1. It is common to use multiple regression in the transportation planning process to predict the number of trips that will be made in any geographic area (zone) of a region. Trip frequency is generally expressed as a function of distance of a zone to the Central Business District (CB-D] and number of autos owned within the zone. The following data come from the Penn-Jersey Transportation Study. Use or = .01. (22 pts total] Total Person Linear Distance Number ofAutos Zone Trips 5100051 to the CBD iMilesl Owned 1100051 1 592 1.6 69 2 317 2.1 11 3 135 3.9 22 4 478 3.0 60 5 262 4.3 38 6 82 6.1 15 7 392 5.9 64 8 456 6.0 67 9 88 9.8 13 It] 136 6.1 22 11 39 9.1 8 a. To carry out the forecasting procedure, a regression model is used to determine the relationship between the dependent variable totoiperson trips and the independent variables distance to the CBD and number ofoutos owned. Derive the regression equation for the sample data using R, generating the parameter estimates, standard errors for the estimates of B; and [33, and the R2 and adjusted R]. b. Interpret the parameter estimates in the regression equation, the R2, the adjusted R2, the standard error of the regression, the coefficient of variation, and the standardized beta values. c. Generate and interpret two scattergrams that include the bivariate regression line, one for rotorl person trips and distance, and one for toto.' person trips and autos owned. Include appropriate descriptive labels. d. Test the hypothesis that there is no relationship between the dependent variable and the independent variables taken jointly [i.e., test the significance of the regressioni. Then test the hypotheses that distance and euros owned are each unrelated to the dependent variable. Interpret your results. e. Generate and interpret the diagnostic plots for the regression and indicate whether any issues or problems are present. If an issue or problem is present, describe it and identify the possible causes that might contribute to its shape. f. Predict the number of total person trips that would occur if the number of autos owned in zone 9 increased from 13 thousand to 15 thousand. Then predict total person trips for a new zone that was 8.2 miles from the CBD and contained 13 thousand owned autos. [Note that you do NOT need to rerun the regression analysis. Simply use the parameter estimates derived above and plug in the indicated values of the independent variables, being consistent in units of measurement.) Are these predictions reliable? Whyorwhynot? g. Predict the number of total person trips for a new zone that was 62.4 miles from the CBD and contained 600 owned autos. Are these predictions reliable? Why or why not