In: Statistics and Probability
Use the advertised prices for a used car of a particular model to create a linear model for the relationship between a car's Year and its Price. Complete parts a through e.
Year Price ($)
1994 11,538
1994 12,333
1994 10,436
1994 11,995
1995 14,567
1995 14,658
1995 13,656
1995 15,199
1996 16,192
1996 17,039
1996 19,074
1997 19,823
1997 19,249
1997 19,242
1997 20,577
a) Find the equation of the regression line.
Price=___ + _____ year
b) Explain the meaning of the slope of the line. Select the correct choice below and fill in the answer box to complete your choice. (Round to the nearest integer as needed.)
A.The slope of ___indicates the Year when the Price of a used car of this model will be $0.
B.The slope of ___ is meaningless and should not be interpreted.
C.The slope indicates that for every one Year increase or one Year newer model, the Price increases by
___.
D.The slope indicates that for every one Year increase or one Year newer model, the Price decreases by
___.
c) Explain the meaning of the intercept of the line. Select the correct choice below and fill in the answer box to complete your choice.
A.The intercept indicates that cars of this model increase in price by ___ per Year.
B.The intercept of ___ indicates the Price at Year 0.
C.The intercept of ____ indicates the Year when the Price of a used car of this model will be $0.
D.The intercept of ____ is meaningless and should not be interpreted.
d) If you want to sell a used car of this particular model from 1997, what price seems appropriate?
e) You have a chance to buy one of two cars. They are about the same age and appear to be in equally good condition. Would you rather buy the one with a positive residual or the one with a negative residual? Explain.
You would rather buy the car with a ____ residual because this indicates that the actual price is ____than the predicted price. This is important because the two cars have the same ____price, since they are the same age and condition, so the one with the lower ____price is a better purchase.
Using Excel<data <megastat<correlation/regression<regression
Here is the output:
Regression Analysis | ||||||
r² | 0.941 | |||||
r | 0.970 | |||||
Std. Error | 845.581 | |||||
n | 15 | |||||
k | 1 | |||||
Dep. Var. | price | |||||
ANOVA table | ||||||
Source | SS | df | MS | F | p-value | |
Regression | 14,69,42,823.7480 | 1 | 14,69,42,823.7480 | 205.51 | 2.41E-09 | |
Residual | 92,95,098.6520 | 13 | 7,15,007.5886 | |||
Total | 15,62,37,922.4000 | 14 | ||||
Regression output | confidence interval | |||||
variables | coefficients | std. error | t (df=13) | p-value | 95% lower | 95% upper |
Intercept | -54,29,552.5541 | 379839.08 | -14.29 | 0.00 | -6250145.00 | -4608960.10 |
Year | 2,728.8142 | 190.3510 | 14.336 | 2.41E-09 | 2,317.5859 | 3,140.0425 |
a)
Price=-5429553 + 2729 year
b)
Option C
The slope indicates that for every one Year increase or one Year newer model, the Price increases by 2729
c) The intercept of -5429553 is meaningless and should not be interpreted.
d) 1997
Price=-5429553 + 2729 year
= -5429553 + 2729 *1997
= 20260
Answer 20260.
e)
You would rather buy the car with a negative residual because this indicates that the actual price is below than the predicted price. This is important because the two cars have the same predicted price, since they are the same age and condition, so the one with the lower actual price is a better purchase.