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The case involves predicting the value score of a car based on the price of the car, five-year cost/mile,
road-test score, and predicted reliability. A car with a value score of 1.0 is considered to be “averagevalue.” A car with a value score of 2.0 is considered to be twice as good a value as a car with a value
score of 1.0; a car with a value score of 0.5 is considered half as good as average; and so on. The data for
20 family sedans, contained in the textbook data file FamilySedans.xlsx, is shown below.
| Car | Price ($) | Cost/Mile | Road-Test Score | Predicted Reliability | Value Score | ||||
| Nissan Altima 2.5 S (4-cyl.) | 23,970.00 | 0.59 | 91 | 4 | 1.75 | ||||
| Kia Optima LX (2.4) | 21,885.00 | 0.58 | 81 | 4 | 1.73 | ||||
| Subaru Legacy 2.5i Premium | 23,830.00 | 0.59 | 83 | 4 | 1.73 | ||||
| Ford Fusion Hybrid | 32,360.00 | 0.63 | 84 | 5 | 1.70 | ||||
| Honda Accord LX-P (4-cyl.) | 23,730.00 | 0.56 | 80 | 4 | 1.62 | ||||
| Mazda6 i Sport (4-cyl.) | 22,035.00 | 0.58 | 73 | 4 | 1.60 | ||||
| Hyundai Sonata GLS (2.4) | 21,800.00 | 0.56 | 89 | 3 | 1.58 | ||||
| Ford Fusion SE (4-cyl.) | 23,625.00 | 0.57 | 76 | 4 | 1.55 | ||||
| Chevrolet Malibu LT (4-cyl.) | 24,115.00 | 0.57 | 74 | 3 | 1.48 | ||||
| Kia Optima SX (2.0T) | 29,050.00 | 0.72 | 84 | 4 | 1.43 | ||||
| Ford Fusion SEL (V6) | 28,400.00 | 0.67 | 80 | 4 | 1.42 | ||||
| Nissan Altima 3.5 SR (V6) | 30,335.00 | 0.69 | 93 | 4 | 1.42 | ||||
| Hyundai Sonata Limited (2.0T) | 28,090.00 | 0.66 | 89 | 3 | 1.39 | ||||
| Honda Accord EX-L (V6) | 28,695.00 | 0.67 | 90 | 3 | 1.36 | ||||
| Mazda6 s Grand Touring (V6) | 30,790.00 | 0.74 | 81 | 4 | 1.34 | ||||
| Ford Fusion SEL (V6, AWD) | 30,055.00 | 0.71 | 75 | 4 | 1.32 | ||||
| Subaru Legacy 3.6R Limited | 30,094.00 | 0.71 | 88 | 3 | 1.29 | ||||
| Chevrolet Malibu LTZ (V6) | 28,045.00 | 0.67 | 83 | 3 | 1.20 | ||||
| Chrysler 200 Limited (V6) | 27,825.00 | 0.70 | 52 | 5 | 1.20 | ||||
| Chevrolet Impala LT (3.6) | 28,995.00 | 0.67 | 63 | 3 | 1.05 |
1. Develop numerical summaries of the data.
Insert a snapshot of the output.
2. Use
value score given the price of the car.
Obtain a
Go to Insert -Graph → Scatterplot…. Select both variables and then click OK.
Insert a snapshot of the scatterplot
Obtain the regression output
Go to Data → Data Analysis→ Regression → In the Regression dialog box, enter Value Score for the y
variable, and Price ($) for the x variable , and then click OK.
Insert a snapshot of the output
Write the equation predicting the value score given the price of the car.
Value Score = 2.358666 - 3.3E-05 * price of car(26000) =
Predict the value score if the price of car=$26000
1.500666
How well does the estimated regression equation fit the data?
R Square = 32.77%
3. Use regression analysis to develop an estimated regression equation that could be used to predict the
value score given the five-year owner costs (cost/mile).
Repeat what you did for #2 above but use Cost/Mile as the independent variable (x)
Insert a snapshot of the output
Write the equation predicting the value score given cost/mile.
2.942219 – 2.31187 * cost/mile(0.6) =
Predict the value score if the cost/mile = 0.6
1.555097
How well does the estimated regression equation fit the data?
R Square = 51.32%
4. Use regression analysis to develop an estimated regression equation that could be used to predict the
value score given the road-test score.
Repeat what you did for #2 above but use Road-Test Score as the independent variable.
Insert a snapshot of the output
Write the equation predicting the value score given the road-test score.
0.7978 + 0.008206 * road-test score(85) =
Predict the value score if the road test score = 85
1.49531
How well does the estimated regression equation fit the data?
R Square = 16.94%
5. Use regression analysis to develop an estimated regression equation that could be used to predict the
value score given the predicted-reliability.
Repeat what you did for #2 above but use Predicted Reliability as the independent variable.
Insert a snapshot of the output
Write the equation predicting the value score given the predicted reliability
1.051548 + 0.108387 * predicted reliability(4) =
Predict the value score if the predicted reliability = 4
1.485096
How well does the estimated regression equation fit the data?
R Square = 12.29%
6. What conclusions can you derive from your analysis?
a) Which one of these four independent variables is "best" for predicting Value Score? Give a
reason for your answer.
b) Predict the value score of the Honda Accord LX-P using the estimated regression equation that
has the independent variable you selected in part a). What is the error of prediction
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