normally distributed with a mean of 0. Normality assumption is a prerequisite for estimating the model.
The qqPlot() function provides a more accurate method of assessing the normality assumption than that provided by the plot() function in the base package. It plots the studentized residuals (also called studentized deleted residuals or jackknifed residuals) against a t distribution with n - p - 1 degrees of freedom, where n is the sample size and p is the number of regression parameters (including the intercept). If the points almost all stay on the straight line, then the normality assumption is met.
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