WEEK 12
Can the cost of flying a commercial airliner be predicted using
regression analysis?
Suppose a study is conducted to predict cost of a flight by number of
passengers using only Boeing 737s traveling 500 miles in comparable
routes during the same season of the year.
Given here is Excel output for a simple regression model that was
developed for this case. Analyze the computer output.
Write down the estimated regression equation. What stands here for Y and
what for X1?
- How many airliners were in the sample?
- Does there appear to be any relationship between 2 variables? Which
indicator do you use to estimate that?
- Discuss the strength of the regression model. Did the estimated
regression equation provide a good fit?
- Discuss the significance of slope coefficient on the basis of the
output.
SUMMARY OUTPUT
____________________
Regression Statistics
____________________
Multiple R 0.942
R Square 0.886
Adjusted R Square 0.875
Standard Error 15.6491
Observations 12
ANOVA
__________________________________________________
df SS MS F Significance F
__________________________________________________
Regression 1 19115.063 19115.0632 78.05 0.00005
Residual 10 2448.937 244.8937
Total 11 21564
_____________________________________________________________________
Coefficients Standard Error T-Stat P-value
_____________________________________________________________________
Intercept 30.9125 13.2542 2.33 0.041888
X1 2.2315 0.2526 8.83 0.000005
Statistics Homework Solutions
#5387
Simple Linear Regression Analysis: Can the cost of flying a commercial airliner be predicted using regression analysis? Suppose a study is conducted to predict cost of a flight by number of passengers using only Boeing 737s traveling 500 miles in comparable routes during the same season of the year. Given here is Excel output for a simple regression model that was developed for this case. Analyze the computer output.
Regression Analysis
Can the cost of flying a commercial airliner be predicted using regression analysis?
Suppose a study is conducted to predict cost of a flight by number of passengers using only Boeing 737s traveling 500 miles in comparable routes during the same season of the year. Given here is Excel output for a simple regression model that was developed for this case. Analyze the computer output.
1) Write down the estimated regression equation. What stands here for Y and what for X1?
2) How many airliners were in the sample?
3) Does there appear to be any relationship between 2 variables? Which indicator do you use to estimate that?
4) Discuss the strength of the regression model. Did the estimated regression equation provide a good fit?
5) Discuss the significance of slope coefficient on the basis of the output.
SUMMARY OUTPUT
____________________
Regression Statistics
____________________
Multiple R 0.942
R Square 0.886
Adjusted R Square 0.875
Standard Error 15.6491
Observations 12
ANOVA
__________________________________________________
df SS MS F Significance F
__________________________________________________
Regression 1 19115.063 19115.0632 78.05 0.00005
Residual 10 2448.937 244.8937
Total 11 21564
_____________________________________________________________________
Coefficients Standard Error T-Stat P-value
_____________________________________________________________________
Intercept 30.9125 13.2542 2.33 0.041888
X1 2.2315 0.2526 8.83 0.000005
The solution interprets and analyzes the computer output for a regression analysis that is used to predict cost of a flight by number of passengers using only Boeing 737s traveling 500 miles in comparable routes during the same season of the year.
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