It is common practice to use multiple regression models in the transportation planning process to predict the number of trips that will be made in any geographic sub area (zone) of a region. trip frequency is generally expressed as a function of variables such as distance of a zone to the central business district (cbd) and number of autos owned within the zone. the following data come from the penn-jersey transportation study. total person linear distance number of autos zone trips (1000s) to the cbd (miles) owned (1000s) 1) 592 1.6 69 2) 317 2.1 41 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 10) 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 total person trips and the independent variables distance to the cbd and number of autos owned. derive the regression equation for the sample data using sas, generating the parameter estimates, standard errors for the estimates of β2 and β3, and the r2.

b) 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 regression). then test the hypotheses that distance and autos owned are individually unrelated to the dependent variable. interpret your results. use α = .01.

c) predict the number of total person trips for a new zone that was 58.4 miles from the cbd and contained 500 owned autos. are these predictions reliable? why or why not?