Factors Determining the Price Of Used Mid Compact Size Vehicles Team 4 INTRODUCTION What Used least squares regression analysis to determine the factors that affect mid compact size vehicle price Why By determining these factors manufacturers dealerships rental agencies and consumers can incorporate these economic indicators into their decision making processes and operations How Identified and defined dependent variable Price of Mid Compact Size Cars Collected sufficient data on potential indicators independent variables Developed regression model by considering different model types and variable interactions Diagnosed and refined model taking into consideration performance parameters Independent Variables Supply Used cars available in a particular month Fleet Percentage of supply of vehicles sold to public agencies police department government offices Lease Percentage of total supply of cars leased Incentives Rebates APR etc dollar value PI Monthly National Personal Income in Billions of dollars Month Month in which Price was recorded Year Year in which Price was recorded Single Variable Regressions Y 0557x 6046 7 R 2 1743 Y 4850 4 4x 6088 3 R 2 2421 Y 4031 1x 7730 3 R 2 3143 Y 7584x 7895 1 R 2 3742 Y 69 184x 7326 R 2 0491 Y 98 334x 7136 3 R 2 0163 Correlation Matrix PRICE SUPPLY MONTH YEAR LEASE INCENTIVE FLEET PRICE 1 0000 0 4175 0 2216 0 1278 0 4921 0 6117 0 5606 SUPPLY 0 4175 1 0000 0 1115 0 0869 0 0825 0 4844 0 3933 MONTH 0 2216 0 1115 1 0000 0 2223 0 0368 0 1127 0 1625 YEAR 0 1278 0 0869 0 2223 1 0000 0 1179 0 0332 0 0661 LEASE 0 4921 0 0825 0 0368 0 1179 1 0000 0 0422 0 1142 INCENTIVE 0 6117 0 4844 0 1127 0 0332 0 0422 1 0000 0 5116 FLEET 0 5606 0 3933 0 1625 0 0661 0 1142 0 5116 1 0000 By looking at the Correlation Matrix we see some fairly high correlations between independent variables and that indicates a potential problem with multicollinearity Developing Models EQ 1 Variable Coefficient Std Error t Statistic SUPPLY 0 0200 0 0075 2 6817 0 0083 2146 8892 425 2621 5 0484 0 0000 0 4330 0 0761 5 6936 0 0000 4240 7041 474 7231 8 9330 0 0000 96 6401 25 9345 3 7263 0 0003 458 4911 297 1605 1 5429 0 1253 PI 0 8325 0 6548 1 2714 0 2059 C 2494 8234 4111 2844 0 6068 0 5451 FLEET INCENTIVE LEASE MONTH YEAR Prob R squared 0 7221 Mean dependent var 6833 9815 Adjusted R squared 0 7068 S D dependent var 1028 1981 556 7549 Akaike info criterion 15 5396 Schwarz criterion 15 7117 F statistic 47 1450 S E of regression Sum squared resid Log likelihood Durbin Watson stat 39366959 4514 1040 9202 0 6384 Prob F statistic 0 0000 Developing Models EQ 2 Variable Coefficient Std Error t Statistic SUPPLY 0 0208 0 0075 2 7853 0 0062 2231 5649 421 0242 5 3003 0 0000 0 4093 0 0739 5 5381 0 0000 4281 7972 474 7604 9 0189 0 0000 MONTH 70 4489 15 7922 4 4610 0 0000 YEAR 83 7062 37 5382 2 2299 0 0275 7709 2045 285 1369 27 0369 0 0000 FLEET INCENTIVE LEASE C Prob R squared 0 7186 Mean dependent var 6833 9815 Adjusted R squared 0 7054 S D dependent var 1028 1981 558 0938 Akaike info criterion 15 5374 Schwarz criterion 15 6880 F statistic 54 4708 S E of regression Sum squared resid Log likelihood Durbin Watson stat 39867988 1607 1041 7738 0 6253 Prob F statistic 0 0000 Testing Variable Interactions Price Vs Year Supply 10000 9000 Y 0071x 6517 7 8000 price 7000 6000 R 2 0548 5000 4000 3000 2000 1000 0 0 20000 40000 60000 80000 100000 120000 140000 160000 Year Supply Price Vs Incentive Fleet Y 1 8835x 7533 3 R 2 3377 Developing Models EQ 3 Variable Coefficient Std Error t Statistic SUPPLY 0 0680 0 0158 4 2942 0 0000 MONTH 66 2268 13 9278 4 7550 0 0000 YEAR 477 4664 71 9577 6 6354 0 0000 LEASE 5719 1001 478 8719 11 9429 0 0000 0 4027 0 0651 6 1848 0 0000 2100 0570 371 4849 5 6531 0 0000 0 0289 0 0047 6 1611 0 0000 8602 7106 290 0318 29 6613 0 0000 INCENTIVE FLEET YEAR SUPPLY C Prob R squared 0 783333561 Mean dependent var 6833 981481 Adjusted R squared 0 771391317 S D dependent var 1028 198109 S E of regression 491 6127776 Akaike info criterion 15 29069057 Sum squared resid 30693756 64 Schwarz criterion Log likelihood Durbin Watson stat 1024 121613 0 867002592 F statistic Prob F statistic 15 462855 65 59349469 0 Final Model Variable Coefficient Std Error t Statistic SUPPLY 0 0642 0 0145 4 4258 0 0000 MONTH 57 8940 12 8331 4 5113 0 0000 YEAR 498 9848 65 8981 7 5721 0 0000 LEASE 5495 1686 439 8410 12 4935 0 0000 0 9477 0 1222 7 7527 0 0000 4523 0306 583 6174 7 7500 0 0000 YEAR SUPPLY 0 0284 0 0043 6 6105 0 0000 INCENTIVE FLEET 2 3145 0 4534 5 1042 0 0000 9055 9077 279 5399 32 3958 0 0000 INCENTIVE FLEET C Prob R squared 0 8205 Mean dependent var 6833 9815 Adjusted R squared 0 8091 S D dependent var 1028 1981 449 2916 Akaike info criterion 15 1176 Schwarz criterion 15 3112 F statistic 71 9727 S E of regression Sum squared resid Log likelihood Durbin Watson stat 25434734 6948 1011 4354 0 9421 Prob F statistic 0 0000 Diagnostics Final Equation PRICE 0 06417398414 SUPPLY 57 89403046 MONTH 498 984817 YEAR 5495 168601 LEASE 0 9477265548 INCENTIVE 4523 030592 FLEET 0 02838232606 YEAR SUPPLY 2 314465082 INCENTIVE FLEET 9055 90772 dPrice dSupply 064 028 Year dPrice dIncentive 9477 2 31 Fleet dPrice dMonth 57 89 dPrice dFleet 4523 03 2 31 Incentive dPrice dYear 498 98 028 Supply dPrice dLease 5495 17 Conclusions The month and lease variables have the most significant impact on price The effect of incentives on price cannot be considered without looking at fleet The effect of supply on price also cannot be considered without looking at year An informed buyer or seller of midcompact sized vehicles should consider these implications before acting
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