QUESTION 4 Consider the following output Model Summary Model R R Square Adjusted
ID: 406551 • Letter: Q
Question
QUESTION 4
Consider the following output
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
Durbin-Watson
1
.812
0.659
.632
124.07540
2
.707
0.500
.428
99.21975
3
.757
0.573
.528
99.15493
2.027
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
24102816.74
1
24102816.74
1565.66
.061
Residual
67936830.74
4413
15394.70
Total
92039647.48
4414
2
Regression
37263918.08
2
18631959.04
1500.74
.013
Residual
54775729.39
4412
12415.17
Total
92039647.48
4414
3
Regression
48416528.95
3
16138842.98
1631.90
.049
Residual
43623118.52
4411
9889.62
Total
92039647.48
4414
Which model would you choose for the analysis?
Model 1
Model 2
Model 3
None
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
Durbin-Watson
1
.812
0.659
.632
124.07540
2
.707
0.500
.428
99.21975
3
.757
0.573
.528
99.15493
2.027
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
24102816.74
1
24102816.74
1565.66
.061
Residual
67936830.74
4413
15394.70
Total
92039647.48
4414
2
Regression
37263918.08
2
18631959.04
1500.74
.013
Residual
54775729.39
4412
12415.17
Total
92039647.48
4414
3
Regression
48416528.95
3
16138842.98
1631.90
.049
Residual
43623118.52
4411
9889.62
Total
92039647.48
4414
Explanation / Answer
1)Model 1
Explanation:
Because Model 1 have sum of squares, df,Mean Square,F and Sig all are highest value, other than model 2 and model 3.
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