The manager of a commuter rail transportation system is asked by his governing board to predict the demand for rides in the large city served by the transportation network. The system manager has collected data on variables thought to be related to the number of weekly riders (measured in thousands) on the city's rail system, including "population" (measured in thousands), "price per ride" (measured in dollars), "income" (measured in dollars), and "parking rate" (measured in dollars). The regression analysis results are as shown below. Answer the following questions:

(1) Common on theoverallsignificance, i.e., the relationship between the current set of explanatory variables and the dependent variable. And justify your answer. (2 points)

(2) Using a 1% significance level, comment on the relationship betweeneachindividual explanatory variable and the dependent variable. Is there any explanatory that should be excluded from the current regression model? And why? (3 points)

SUMMARY OUTPUT Regression Statistics Multiple R 0.962622354 R Square 0.926641797 Adjusted R Square 0.913303942 Standard Error 27.65834468 Observations 27 ANOVA df SS MS F Significance F Regression 4 212587.76 53146.94 69.474574 3.706E-12 Residual 22 16829.649 764.98403 Total 26 229417.41 Coefficients Standard Errol t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 1064.705865 134.60344 7.9099455 7.12E-08 785.55542 1343.8563 785.55542 1343.8563 Price per Ride -141.0772912 62.200911 -2.2680904 0.0334874 -270.07408 -12.080498 -270.07408 -12.080498 Population 0.105096562 0.0662129 1.5872527 0.1267251 -0.0322205 0.2424137 -0.0322205 0.2424137 Income -0.057608177 0.0132 -4.3642535 0.0002478 -0.0849833 -0.030233 -0.0849833 -0.030233 Parking Rate 188.2080487 52.888273 3.5585969 0.0017583 78.524483 297.89161 78.524483 297.89161