Question

# Discuss the strength and the significance of your regression model by using R-square and significance F...

Discuss the strength and the significance of your regression model by using R-square and significance F where α = 0.05.

 SUMMARY OUTPUT Regression Statistics Multiple R 0.919011822 R Square 0.844582728 Adjusted R Square 0.834446819 Standard Error 163.953479 Observations 50 ANOVA df SS MS F Significance F Regression 3 6719578.309 2239859.44 83.3257999 1.28754E-18 Residual 46 1236514.191 26880.7433 Total 49 7956092.5 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 21.7335244 114.2095971 0.19029508 0.84991523 -208.158471 251.62552 -208.15847 251.62552 sqft 0.546807448 0.076647924 7.13401509 5.738E-09 0.392523179 0.70109172 0.39252318 0.70109172 age -3.075663237 2.263665569 -1.3587092 0.18086435 -7.632185699 1.48085922 -7.6321857 1.48085922 features 40.54881241 15.65468404 2.59020318 0.0128049 9.0375678 72.060057 9.0375678 72.060057

Multiple Regression

From the given output we can see that R Square is 0.844582728 which means approximately 84% of the total variation in the dependent variable can be explained by the set of independent variables. So the model is good enough strong for prediction.

We see that the significance F is 1.28754E-18 which is smaller than alpha 0.05. It indicates that at 95% level of significance the regression coefficients are significant. Thus we can say that the model is fine and we can continue with this set of independent variables.

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