test

20.17
SUMMARY
OUTPUT
Regression Statistics
Multiple R 0.670148537
R Square 0.449099062
Adjusted R Square 0.431883408
Standard Error 1184.983521
Observations 100

ANOVA

df SS MS F
Significance
F

Regression 3 109891598.6 36630532.86 26.0866682 1.99289E-12
Residual 96 134801850.8 1404185.946
Total 99 244693449.4
Coefficients
Standard
Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept
-
236.2222443 405.6746521 -0.58229481 0.561733216
-
1041.480064 569.0355757
-
1041.480064 569.0355757
TMILE 0.021830996 0.008405271 2.597298214 0.010873883 0.005146664 0.038515327 0.005146664 0.038515327
VTYPE 2978.918129 382.1613928 7.794921687 7.6976E-12 2220.33376 3737.502498 2220.33376 3737.502498
TDAYS 3.360349979 1.39260558 2.412994768 0.017720412 0.596049751 6.124650207 0.596049751 6.124650207
The P-value of F test is less than 5% making the model reliable.
The adjusted R-squared is a modified version of R-squared that has been adjusted for the
number of predictors in the model. The adjusted R-squared increases only if the new term
improves the model more than would be expected by chance. It decreases when a predictor
improves the model by less than expected by chance.
(Source: http://blog.minitab.com/blog/adventures-in-statistics-2/multiple-regession-analysis-use-adjusted-r-
squared-and-predicted-r-squared-to-include-the-correct-number-of-variables)
Here the adjusted R-square has decreased which implies that the inclusion of new term makes a
lesser improvement than expected by chance.
The p-value for all the factors is less than 5%. Hence, we can ascertain with 95% probability that
these factors explain the annual maintenance cost per vehicle. However, the adjusted R-Square
value is 43% which implies this regression model is a fair estimation but is not a very good fit.
http://blog.minitab.com/blog/adventures-in-statistics-2/multiple-regession-analysis-use-adjusted-r-squared-and-predicted-r-squared-to-include-the-correct-number-of-variables
http://blog.minitab.com/blog/adventures-in-statistics-2/multiple-regession-analysis-use-adjusted-r-squared-and-predicted-r-squared-to-include-the-correct-number-of-variables
20.18
SUMMARY
OUTPUT
Regression Statistics
Multiple R 0.665914132
R Square 0.443441631
Adjusted R Square 0.420724963
Standard Error 129.7799853
Observations 52

ANOVA

df SS MS F
Significance
F
Regression 2 657562.8461 328781.423 19.52054009 5.8212E-07
Residual 49 825299.3847 16842.84459
Total 51 1482862.231
Coefficients
Standard
Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 1383.655748 88.44994518 15.64337598 1.09188E-20 1205.908929...