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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 l 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 Resid. Mean Square (MSE)  532.986 Adjusted R-Squared 0.5972 Standard Deviation 23.0865\begin{array}{lccc}\text { R-Squared } & 0.6027 & \text { Resid. Mean Square (MSE) } & 532.986 \\\text { Adjusted R-Squared } &0.5972 & \text { Standard Deviation } & 23.0865\end{array}
The model was then used to create 95% confidence and prediction intervals for y and for E(Y) when the tuition charged by the MBA program was $75,000. The results are shown here: 95% confidence interval for E(Y) : ($123,390, $134,220) 95% prediction interval for Y: ($82,476, $175,130) Which of the following interpretations is correct if you want to use the model to predict Y for a single MBA programs?


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