Linear Regression Analysis
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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
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Regression Statistics
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Multiple R 0.942
R Square 0.886
Adjusted R Square 0.875
Standard Error 15.6491
Observations 12
ANOVA
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df SS MS F Significance F
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Regression 1 19115.063 19115.0632 78.05 0.00005
Residual 10 2448.937 244.8937
Total 11 21564
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Coefficients Standard Error T-Stat P-value
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Intercept 30.9125 13.2542 2.33 0.041888
X1 2.2315 0.2526 8.83 0.000005
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Solution Summary
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.
Solution Preview
1) Write down the estimated regression equation. What stands here for Y and what for X1?
Y=30.9125 + 2.2315 X1
Y= cost of a flight
X1 = No of passengers
2) How many airliners were in the sample?
There were 12 airlines in the sample.
3) Does there appear to be any relationship between 2 variables? Which indicator do you use to estimate that? ...
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