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q2-q23.docx
Model2-
Fit of Profits ($M) By Assets
Linear Fit
Profits ($M) = -100.8766 + 0.1279341 Assets
Summary of Fit
|
RSquare |
0.571986 |
|
RSquareAdj |
0.5567 |
|
Root Mean Square Error |
312.406 |
|
Mean of Response |
332.65 |
|
Observations (or Sum Wgts) |
30 |
Analysis of Variance
|
Source |
DF |
Sum of Squares |
Mean Square |
F Ratio |
|
Model |
1 |
3651944.7 |
3651945 |
37.4184 |
|
Error |
28 |
2732730.5 |
97598 |
Significance F |
|
C. Total |
29 |
6384675.2 |
|
<.0001 |
Parameter Estimates
|
Term |
|
Estimate |
Std Error |
t Ratio |
p-value |
|
Intercept |
|
-100.8766 |
90.97281 |
-1.11 |
0.2769 |
|
Assets |
|
0.1279341 |
0.020914 |
6.12 |
<.0001 |
MODEL 3
Y: Profits ($M)
Summary of Fit
|
RSquare |
0.681512 |
|
RSquareAdj |
0.650513 |
|
Root Mean Square Error |
314.5783 |
|
Mean of Response |
332.65 |
|
Observations (or Sum Wgts) |
30 |
Analysis of Variance
|
Source |
DF |
Sum of Squares |
Mean Square |
F Ratio |
|
Model |
2 |
3712768.0 |
1856384 |
18.7590 |
|
Error |
27 |
2671907.2 |
98960 |
Significance F |
|
C. Total |
29 |
6384675.2 |
|
<.0001 |
Parameter Estimates
|
Term |
|
Estimate |
Std Error |
t Ratio |
p-value |
|
Intercept |
|
-102.6623 |
91.6337 |
-1.12 |
0.2724 |
|
Assets |
|
0.1609338 |
0.047067 |
3.42 |
0.0020 |
|
# Employ |
|
-0.044355 |
0.005555 |
-2.78 |
0.0399 |
Residual by Predicted Plot
MODEL 4
Y: Profits ($M)
Summary of Fit
|
RSquare |
0.625649 |
|
RSquareAdj |
0.604215 |
|
Root Mean Square Error |
316.7746 |
|
Mean of Response |
332.65 |
|
Observations (or Sum Wgts) |
30 |
Analysis of Variance
|
Source |
DF |
Sum of Squares |
Mean Square |
F Ratio |
|
Model |
2 |
3675328.7 |
1837664 |
18.3132 |
|
Error |
27 |
2709346.5 |
100346 |
Significance F |
|
C. Total |
29 |
6384675.2 |
|
<.0001 |
Parameter Estimates
|
Term |
|
Estimate |
Std Error |
t Ratio |
p-value |
|
Intercept |
|
-107.6454 |
93.30456 |
-1.15 |
0.2587 |
|
Sales ($M) |
|
0.0253284 |
0.052469 |
0.48 |
0.6332 |
|
Assets |
|
0.1026876 |
0.056435 |
1.82 |
0.0799 |
Question8- the best model is
|
Model 3 because it has the highest R2
|
|
Model 2 because it has a strong R2 value and it is a simple model with significant parameter estimates |
|
Model 4 because it has a strong R2 value and has meaningful Beta estimates
|
|
All are good models, we can go with any of them
|
