Download the data file "TRUCKING.txt"DATA.

The research problem is to model the price charged for trucking service in Florida. In the early 1980s, several states removed regulatory constraints on the rate charged for intrastate trucking services, Florida being the first one to embark on a deregulation policy. The objective of the regression analysis is twofold: (1) assess the impact of deregulation on the prices charged for trucking service in Florida, and (2) estimate a model of supply price fore predicting future prices.The data (n = 134) were obtained from a particular carrier whose trucks originated from either the city of Jacksonville or Miami. The dependent variable of interest is the price in dollars charged per ton-mile. The potential predictors are:

Use the following prompts to build your model.Note: (1) The suggested R commands are not necessarily the complete command for you to just copy and paste. It's just a hint.Use the help file to find out how to call these functions in the correct way.

Import data from the .txt file into R. Tryread.table()or use the drop-down menu. If usingread.table(), remember to add the argument header=TRUE to override the default. Usedim()to check the number of rows in the dataset is equal to the correct sample size.

Usehead()andstr()to learn about the types and codings of the variables. Have all categorical variables been read in correctly as the "factor" type?

Usecor()andpairs()to check the marginal relation among the quantitative variables. What do you see?

Fit the initial "full" model. What is your full model?

Usevif()to check multicollinearity in the initial "full" model. Do the VIF values suggest you remove some of the predictors? Are these findings consistent with your findings from question 3?

Refit a new model after removing predictor(s) as suggested above. UseresidualPlots()to check the residual distribution of the fitted model. What do you see? Does a transformation seem to be needed?

If deemed necessary, conduct a transformation and refit the model. Which transformation did you choose?

Use thestep()to perform automated stepwise selection. Write down the best subset model? Also try Backward and forward selection usingstep()function. Do you see a difference in the results?

Try adding interaction terms and perform the automated stepwise selection again. Which interaction model did you arrive at?

On the final interaction model, perform a diagnostic analysis. What are your findings and conclusion?

Interpret and evaluate your final model.

trucking.txt file:

