• Multiple linear regression should always begin with a careful examination of the data. This involves looking at each variable separately and then at pairs of variables. Cases with extreme values should be noted and examined carefully throughout the analysis.
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• Multiple linear regression has the same model conditions as simple linear regression. Prior to inference, always examine the distribution of the residuals and plot them against each of the explanatory variables to make sure there are no remaining patterns or nonconstant variance.
• The estimate bj of βj and the test and confidence interval for βj are all based on a specific multiple linear regression model. The results of all these procedures change if other explanatory variables are added to or deleted from the model.