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Linear Regression Analysis

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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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? ...

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.

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