PRICPTM DISTANCE WEIGHT PCTLOAD ORIGIN MARKET DEREG CARRIER PRODUCT LNPRICE

19942 3.60 7.50 32.6 MIA LARGE YES B 100 9.9006

112162 0.25 7.50 32.6 MIA LARGE YES B 100 11.6277

72973 0.25 15.00 65.2 MIA LARGE YES B 100 11.1978

41892 0.25 24.00 100.0 MIA LARGE YES B 100 10.6428

23519 2.60 7.50 32.6 MIA LARGE YES B 100 10.0656

58221 1.50 0.25 1.1 MIA SMALL YES B 100 10.9720

25725 4.80 0.25 1.1 MIA LARGE YES B 100 10.1552

17103 4.80 7.50 32.6 MIA LARGE YES B 100 9.7470

5884 6.00 24.00 100.0 MIA SMALL YES B 100 8.6800

35079 3.00 0.25 1.1 JAX SMALL YES B 100 10.4654

33671 3.00 0.75 3.3 JAX SMALL YES B 100 10.4244

26408 3.00 3.00 13.0 JAX SMALL YES B 100 10.1814

21734 3.00 7.50 32.6 JAX SMALL YES B 100 9.9866

8390 3.00 24.00 100.0 JAX SMALL YES B 100 9.0348

37063 1.70 3.00 13.0 JAX LARGE YES B 100 10.5204

11328 1.70 24.00 100.0 JAX LARGE YES B 100 9.3350

25118 2.30 7.50 32.6 JAX SMALL YES B 100 10.1313

41988 2.10 0.75 3.3 JAX LARGE YES B 100 10.6451

32416 2.10 3.00 13.0 JAX LARGE YES B 100 10.3864

7789 3.60 24.00 100.0 MIA LARGE YES B 150 8.9605

215541 0.25 0.25 1.1 MIA LARGE YES B 150 12.2809

112162 0.25 7.50 32.6 MIA LARGE YES B 150 11.6277

72973 0.25 15.00 65.2 MIA LARGE YES B 150 11.1978

15463 2.60 15.00 65.2 MIA LARGE YES B 150 9.6462

9161 2.60 24.00 100.0 MIA LARGE YES B 150 9.1227

52703 1.50 0.75 3.3 MIA SMALL YES B 150 10.8724

40090 1.50 3.00 13.0 MIA SMALL YES B 150 10.5989

21009 4.80 3.00 13.0 MIA LARGE YES B 150 9.9527

17103 4.80 7.50 32.6 MIA LARGE YES B 150 9.7470

15541 6.00 7.50 32.6 MIA SMALL YES B 150 9.6512

11852 6.00 15.00 65.2 MIA SMALL YES B 150 9.3803

50394 1.80 0.25 1.1 MIA SMALL YES B 150 10.8276

35567 1.80 3.00 13.0 MIA SMALL YES B 150 10.4792

32343 3.40 0.25 1.1 JAX LARGE YES B 150 10.3842

24841 3.40 3.00 13.0 JAX LARGE YES B 150 10.1203

7850 3.40 24.00 100.0 JAX LARGE YES B 150 8.9682

35079 3.00 0.25 1.1 JAX SMALL YES B 150 10.4654

33671 3.00 0.75 3.3 JAX SMALL YES B 150 10.4244

26408 3.00 3.00 13.0 JAX SMALL YES B 150 10.1814

41988 2.10 0.75 3.3 JAX LARGE YES B 150 10.6451

30593 3.60 0.25 1.1 MIA LARGE YES B 200 10.3285

30218 3.60 0.75 3.3 MIA LARGE YES B 200 10.3162

133108 0.25 3.00 13.0 MIA LARGE YES B 200 11.7989

72973 0.25 15.00 65.2 MIA LARGE YES B 200 11.1978

36772 2.60 0.75 3.3 MIA LARGE YES B 200 10.5125

28846 2.60 3.00 13.0 MIA LARGE YES B 200 10.2697

15463 2.60 15.00 65.2 MIA LARGE YES B 200 9.6462

25725 4.80 0.75 3.3 MIA LARGE YES B 200 10.1552

21009 4.80 3.00 13.0 MIA LARGE YES B 200 9.9527

17103 4.80 7.50 32.6 MIA LARGE YES B 200 9.7470

6616 4.80 24.00 100.0 MIA LARGE YES B 200 8.7972

22832 6.00 0.75 3.3 MIA SMALL YES B 200 10.0359

5884 6.00 24.00 100.0 MIA SMALL YES B 200 8.6800

35567 1.80 3.00 13.0 MIA SMALL YES B 200 10.4792

28810 1.80 7.50 32.6 MIA SMALL YES B 200 10.2685

19050 1.80 15.00 65.2 MIA SMALL YES B 200 9.8548

7850 3.40 24.00 100.0 JAX LARGE YES B 200 8.9682

8390 3.00 24.00 100.0 JAX SMALL YES B 200 9.0348

30207 1.70 7.50 32.6 JAX LARGE YES B 200 10.3158

13044 3.60 15.00 65.2 JAX LARGE YES B 200 9.4761

33671 3.00 0.75 3.3 JAX SMALL YES B 200 10.4244

26408 3.00 3.00 13.0 JAX SMALL YES B 200 10.1814

21734 3.00 7.50 32.6 JAX SMALL YES B 200 9.9866

14471 3.00 15.00 65.2 JAX SMALL YES B 200 9.5799

25118 2.30 7.50 32.6 JAX SMALL YES B 200 10.1313

44562 2.10 0.25 1.1 JAX LARGE YES B 200 10.7046

