1. Presented by: Alex Kelson, Ben Sparks, Drew Walsh, Oyebola Akinmulero, Shaghayegh Tareh, Tim Pearson, Tina Tran 1

2. Table of Contents 1. Find a statistical question to answer Located on Page 3 2. Come up with your own hypothesis Located on Page 3 3. Collect data Raw Data Located on Page 4 (Table 1 and Graph 1) 4. Organize and summarize the data Located on Pages 5-6 5. Plot x vs. y and calculate linear correlation coefficient, r Located on Page 7 6. Predicted Y Located on page 8 Residual Located on page 9 7. Check to see if the linear model assumption is valid Located on Page 10 8. Make a few predictions Located on Page 11 9. Afterthought Located on Page 12 Sources located on page 13 2

3. Statistical Analysis Is there a correlation between the miles on a used Toyota Corolla LE and the asking price at a used car dealership? We believe that there is a linear correlation where more miles that a used Toyota Corolla LE has the lower the sticker or asking price will be at the used car dealership. 3

4. Data on Toyota Corolla LE Mileage (X)Price (Y) 37616 $18,326 30695 $17,494 35461 $17,079 45233 $16,941 31727 $15,990 39797 $15,690 45721 $15,690 41454 $15,490 36589 $15,290 39775 $14,877 40547 $14,359 40881 $14,359 40957 $14,359 52171 $14,290 33371 $14,000 34571 $13,998 48691 $13,715 43353 $13,656 36162 $13,577 71259 $12,995 70936 $12,990 38210 $11,995 96495 $10,999 87974 $10,990 115085 $10,596 82190 $10,444 88727 $10,000 96962 $8999 102618 $8995 109529 $7977 4 Table 1Graph 1

5. Dataset X (mileage) ColumnnMeanVarianceStd. Dev.Std. Err.MedianRangeMinMaxQ1Q3 MILEAGE 3057158.5667.2311782E826890.854909.57542403.584390306951150853761682190 5

6. Dataset Y (Price) ColumnNMeanVarianceStd. Dev.Std. Err.MedianRangeMinMaxQ1Q3 PRICE3013545.3337218841.52686.7903490.5385413999103497977183261099915690 6

7. Best Fit Line and Correlation Coefficient Y=-0.0859x +18450 R=.8615683374 MILES PRICEPRICE 7

8. Calculations and Graph for Predicted Y Predicted Y vs X To get predicted Y we plug Observed X from Table 2 into the Best Fit Line Equation of Y=-0.0859x +18450. (Graph 3) 8 Table 2

9. Calculations and Graph for Residual Observed XObserved YPredicted YResidual 3761618326 15218.78563107.2144 3069517494 15813.29951680.7005 3546117079 15403.90011675.0999 4523316941 14564.48532376.5147 3172715990 15724.6507265.3493 3979715690 15031.4377658.5623 4572115690 14522.56611167.4339 4145415490 14889.1014600.8986 3658915290 15307.0049-17.0048999999 3977514877 15033.3275-156.327499999 4054714359 14967.0127-608.012699999 4088114359 14938.3221-579.322099999 4095714359 14931.7937-572.7937 5217114290 13968.5111321.4889 3337114000 15583.4311-1583.4311 3457113998 15480.3511-1482.3511 4869113715 14267.4431-552.4431 4335313656 14725.9773-1069.9773 3616213577 15343.6842-1766.6842 7125912995 12328.8519666.1481 7093612990 12356.5976633.402399999 3821011995 15167.761-3172.761 9649510999 10161.0795837.9205 8797410990 10893.033496.966599999 11508510596 8564.19852031.8015 8219010444 11389.879-945.87900000 8872710000 10828.3507-828.35069999 969628999 10120.9642-1121.9642 1026188995 9635.1138-640.113799999 1095297977 9041.4589-1064.4589 Residual vs. X PRICEPRICE MILES 9 Graph 4 Table 2 Residual To produce the residual we subtract the Observed Y from the Predicted Y in Table 2. This produces Graph 4.

10. Linear Model Assumption Due to the fact that there is a linear correlation using the best fit line model we have met the criteria for section A on #7 Since there was no discernible pattern in the graph of “Residual vs X” (found on Page 8), the data is linearly related. Since we have used 30 points in our data set and our correlation coefficient or R =.8615 this is much higher than the required.361 we have found a strong positive correlation. We have concluded that using the data from cars.com, a strong correlation between the miles on a used vehicle will lower the sticker/asking price of a Toyota Corolla LE. 10

11. Predictions Using Equation To get predictions we come up with X that is within the ranges of our data and plug it into the Best Fit Line Equation of Y=-0.0859x +18450 XY 3000015873 3500015443.5 4000015014 4500014584.5 5000014155 5500013725.5 6250013081.25 7000012437 7500012007.5 8000011578 8500011148.5 9000010719 9500010289.5 1000009860 Predicted using Equation 11 Graph 5 Table 3

12. After Thought Did you just do a convenience or voluntary-response sampling to collect your data? The sampling data was convenience based. We found it through an internet search. Did your study suffer from too few data points? We don’t believe the study suffered from too few data points, it points out clearly that car value decreases with increased mileage. Are you misrepresenting the data? No we are not misrepresenting the data. Is your analysis correct? The analysis is correct. Does your conclusion make sense? Yes our conclusion makes sense. We believe that the study was useful for our reader. However, most of this knowledge is considered common sense. We think it would have been helpful to research a variety of cars and compare resale value based on mileage. This study could have more value by comparing upgrades vs. stock resale value and this could be done for multiple makes and models. This could influence the reader’s decision between makes and models and the upgrades purchased depending on the added resale value. 12

13. Sources All statistical data was acquired using the Cars.com search engine for “Used Toyota Corolla LE” search grid within 10 miles of 84070 accessed on 11/14/11 http://www.cars.com/for-sale/used/toyota/corolla/le/_/N- ma9Zfi0Zg3hZinqZm5d?sf1Dir=DESC&mkId=20088&mdId=20861&rd=10&zc=84070&PMmt=1-1- 0&stkTypId=28881&sf2Dir=ASC&sf1Nm=price&sf2Nm=miles&rpp=50&feedSegId=28705&searchSource =GN_REFINEMENT&crSrtFlds=stkTypId-feedSegId-mkId-mdId-trId&pgId=2102&trId=24182 13