Interpret the regression coefficients for the least-squares regression equation found in Example 2. Approach Interpret the coefficients
Question:
Interpret the regression coefficients for the least-squares regression equation found in Example 2.
Approach Interpret the coefficients the same way as we did for a regression with one explanatory variable. However, assume that the remaining explanatory variables are constant.
Data from Example 2
Use the data in Table 4.
Find the least-squares regression equation \(\hat{y}=b_{0}+b_{1} x_{1}+b_{2} x_{2}\), where \(x_{1}\) represents the patient's age, \(x_{2}\) represents the patient's daily consumption of saturated fat, and \(y\) represents the patient's total cholesterol.
Draw residual plots and a boxplot of the residuals to assess the adequacy of the model.
Enter the data into Minitab to obtain the least-squares regression equation and to draw the residual plots and boxplot of the residuals. The steps for determining the multiple regression equation and residual plots using Minitab, Excel, and StatCrunch are given in the Technology Step-by-Step.
Step by Step Answer:
Statistics Informed Decisions Using Data
ISBN: 9781292157115
5th Global Edition
Authors: Michael Sullivan