10296 2.10 24.00 100.0 JAX LARGE YES B 200 9.2395

44042 3.60 0.75 3.3 MIA LARGE NO B 100 10.6929

23298 3.60 24.00 100.0 MIA LARGE NO B 100 10.0561

201871 0.25 3.00 13.0 MIA LARGE NO B 100 12.2154

165482 0.25 7.50 32.6 MIA LARGE NO B 100 12.0166

129628 0.25 24.00 100.0 MIA LARGE NO B 100 11.7724

43570 2.60 3.00 13.0 MIA LARGE NO B 100 10.6821

35906 2.60 7.50 32.6 MIA LARGE NO B 100 10.4886

20311 4.80 24.00 100.0 MIA LARGE NO B 100 9.9189

33573 6.00 0.25 1.1 MIA SMALL NO B 100 10.4215

33573 6.00 0.75 3.3 MIA SMALL NO B 100 10.4215

18353 6.00 24.00 100.0 MIA SMALL NO B 100 9.8175

30706 3.40 15.00 65.2 JAX LARGE NO B 100 10.3322

32995 3.00 15.00 65.2 JAX SMALL NO B 100 10.4041

25620 3.00 24.00 100.0 JAX SMALL NO B 100 10.1511

55679 1.70 3.00 13.0 JAX LARGE NO B 100 10.9274

44042 3.60 0.75 3.3 JAX LARGE NO B 100 10.6929

29963 3.60 15.00 65.2 JAX LARGE NO B 100 10.3077

23298 3.60 24.00 100.0 JAX LARGE NO B 100 10.0561

49962 3.00 0.25 1.1 JAX SMALL NO B 100 10.8190

32995 3.00 7.50 32.6 JAX SMALL NO B 100 10.4041

56881 2.30 0.75 3.3 JAX SMALL NO B 100 10.9487

37858 2.30 15.00 65.2 JAX SMALL NO B 100 10.5416

29760 2.30 24.00 100.0 JAX SMALL NO B 100 10.3009

60442 2.10 0.75 3.3 JAX LARGE NO B 100 11.0094

63897 3.60 0.75 3.3 MIA LARGE NO B 150 11.0650

431467 0.25 0.25 1.1 MIA LARGE NO B 150 12.9749

392479 0.25 0.75 3.3 MIA LARGE NO B 150 12.8802

251256 0.25 7.50 32.6 MIA LARGE NO B 150 12.4342

51651 2.60 7.50 32.6 MIA LARGE NO B 150 10.8523

51651 2.60 15.00 65.2 MIA LARGE NO B 150 10.8523

107145 1.50 0.75 3.3 MIA SMALL NO B 150 11.5819

39123 4.80 15.00 65.2 MIA LARGE NO B 150 10.5745

49204 6.00 0.25 1.1 MIA SMALL NO B 150 10.8037

49204 6.00 0.75 3.3 MIA SMALL NO B 150 10.8037

35125 6.00 7.50 32.6 MIA SMALL NO B 150 10.4667

46505 3.40 15.00 65.2 JAX LARGE NO B 150 10.7473

22373 3.40 24.00 100.0 JAX LARGE NO B 150 10.0156

70323 3.00 0.75 3.3 JAX SMALL NO B 150 11.1609

49385 3.00 15.00 65.2 JAX SMALL NO B 150 10.8074

63897 3.60 0.75 3.3 JAX LARGE NO B 150 11.0650

49385 3.00 15.00 65.2 JAX SMALL NO B 150 10.8074

74057 2.10 3.00 13.0 JAX LARGE NO B 150 11.2126

29163 2.10 24.00 100.0 JAX LARGE NO B 150 10.2807

60107 3.60 15.00 65.2 MIA LARGE NO B 200 11.0039

55520 3.60 24.00 100.0 MIA LARGE NO B 200 10.9245

118697 1.50 3.00 13.0 MIA SMALL NO B 200 11.6843

90288 1.50 24.00 100.0 MIA SMALL NO B 200 11.4108

46930 6.00 7.50 32.6 MIA SMALL NO B 200 10.7564

46930 6.00 15.00 65.2 MIA SMALL NO B 200 10.7564

43353 6.00 24.00 100.0 MIA SMALL NO B 200 10.6771

128997 1.80 0.25 1.1 MIA SMALL NO B 200 11.7675

87843 1.80 7.50 32.6 MIA SMALL NO B 200 11.3833

87843 1.80 15.00 65.2 MIA SMALL NO B 200 11.3833

87213 3.40 0.25 1.1 JAX LARGE NO B 200 11.3761

86576 3.40 0.75 3.3 JAX LARGE NO B 200 11.3688

75109 3.40 3.00 13.0 JAX LARGE NO B 200 11.2267

65702 3.00 7.50 32.6 JAX SMALL NO B 200 11.0929

134929 1.70 0.25 1.1 JAX LARGE NO B 200 11.8125

84492 1.70 24.00 100.0 JAX LARGE NO B 200 11.3444

84173 3.60 0.25 1.1 JAX LARGE NO B 200 11.3406

83752 3.60 0.75 3.3 JAX LARGE NO B 200 11.3356

92416 3.00 0.75 3.3 JAX SMALL NO B 200 11.4341

80214 3.00 3.00 13.0 JAX SMALL NO B 200 11.2925

75998 2.30 7.50 32.6 JAX SMALL NO B 200 11.2385

70194 2.30 24.00 100.0 JAX SMALL NO B 200 11.1590

114076 2.10 0.75 3.3 JAX LARGE NO B 200 11.6446

80555 2.10 15.00 65.2 JAX LARGE NO B 200 11.2967