1. Factors Determining the Price Of Used Mid- Compact Size Vehicles Team 4

2. INTRODUCTION Used least squares regression analysis to determine the factors that affect mid-compact size vehicle price. By determining these factors manufacturers, dealerships, rental agencies, and consumers can incorporate these economic indicators into their decision making processes and operations What? Why?

3. 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 How?

4. Independent Variables Supply - Used cars available in a particular month Supply - Used cars available in a particular month Fleet - Percentage of supply of vehicles sold to public agencies (police department, government offices) Fleet - Percentage of supply of vehicles sold to public agencies (police department, government offices) Lease - Percentage of total supply of cars leased. Lease - Percentage of total supply of cars leased. Incentives - Rebates, APR, etc. dollar value($) Incentives - Rebates, APR, etc. dollar value($) PI- Monthly National Personal Income in Billions of dollars PI- Monthly National Personal Income in Billions of dollars Month - Month in which Price was recorded. Month - Month in which Price was recorded. Year -Year in which Price was recorded. Year -Year in which Price was recorded.

5. Single Variable Regressions Y=.0557x+6046.7 R^2=.1743Y=-4031.1x+7730.3 R^2=.3143 Y=4850.4.4x+6088.3 R^2=.2421 Y=-.7584x+7895.1 R^2=.3742

6. Y=-69.184x+7326 R^2=.0491 Y=-98.334x+7136.3 R^2=.0163

7. PRICESUPPLYMONTHYEARLEASEINCENTIVEFLEET PRICE1.00000.4175-0.2216-0.12780.4921-0.6117-0.5606 SUPPLY0.41751.0000-0.11150.0869-0.0825-0.4844-0.3933 MONTH-0.2216-0.11151.0000-0.2223-0.03680.1127-0.1625 YEAR-0.12780.0869-0.22231.0000-0.11790.03320.0661 LEASE0.4921-0.0825-0.0368-0.11791.0000-0.0422-0.1142 INCENTIVE-0.6117-0.48440.11270.0332-0.04221.00000.5116 FLEET-0.5606-0.3933-0.16250.0661-0.11420.51161.0000 Correlation Matrix By looking at the Correlation Matrix we see some fairly high correlations between independent variables and that indicates a potential problem with multicollinearity

8. Developing Models (EQ 1) R-squared0.7221 Mean dependent var6833.9815 Adjusted R-squared0.7068 S.D. dependent var1028.1981 S.E. of regression556.7549 Akaike info criterion15.5396 Sum squared resid39366959.4514 Schwarz criterion15.7117 Log likelihood-1040.9202 F-statistic47.1450 Durbin-Watson stat0.6384 Prob(F-statistic)0.0000 VariableCoefficientStd. Errort-StatisticProb. SUPPLY0.02000.00752.68170.0083 FLEET-2146.8892425.2621-5.04840.0000 INCENTIVE-0.43300.0761-5.69360.0000 LEASE4240.7041474.72318.93300.0000 MONTH-96.640125.9345-3.72630.0003 YEAR-458.4911297.1605-1.54290.1253 PI0.83250.65481.27140.2059 C2494.82344111.28440.60680.5451

9. Developing Models (EQ 2) VariableCoefficientStd. Errort-StatisticProb. SUPPLY0.02080.00752.78530.0062 FLEET-2231.5649421.0242-5.30030.0000 INCENTIVE-0.40930.0739-5.53810.0000 LEASE4281.7972474.76049.01890.0000 MONTH-70.448915.7922-4.46100.0000 YEAR-83.706237.5382-2.22990.0275 C7709.2045285.136927.03690.0000 R-squared0.7186 Mean dependent var6833.9815 Adjusted R-squared0.7054 S.D. dependent var1028.1981 S.E. of regression558.0938 Akaike info criterion15.5374 Sum squared resid39867988.1607 Schwarz criterion15.6880 Log likelihood-1041.7738 F-statistic54.4708 Durbin-Watson stat0.6253 Prob(F-statistic)0.0000

10. Testing Variable Interactions Price Vs Year*Supply Price Vs Incentive*Fleet Y=.0071x+6517.7 R^2=.0548 Y=-1.8835x+7533.3 R^2=.3377

11. Developing Models (EQ 3) VariableCoefficientStd. Errort-StatisticProb. SUPPLY-0.06800.0158-4.29420.0000 MONTH-66.226813.9278-4.75500.0000 YEAR-477.466471.9577-6.63540.0000 LEASE5719.1001478.871911.94290.0000 INCENTIVE-0.40270.0651-6.18480.0000 FLEET-2100.0570371.4849-5.65310.0000 YEAR*SUPPLY0.02890.00476.16110.0000 C8602.7106290.031829.66130.0000 R-squared0.783333561 Mean dependent var6833.981481 Adjusted R-squared0.771391317 S.D. dependent var1028.198109 S.E. of regression491.6127776 Akaike info criterion15.29069057 Sum squared resid30693756.64 Schwarz criterion15.462855 Log likelihood-1024.121613 F-statistic65.59349469 Durbin-Watson stat0.867002592 Prob(F-statistic)0

12. Final Model VariableCoefficientStd. Errort-StatisticProb. SUPPLY-0.06420.0145-4.42580.0000 MONTH-57.894012.8331-4.51130.0000 YEAR-498.984865.8981-7.57210.0000 LEASE5495.1686439.841012.49350.0000 INCENTIVE-0.94770.1222-7.75270.0000 FLEET-4523.0306583.6174-7.75000.0000 YEAR*SUPPLY0.02840.00436.61050.0000 INCENTIVE*FLEET2.31450.45345.10420.0000 C9055.9077279.539932.39580.0000 R-squared0.8205 Mean dependent var6833.9815 Adjusted R-squared0.8091 S.D. dependent var1028.1981 S.E. of regression449.2916 Akaike info criterion15.1176 Sum squared resid25434734.6948 Schwarz criterion15.3112 Log likelihood-1011.4354 F-statistic71.9727 Durbin-Watson stat0.9421 Prob(F-statistic)0.0000

13. Diagnostics

14. 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/dMonth= -57.89 dPrice/dYear= -498.98 +.028(Supply) dPrice/dLease= 5495.17 dPrice/dIncentive= -.9477+2.31(Fleet) dPrice/dFleet= -4523.03+2.31(Incentive)

15. 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 mid- compact sized vehicles should consider these implications before acting