MINITAB was used to fit the model
y = β0 + β0x1 + β0x2 + ε
where

to n = 15 data points. The results are shown in the accompanying MINITAB printout (top of next column).
a. Report the least squares prediction equation.
b. Interpret the values of β1 and β2.

c. Interpret the following hypotheses in terms of μ1, μ2, and μ3:
H0: β1 = β2 = 0
Ha: At least one of the parameters β1 and β2 differs from 0
d. Conduct the hypothesis test of part c.

1 if level 2 xi=10 f not 1 if level 3 x2 10 if not The regression equation is Y=80.0 + 16.8 X1 +40.4 X2 Predictor onstant X1 X2 Coef SE Coef 80.000 16.800 40.400 4.082 19.60 0.000 5.774 2.91 0.013 5.774 7.00 0.000 R-Sq 80.5% R-Sq (adj ) 77.2% = Analysis of Variance Source Regression Residual Error 12 1000.0 Total DF HS 2 4118.9 2059.5 24.720.000 83.3 14 5118.9