# Question

An airline plans to initiate service at an airport in a city of approximately 500,000 people. To determine staffing requirements, officials for the airline take advantage of the sample survey data on the relationship between the number of flights per week and the number of employees for 30 airlines at various airports in cities that are similar in size (approximately 300,000 to 700,000). The data is found below.

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Regression Analysis: EMP. versus FLIGHTS

The regression equation is

EMP. = - 23.3 + 1.04 FLIGHTS

Predictor Coef SE Coef T P

Constant -23.261 2.177 -10.68 0.000

FLIGHTS 1.04407 0.03202 32.60 0.000

S = 4.31057 R-Sq = 97.4% R-Sq(adj) = 97.3%

Analysis of Variance

Source DF SS MS F P

Regression 1 19752 19752 1063.00 0.000

Residual Error 28 520 19

Total 29 20272

Predicted Values for New Observations

New Obs Fit SE Fit 95% CI 95% PI

1 28.943 0.896 ( 27.107, 30.779) ( 19.924, 37.961)

2 133.350 2.883 (127.445, 139.255) (122.728, 143.973)XX

XX denotes a point that is an extreme outlier in the predictors.

Values of Predictors for New Observations

New Obs FLIGHTS 1..........50 2........150

Correlations: FLIGHTS, EMP.

Pearson correlation of FLIGHTS and EMP. = 0.987

P-Value = 0.000

Analyze the above output to determine the regression equation.

Find and interpret βˆ1 in the context of this problem.

Find and interpret the coefficient of determination (r-squared).

Find and interpret coefficient of correlation.

Does the data provide significant evidence (α = .05) that the number of flights can be used to predict the number of employees? Test the utility of this model using a two-tailed test. Find the observed p-value and interpret.

Find the 95% prediction interval for the number of employees needed for an airline that has 50 flights per week. Interpret this interval.

Find the 95% confidence interval for the mean number of employees needed for airlines that have 50 flights per week. Interpret this interval.

What can we say about the number of employees needed for an airport that has 150 flights per week?

.:.

.:.

Regression Analysis: EMP. versus FLIGHTS

The regression equation is

EMP. = - 23.3 + 1.04 FLIGHTS

Predictor Coef SE Coef T P

Constant -23.261 2.177 -10.68 0.000

FLIGHTS 1.04407 0.03202 32.60 0.000

S = 4.31057 R-Sq = 97.4% R-Sq(adj) = 97.3%

Analysis of Variance

Source DF SS MS F P

Regression 1 19752 19752 1063.00 0.000

Residual Error 28 520 19

Total 29 20272

Predicted Values for New Observations

New Obs Fit SE Fit 95% CI 95% PI

1 28.943 0.896 ( 27.107, 30.779) ( 19.924, 37.961)

2 133.350 2.883 (127.445, 139.255) (122.728, 143.973)XX

XX denotes a point that is an extreme outlier in the predictors.

Values of Predictors for New Observations

New Obs FLIGHTS 1..........50 2........150

Correlations: FLIGHTS, EMP.

Pearson correlation of FLIGHTS and EMP. = 0.987

P-Value = 0.000

Analyze the above output to determine the regression equation.

Find and interpret βˆ1 in the context of this problem.

Find and interpret the coefficient of determination (r-squared).

Find and interpret coefficient of correlation.

Does the data provide significant evidence (α = .05) that the number of flights can be used to predict the number of employees? Test the utility of this model using a two-tailed test. Find the observed p-value and interpret.

Find the 95% prediction interval for the number of employees needed for an airline that has 50 flights per week. Interpret this interval.

Find the 95% confidence interval for the mean number of employees needed for airlines that have 50 flights per week. Interpret this interval.

What can we say about the number of employees needed for an airport that has 150 flights per week?

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