Factors Determining the Price Of Used Mid-Compact Size VehiclesINTRODUCTIONSlide 3Independent VariablesSingle Variable RegressionsSlide 6Correlation MatrixDeveloping Models (EQ 1)Developing Models (EQ 2)Testing Variable InteractionsDeveloping Models (EQ 3)Final ModelDiagnosticsFinal EquationConclusionsFactors Determining the Factors Determining the Price Of Used Mid-Compact Price Of Used Mid-Compact Size VehiclesSize VehiclesTeam 4Team 4INTRODUCTIONINTRODUCTIONUsed least squares regression analysis to Used least squares regression analysis to determine the factors that affect mid-compact determine the factors that affect mid-compact size vehicle price.size vehicle price.By determining these factors manufacturers, By determining these factors manufacturers, dealerships, rental agencies, and consumers dealerships, rental agencies, and consumers can incorporate these economic indicators into can incorporate these economic indicators into their decision making processes and operationstheir decision making processes and operationsWhat?Why?Identified and defined dependent variable Identified and defined dependent variable (Price of Mid-Compact Size Cars) (Price of Mid-Compact Size Cars)Collected sufficient data on potential Collected sufficient data on potential indicators/independent variablesindicators/independent variablesDeveloped regression model by considering Developed regression model by considering different model types and variable different model types and variable interactionsinteractionsDiagnosed and refined model taking into Diagnosed and refined model taking into consideration performance parametersconsideration performance parametersHow?Independent VariablesIndependent VariablesSupplySupply - - Used cars available in a particular monthUsed cars available in a particular monthFleetFleet - Percentage of supply of vehicles sold to public - Percentage of supply of vehicles sold to public agencies (police department, government offices)agencies (police department, government offices)LeaseLease - Percentage of total supply of cars leased.- Percentage of total supply of cars leased.IncentivesIncentives - Rebates, APR, etc. dollar value($) - Rebates, APR, etc. dollar value($)PIPI- - MonthlyMonthly National Personal Income in National Personal Income in Billions of dollarsBillions of dollarsMonthMonth - - Month in which Price was recorded. Month in which Price was recorded.YearYear - -Year in which Price was recorded.Year in which Price was recorded.Single Variable RegressionsSingle Variable RegressionsY=.0557x+6046.7 R^2=.1743 Y=-4031.1x+7730.3 R^2=.3143Y=4850.4.4x+6088.3 R^2=.2421Y=-.7584x+7895.1 R^2=.3742Y=-69.184x+7326R^2=.0491Y=-98.334x+7136.3R^2=.0163PRICE SUPPLY MONTH YEAR LEASE INCENTIVE FLEETPRICE 1.0000 0.4175 -0.2216 -0.1278 0.4921 -0.6117 -0.5606SUPPLY 0.4175 1.0000 -0.1115 0.0869 -0.0825 -0.4844 -0.3933MONTH -0.2216 -0.1115 1.0000 -0.2223 -0.0368 0.1127 -0.1625YEAR -0.1278 0.0869 -0.2223 1.0000 -0.1179 0.0332 0.0661LEASE 0.4921 -0.0825 -0.0368 -0.1179 1.0000 -0.0422 -0.1142INCENTIVE -0.6117 -0.4844 0.1127 0.0332 -0.0422 1.0000 0.5116FLEET -0.5606 -0.3933 -0.1625 0.0661 -0.1142 0.5116 1.0000Correlation MatrixCorrelation MatrixBy