A Study of the Top 75 MBA Programs Attempted to Predict

question 104

Multiple Choice

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
$\begin{array} { l r c c l } \text { Variables } & \text { Coefficient } & \text { Std Error } & \text { T } & \text { P } \\ \text { Constant } & 18.1849 & 10.3336 & 1.76 & 0.0826 \\ \text { Size } & 1.47494 & 0.14017 & 10.52 & 0.0000 \end{array}$

R-Squared $\quad 0.6027 \quad$ Resid. Mean Square (MSE) $532.986$
Adjusted R-Squared $0.5972$ Standard Deviation $23.0865$ Interpret the estimated slope of the regression line.

• A) For every $1000 increase in the tuition charged by the MBA program, we estimate that the average starting salary will decrease by $1474.94.
• B) For every $1000 increase in the tuition charged by the MBA program, we estimate that the average starting salary will increase by $1474.94.
• C) For every $1474.94 increase in the tuition charged by the MBA program, we estimate that the average starting salary will increase by $18,184.90.
• D) For every $1000 increase in the average starting salary, we estimate that the tuition charged by the MBA program will increase by $1474.94.

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Definitions:

SST

Sum of Squares Total, a measure in statistics that quantifies the total variance within a dataset.

Multiple Coefficient

A term used in multiple regression analysis that indicates the strength and direction of a relationship between variables.

Determination

The measure of how well a set of data points fit a statistical model.

SSR

Sum of Squares for Regression, a statistical measure that showcases how well a regression model captures the explained variance in the data.