using excel>data >data analysis >Regression
we have
| SUMMARY OUTPUT |
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| Regression Statistics |
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| Multiple R |
0.906854 |
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| R Square |
0.822383 |
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| Adjusted R Square |
0.78686 |
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| Standard Error |
0.501071 |
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| Observations |
19 |
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| ANOVA |
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df |
SS |
MS |
F |
Significance F |
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| Regression |
3 |
17.43737 |
5.812455 |
23.15051 |
7E-06 |
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| Residual |
15 |
3.766087 |
0.251072 |
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| Total |
18 |
21.20345 |
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Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
| Intercept |
-2.96382 |
0.683133 |
-4.33856 |
0.000585 |
-4.41988 |
-1.50775 |
| HSGPA |
0.024894 |
0.20413 |
0.121952 |
0.904556 |
-0.4102 |
0.459986 |
| SAT |
0.003718 |
0.00074 |
5.027318 |
0.00015 |
0.002142 |
0.005294 |
| REF |
0.19632 |
0.108629 |
1.807251 |
0.090819 |
-0.03522 |
0.427857 |
a ) the model is
CGPA = -2.964 + 0.0249*HSGPA +0.00372*SAT +0.19632*REF
b ) since p value of f stat is 0.000 which is less than 0.05 so
we conclude that model is significant to use
c) variable HSGPA and REF is not good predictor because their p
value is greater than 0.05