looking at the Correlation Matrix we see some fairly high By looking at the Correlation Matrix we see some fairly high correlations between independent variables and that indicates a correlations between independent variables and that indicates a potential problem with multicollinearitypotential problem with multicollinearityDeveloping ModelsDeveloping Models (EQ 1) (EQ 1)R-squared 0.7221 Mean dependent var 6833.9815Adjusted R-squared 0.7068 S.D. dependent var 1028.1981S.E. of regression 556.7549 Akaike info criterion 15.5396Sum squared resid 39366959.4514 Schwarz criterion 15.7117Log likelihood -1040.9202 F-statistic 47.1450Durbin-Watson stat 0.6384 Prob(F-statistic) 0.0000Variable Coefficient Std. Error t-Statistic Prob. SUPPLY 0.0200 0.0075 2.6817 0.0083FLEET -2146.8892 425.2621 -5.0484 0.0000INCENTIVE -0.4330 0.0761 -5.6936 0.0000LEASE 4240.7041 474.7231 8.9330 0.0000MONTH -96.6401 25.9345 -3.7263 0.0003YEAR -458.4911 297.1605 -1.5429 0.1253PI 0.8325 0.6548 1.2714 0.2059C 2494.8234 4111.2844 0.6068 0.5451Developing ModelsDeveloping Models (EQ 2) (EQ 2)Variable Coefficient Std. Error t-Statistic Prob. SUPPLY 0.0208 0.0075 2.7853 0.0062FLEET -2231.5649 421.0242 -5.3003 0.0000INCENTIVE -0.4093 0.0739 -5.5381 0.0000LEASE 4281.7972 474.7604 9.0189 0.0000MONTH -70.4489 15.7922 -4.4610 0.0000YEAR -83.7062 37.5382 -2.2299 0.0275C 7709.2045 285.1369 27.0369 0.0000R-squared 0.7186 Mean dependent var 6833.9815Adjusted R-squared 0.7054 S.D. dependent var 1028.1981S.E. of regression 558.0938 Akaike info criterion 15.5374Sum squared resid 39867988.1607 Schwarz criterion 15.6880Log likelihood -1041.7738 F-statistic 54.4708Durbin-Watson stat 0.6253 Prob(F-statistic) 0.0000Testing Variable InteractionsTesting Variable Interactions0100020003000400050006000700080009000100000 20000 40000 60000 80000 100000 120000 140000 160000Year*SupplypricePrice Vs Year*SupplyPrice Vs Incentive*FleetY=.0071x+6517.7R^2=.0548Y=-1.8835x+7533.3R^2=.3377Developing ModelsDeveloping Models (EQ 3) (EQ 3)Variable Coefficient Std. Error t-Statistic Prob. SUPPLY -0.0680 0.0158 -4.2942 0.0000MONTH -66.2268 13.9278 -4.7550 0.0000YEAR -477.4664 71.9577 -6.6354 0.0000LEASE 5719.1001 478.8719 11.9429 0.0000INCENTIVE -0.4027 0.0651 -6.1848 0.0000FLEET -2100.0570 371.4849 -5.6531 0.0000YEAR*SUPPLY 0.0289 0.0047 6.1611 0.0000C 8602.7106 290.0318 29.6613 0.0000R-squared 0.783333561 Mean dependent var 6833.981481Adjusted R-squared 0.771391317 S.D. dependent var 1028.198109S.E. of regression 491.6127776 Akaike info criterion 15.29069057Sum squared resid 30693756.64 Schwarz criterion 15.462855Log likelihood -1024.121613 F-statistic 65.59349469Durbin-Watson stat 0.867002592 Prob(F-statistic) 0Final ModelFinal ModelVariable Coefficient Std. Error t-Statistic Prob. SUPPLY -0.0642 0.0145 -4.4258 0.0000MONTH -57.8940 12.8331 -4.5113 0.0000YEAR -498.9848 65.8981 -7.5721 0.0000LEASE 5495.1686 439.8410 12.4935 0.0000INCENTIVE -0.9477 0.1222 -7.7527 0.0000FLEET -4523.0306 583.6174 -7.7500 0.0000YEAR*SUPPLY 0.0284 0.0043 6.6105 0.0000INCENTIVE*FLEET 2.3145 0.4534 5.1042 0.0000C 9055.9077 279.5399 32.3958 0.0000R-squared 0.8205 Mean dependent var
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