Usually, a significance level (denoted as α or alpha) of 0.05 works well. (Click the graph to see it in more detail.To determine whether the data do not follow a normal distribution, compare the p-value to the significance level. In our example, the normality plot of the residuals are pretty much linear, but I would be concerned about the upward trend at the far right end of the graph. Review this graph and ask yourself: Do the residual points fall more-or-less on a straight line in the normal probability plot? If they do, you can conclude that the errors are distributed normally and the normality of errors assumption is valid. Minitab even draws a line through the residuals for us (presumably using the method of least-squares). The y-axis of this graph is adjusted so that if the data are distributed normally, they will fall on a straight line on the graph.
Minitab creates the normal probability plot of the residuals. Click OK in the Graphs and the Regression dialogs. Click the Graph button and under Residual Plots, check the Normal plot of residuals checkbox.ĥ. Put Annual Sales in the Response box since it's the dependent (response) variable and put Square Feet in the Predictors box since it's the independent (predictor) variable.Ĥ. Select Stat-Regression-Regression from the menu bar.ģ. As usual, we'll use the site.mtw file for our example.Ģ. Fortunately for us, Minitab has a built-in normal probability plot function.ġ.
We do this by running a normal probability plot of the residuals. A larger error is less likely than a smaller error and the distribution of errors at any x follows the normal distribution.Īlthough we typically only have one observation at each x, if we assume that the distribution of the errors is the same at each x, we can simply plot all the errors (residuals) and check if they follow the normal distribution. This assumption states that the error in the observation is distributed normally at each x-value. In the meantime, we looked at the next assumption: Normality of Error. We skipped that one momentarily because it's a bit more complex than the others. The next assumption in the LINE mnemonic after Linearity is Independence of Errors.
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