# 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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