Suppose you used MINITAB to fit the model y = b0 + b1 x1 + b2 x2 + e to n = 15 data points and you obtained the printout below.

a. What is the least squares prediction equation?

b. Find R2 and interpret its value.

c. Is there sufficient evidence to indicate that the model is useful in predicting y? Conduct an F-test, using a = .05.

d. Test the null hypothesis H0: b1 = 0 against the alternative hypothesis Ha: b1 0. Use a = .05. Draw the appropriate conclusions.

e. Find the standard deviation of the regression model and interpret it.

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The regression equation is Y = 90.11.84 X1 + .285 X2 Predictor Coef SE Coef T P Constant 90.10 23.10 3.90 0.002 X1 -1.836 0.367 -5.01 0.001 X2 0.285 0.231 1.24 0.465 S = 10.68 R-Sq = 91.6% R-Sq (adj) = 90.2% Analysis of Variance. Source DF SS MS Regression 2 14801 7400 Residual Error 12 1364 114 Total 14 16165 F P 64.91 0.001