> summary(lm(Y~X1+X2)) # Equation 10.3 Call: lm(formula = Y ~ X1 + X2) Residuals: Min 1Q Median 3Q Max -15.674 -10.225 -1.566 8.989 24.184 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -205.7187 11.3927 -18.057 1.38e-11 *** X1 6.2880 0.2041 30.801 5.63e-15 *** X2 4.7376 1.3781 3.438 0.00366 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 12.66 on 15 degrees of freedom Multiple R-squared: 0.9864, Adjusted R-squared: 0.9845 F-statistic: 542.3 on 2 and 15 DF, p-value: 1.026e-14 > summary(lm(Y~X2)) # Equation 10.4a Call: lm(formula = Y ~ X2) Residuals: Min 1Q Median 3Q Max -117.40 -63.77 -34.67 48.91 249.76 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 50.70 60.36 0.840 0.413 X2 15.54 10.34 1.502 0.152 Residual standard error: 98.27 on 16 degrees of freedom Multiple R-squared: 0.1236, Adjusted R-squared: 0.06887 F-statistic: 2.257 on 1 and 16 DF, p-value: 0.1525 > summary(lm(X1~X2)) # Equation 10.4b Call: lm(formula = X1 ~ X2) Residuals: Min 1Q Median 3Q Max -22.517 -10.114 -3.916 10.009 36.883 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 40.779 9.524 4.282 0.000572 *** X2 1.718 1.632 1.053 0.308159 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 15.51 on 16 degrees of freedom Multiple R-squared: 0.06476, Adjusted R-squared: 0.006312 F-statistic: 1.108 on 1 and 16 DF, p-value: 0.3082