GI 72% D ooooo Sprint LTE 9:39 AM bb.wpunji.edu Part (a) (2 points Using the \"S
ID: 3218245 • Letter: G
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GI 72% D ooooo Sprint LTE 9:39 AM bb.wpunji.edu Part (a) (2 points Using the "School Bus Data.xlsx" file, run a regression with "Maintenance cost per month" as the dependent O)variable and "Age" as the independent (X)variable. You must submit your actual Excel file with the output as part of the assignment. Part (b) (2 points) Explain why it makes sense to claim that there is causation going from "Age" to "Maintenance cost per month, i e, what is the "story" we can tell that explains why changes in the age of buses plausibly have an impact on the buses' maintenance cost. Part (c) (2 points) Interpret the estimated value of the coefficient on "Age," i.e., explain what the number means in this regression. Part (d) (2 points) How high are the predicted maintenance costs for a bus that is 7 years old? Part (e) (2 points) Is the estimate of the coefficient on the "Age" variable statistically significant? Please answer "yes" or "no" and explain how you can tell. Part (f (2 points) What percentage of variation in maintenance cost cannot be explained by variation in the age of the buses? Part (g) (2 points) If instead of regressing "Maintenance cost per month" on "Age" as in Part (a), we regressed "Age" on "Maintenance cost per month" instead, would R-Squared be the same as in the regression in Part (a)? Justify your answer. Part (h) (2 points) If instead of regressing"Maintenance cost per month" on "Age" as in Part (a), we regressed "Age" on "Maintenance cost per month" instead, would the estimate of the slope coefficient be the same as in the regression in Part (a)? Justify your answer. Part (i) (2 points) Would it make sense to claim that there is causation going from "Maintenance cost per month" to "Age," i.e., is there a "story" we can tell that would explain why changes in the maintenance cost of buses would plausibly have an impact on the age of buses. Page 1 of 2Explanation / Answer
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.663697769
R Square
0.440494729
Adjusted R Square
0.432017376
Standard Error
44.67702904
Observations
68
ANOVA
df
SS
MS
F
Significance F
Regression
1
103716.7836
103716.7836
51.9613552
6.89154E-10
Residual
66
131738.437
1996.036924
Total
67
235455.2206
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
362.3907162
14.82172619
24.44996701
8.51653E-35
332.7981851
391.9832474
Age
13.18212728
1.828711795
7.208422518
6.89154E-10
9.53098638
16.83326818
b)
As a general concept, as the age of the vehicle increases, the maintenance cost of the same is bound to go up.
c)
13.18. This means that for every increase of 1 in age, the maintenance cost would go up by 13.18 all other factors remaining constant.
d)
y=362.39+13.18x=362.39+13.18(7)=454.65
e)
Yes. Because p-value is less than 0.05
f)
1-0.44=0.66 or 66%
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.663697769
R Square
0.440494729
Adjusted R Square
0.432017376
Standard Error
44.67702904
Observations
68
ANOVA
df
SS
MS
F
Significance F
Regression
1
103716.7836
103716.7836
51.9613552
6.89154E-10
Residual
66
131738.437
1996.036924
Total
67
235455.2206
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
362.3907162
14.82172619
24.44996701
8.51653E-35
332.7981851
391.9832474
Age
13.18212728
1.828711795
7.208422518
6.89154E-10
9.53098638
16.83326818
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