Shown is a partial computer output from a regression analysis:

Y X1 X2 X3

100 10 3 10

80 6 6 15

87 7 7 8

70 5 10 6

65 4 12 4

77 5 8 6

65 2 14 3

78 6 9 5

90 8 4 12

82 7 5 8

Regression Analysis: y versus x1, x2, x3 .The regression equation is

Y = 61.53 + 3.837 X1 - 0.66 X2 - 0.004 X3

Predictor Coef SE Coef T P

Constant 61.53 26.10 2.36 0.057

X1 3.837 1.930 1.99 0.094

X2 -0.657 1.508 -0.440.678

X3 -0.0035 0.6235 -0.01 0.996

R-Sq = -----

Analysis of Variance(ANOVA Table)

Source DF SS MS F P

-----------------------------------------------------------------

Regression ---- ---- ---- 23.99 0.001

Residual Error ---- 24.00 ----

Total ---- ----

Stepwise Regression: Y versus X1, X2, X3

Backward elimination.

Response is Y on 3 predictors,

Step 1 2 3

Constant 61.53 61.42 50.49

X1 3.84 3.84 4.82

T-Value 1.99 2.45 9.48

P-Value 0.094 0.044 0.000

X2 -0.66 -0.65

T-Value -0.44 -0.66

P-Value 0.678 0.530

X3 -0.00

T-Value -0.01

P-Value 0.996

1. Calculate all of the missing entries.

1. Determine whether there is a significant linear relationship between Y and at least one of the three explanatory variables at 0.05 level of significance.

c) At the 0.05 level of significance, determine whether each explanatory variable makes a significant contribution to the regression model. Based upon these results, indicate the regression model that should be utilized in this problem.