In: Math
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Hello Sir/ Mam
Basic Multiple Regression Data :
Other regression data, may be less useful, but still as you asked for all the regression data :
SUMMARY OUTPUT | ||||||||
Regression Statistics | ||||||||
Multiple R | 0.97049613 | |||||||
R Square | 0.941862738 | |||||||
Adjusted R Square | 0.903104563 | |||||||
Standard Error | 1.702108569 | |||||||
Observations | 6 | |||||||
ANOVA | ||||||||
df | SS | MS | F | Significance F | ||||
Regression | 2 | 140.8084793 | 70.40423963 | 24.30100843 | 0.014017865 | |||
Residual | 3 | 8.691520743 | 2.897173581 | |||||
Total | 5 | 149.5 | ||||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
Intercept | 6.931902341 | 1.554858303 | 4.458221258 | 0.021010121 | 1.983649278 | 11.8801554 | 1.983649278 | 11.8801554 |
Performance Rate | 1.054352602 | 0.245886285 | 4.28796833 | 0.023313461 | 0.271832703 | 1.8368725 | 0.271832703 | 1.8368725 |
# of Training Courses | 0.616017071 | 0.173737082 | 3.545685601 | 0.03820818 | 0.063108135 | 1.168926008 | 0.063108135 | 1.168926008 |
RESIDUAL OUTPUT | PROBABILITY OUTPUT | |||||||
Observation | Predicted Wage Rate | Residuals | Standard Residuals | Percentile | Wage Rate | |||
1 | 10.09496015 | -0.094960146 | -0.072024208 | 8.333333333 | 10 | |||
2 | 11.0663403 | 0.9336597 | 0.708150768 | 25 | 12 | |||
3 | 15.98274022 | -0.982740225 | -0.745376762 | 41.66666667 | 15 | |||
4 | 17.13180192 | -0.131801921 | -0.099967506 | 58.33333333 | 17 | |||
5 | 21.70457541 | -1.70457541 | -1.292865468 | 75 | 20 | |||
6 | 23.019582 | 1.980418 | 1.502083176 | 91.66666667 | 25 |
I hope this solves your doubt.
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