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A Study of the Top 75 MBA Programs Attempted to Predict

question 46

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A study of the top 75 MBA programs attempted to predict the average starting salary (in $1000's) of graduates of the program based on the amount of tuition (in $1000's) charged by the program.
The results of a simple linear regression analysis are shown below: Least Squares Linear Regression of Salary
Predictor
 Variables  Coefficient  Std Error  T  P  Constant 18.184910.33361.760.0826 Size 1.474940.1401710.520.0000\begin{array} { l r r c l } \text { Variables } & \text { Coefficient } & \text { Std Error } & \text { T } & \text { P } \\ \text { Constant } & 18.1849 & 10.3336 & 1.76 & 0.0826 \\ \text { Size } & 1.47494 & 0.14017 & 10.52 & 0.0000 \end{array}

R-Squared 0.6027 \quad 0.6027 \quad Resid. Mean Square (MSE) 532.986 532.986
Adjusted R-Squared 0.5972 0.5972 Standard Deviation 23.0865 \quad 23.0865

The model was then used to create 95%95 \% confidence and prediction intervals for yy and for E(Y) \mathrm { E } ( \mathrm { Y } ) when the tuition charged by the MBA program was $75,000\$ 75,000 . The results are shown here:
95\% confidence interval for E(Y) \mathrm { E } ( \mathrm { Y } ) : ($123,390,$134,220) ( \$ 123,390 , \$ 134,220 )
95%95 \% prediction interval for YY : ($82,476,$175,130) ( \$ 82,476 , \$ 175,130 )
Which of the following interpretations is correct if you want to use the model to estimate E(Y) E ( Y ) for all MBA programs?


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