Part (b) Interpret the estimated value of the coefficient on the “Gasoline dummy

Question

Part (b)

Interpret the estimated value of the coefficient on the “Gasoline dummy” variable, i.e., explain what the number means in this regression.

Part (c)   

What is the estimated average effect of the miles driven per month on the monthly maintenance cost for buses with Diesel engines?

Part (d)

What is the estimated average effect of the miles driven per month on the monthly maintenance cost for buses with gasoline engines?

Part (e)

What share of the variation in the “Maintenance per month” variable can be explained by this regression?

SUMMARY OUTPUT Regression Statistics Multiple R 0.63750955 R Square Adjusted RSq 0.38861098 Standard Erro 45.8375958 Observations 0.40641842 104 ANOVA MS Significance F Regression Residual Total 3 143858.866 47952.9553 22.8229468 2.4534E-11 100 210108.519 2101.08519 103 353967.385 Coefficients StandardErrot Stat 80.9244942 109.296831 0.740410260.460786135.9173 297.766293135.9173297.766293 P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept Miles per 0.4632852 0.12995732 3.56490271 0.00056001 0.20545358 0.72111681 0.20545358 0.72111681 Gasoline dum 324.64698 149.445733 -2.1723402 0.03219301 621.14305 -28.150899 -621.1430s 28.150899 Interaction te 0.35581472 0.17808462 1.99800924 0.04843122 0.0024999 0.70912953 0.0024999 0.70912953

Explanation / Answer

Answers to the question is as follows:

b. If Gasoline dummy = 0 means that it' a diseal engines it means that maintainance cost is higher by $324.647 compared to maintainance cost of engines with Gasoline engines

c. If Gasoline dummy, if 1, means that on an average the montly maintaince cost of buses decreases by $324.647 , compared to diseal engines. I am guessing the Gasoline dummy stands for if a bus is a Gasoline bus or not

c. Per increase in the miles per month driven the maintaince cost increases by .4633 units.

d. Rsquare gives this share of variation in "Maintaince per month" that can be explained by this regression. Since Rsquare is .4064 , it means that 40.64% of total variation in the in "Maintaince per month" can be explained by this regression.

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