MINITAB was used to fit the model y = b0 + b1 x1 + b2 x2 + e where x1 = e 1 if level 2 0 if not x2 = e 1 if level 3 0 if not to n = 15 data points. The results are shown in the accompanying MINITAB printout.

a. Report the least squares prediction equation.

b. Interpret the values of b1 and b2.

c. Interpret the following hypotheses in terms of m1, m2, and m3:
H0: b1 = b2 = 0 Ha: At least one of the parameters b1 and b2 differs from 0

d. Conduct the hypothesis test of part c.

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The regression equation is Y = 80.0 +16.8 X1 +40.4 X2 Predictor Coef SE Coef Constant 80.000 X1 16.800 4.082 19.60 5.774 2.91 0.013 T P 0.000 X2 40.400 5.774 7.00 0.000 S=9.129 R-Sq 80.5% R-Sq (adj) = 77.2% Analysis of Variance Source DF SS MS F P Regression 2 4118.9 2059.5 24.72 0.000 Residual Error 12 1000.0 83.3 Total 14 5118.9