Finding the Best Car Value Problem? When trying to decide what car to buy, real
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Question
Finding the Best Car Value Problem?
When trying to decide what car to buy, real value is not necessarily determined by how much you spend on the initial purchase. Instead, cars that are reliable and don’t cost much to own often represent the best values. But, no matter how reliable or inexpensive a car may cost to own, it must also perform well. To measure value, consumer reports developed a statistic referred to as a value score. The value score is based upon- five-year owner costs, overall road-test scores, and predicted reliability ratings. Five-year owners costs are based on the expenses incurred in the first five years of ownership, including depreciation, fuel maintenance and repairs, and so on. Using a national average of 12,000 miles per year, an average cost per mile driven is used as the measure of five-year owner costs. Road-test scores are the result of more than 50 tests and evaluation and are based upon a 100-point scale, with higher scores indicating better performance, comfort, convenience, and fuel economy. Predicted-reliability ratings (1=poor, 2=fair, 3=good, 4=very good, and 5=excellent) are based on data from consumer reports’ annual auto survey. A car with a value score of 1.0 is considered to be “average value.” A car with a score of 2.0 is considered to be twice as good as a value as a car with a value score of 1.0; a car with value score of 0.5 is considered half as good as average: and so on. The data for 20 family sedans, including the price ($) of each car is given in the Excel file Family Sedans.
Use regression analysis to develop an estimated regression equation that could be used to predict the value score given the road-test score.
Use regression analysis to develop an estimated regression equation that could be used to predict the value score given the predicted-reliability.
What conclusions can you derive from your analysis? And Which is the Best Car Value?
Car
Price ($)
Cost/Mile
Road-Test Score
Predicted Reliability
Value Score
Nissan Altima 2.5 S (4-cyl.)
23,970
0.59
91
4
1.75
Kia Optima LX (2.4)
21,885
0.58
81
4
1.73
Subaru Legacy 2.5i Premium
23,830
0.59
83
4
1.73
Ford Fusion Hybrid
32,360
0.63
84
5
1.70
Honda Accord LX-P (4-cyl.)
23,730
0.56
80
4
1.62
Mazda6 i Sport (4-cyl.)
22,035
0.58
73
4
1.60
Hyundai Sonata GLS (2.4)
21,800
0.56
89
3
1.58
Ford Fusion SE (4-cyl.)
23,625
0.57
76
4
1.55
Chevrolet Malibu LT (4-cyl.)
24,115
0.57
74
3
1.48
Kia Optima SX (2.0T)
29,050
0.72
84
4
1.43
Ford Fusion SEL (V6)
28,400
0.67
80
4
1.42
Nissan Altima 3.5 SR (V6)
30,335
0.69
93
4
1.42
Hyundai Sonata Limited (2.0T)
28,090
0.66
89
3
1.39
Honda Accord EX-L (V6)
28,695
0.67
90
3
1.36
Mazda6 s Grand Touring (V6)
30,790
0.74
81
4
1.34
Ford Fusion SEL (V6, AWD)
30,055
0.71
75
4
1.32
Subaru Legacy 3.6R Limited
30,094
0.71
88
3
1.29
Chevrolet Malibu LTZ (V6)
28,045
0.67
83
3
1.20
Chrysler 200 Limited (V6)
27,825
0.70
52
5
1.20
Chevrolet Impala LT (3.6)
28,995
0.67
63
3
1.05
Car
Price ($)
Cost/Mile
Road-Test Score
Predicted Reliability
Value Score
Nissan Altima 2.5 S (4-cyl.)
23,970
0.59
91
4
1.75
Kia Optima LX (2.4)
21,885
0.58
81
4
1.73
Subaru Legacy 2.5i Premium
23,830
0.59
83
4
1.73
Ford Fusion Hybrid
32,360
0.63
84
5
1.70
Honda Accord LX-P (4-cyl.)
23,730
0.56
80
4
1.62
Mazda6 i Sport (4-cyl.)
22,035
0.58
73
4
1.60
Hyundai Sonata GLS (2.4)
21,800
0.56
89
3
1.58
Ford Fusion SE (4-cyl.)
23,625
0.57
76
4
1.55
Chevrolet Malibu LT (4-cyl.)
24,115
0.57
74
3
1.48
Kia Optima SX (2.0T)
29,050
0.72
84
4
1.43
Ford Fusion SEL (V6)
28,400
0.67
80
4
1.42
Nissan Altima 3.5 SR (V6)
30,335
0.69
93
4
1.42
Hyundai Sonata Limited (2.0T)
28,090
0.66
89
3
1.39
Honda Accord EX-L (V6)
28,695
0.67
90
3
1.36
Mazda6 s Grand Touring (V6)
30,790
0.74
81
4
1.34
Ford Fusion SEL (V6, AWD)
30,055
0.71
75
4
1.32
Subaru Legacy 3.6R Limited
30,094
0.71
88
3
1.29
Chevrolet Malibu LTZ (V6)
28,045
0.67
83
3
1.20
Chrysler 200 Limited (V6)
27,825
0.70
52
5
1.20
Chevrolet Impala LT (3.6)
28,995
0.67
63
3
1.05
Explanation / Answer
The regresion equation is given by y=a+bx
by using the excel formula of slope and intercept the regression equation is for the given test score
y=0.7978+0.0082x
and for given predicted value is
y =1.051548+0.108387
we can see that predicted reliabity gives the better score value than the
road test score based on the slope value
y=0.7978+0.0082x
and for given predicted value is
y =1.051548+0.108387
we can see that predicted reliabity gives the better score value than the
road test score based on the slope value
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