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A Study of the Top MBA Programs Attempted to Predict

question 101

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A study of the top MBA programs attempted to predict the average starting salary (in $1000ʹs) of graduates of the program based on the amount of tuition (in $1000ʹs) charged by the program and
The average GMAT score of the programʹs students. The results of a regression analysis based on a
Sample of 75 MBA programs is shown below: Least Squares Linear Regression of Salary
$A study of the top MBA programs attempted to predict the average starting salary (in $1000ʹs) of graduates of the program based on the amount of tuition (in $1000ʹs) charged by the program and The average GMAT score of the programʹs students. The results of a regression analysis based on a Sample of 75 MBA programs is shown below: Least Squares Linear Regression of Salary \begin{array}{lccr} \text { R-Squared } & 0.6857 & \text { Resid. Mean Square (MSE) } & 427.511 \\ \text { Adjusted R-Squared } & 0.6769 & \text { Standard Deviation } & 20.6763 \end{array} Interpret the coefficient of determination value shown in the printout. A) At \alpha = 0.05 , there is insufficient evidence to indicate that something in the regression model is useful for predicting the average starting salary of the graduates of an MBA program. B) We expect most of the average starting salaries to fall within \$ 20,676 of their least squares predicted values. C) We expect most of the average starting salaries to fall within \$ 41,353 of their least squares predicted values. D) We can explain 68.57 \% of the variation in the average starting salaries around their mean using the model that includes the average GMAT score and the tuition for the MBA program.$

$\begin{array}{lccr}\text { R-Squared } & 0.6857 & \text { Resid. Mean Square (MSE) } & 427.511 \\\text { Adjusted R-Squared } & 0.6769 & \text { Standard Deviation } & 20.6763\end{array}$

$A study of the top MBA programs attempted to predict the average starting salary (in $1000ʹs) of graduates of the program based on the amount of tuition (in $1000ʹs) charged by the program and The average GMAT score of the programʹs students. The results of a regression analysis based on a Sample of 75 MBA programs is shown below: Least Squares Linear Regression of Salary \begin{array}{lccr} \text { R-Squared } & 0.6857 & \text { Resid. Mean Square (MSE) } & 427.511 \\ \text { Adjusted R-Squared } & 0.6769 & \text { Standard Deviation } & 20.6763 \end{array} Interpret the coefficient of determination value shown in the printout. A) At \alpha = 0.05 , there is insufficient evidence to indicate that something in the regression model is useful for predicting the average starting salary of the graduates of an MBA program. B) We expect most of the average starting salaries to fall within \$ 20,676 of their least squares predicted values. C) We expect most of the average starting salaries to fall within \$ 41,353 of their least squares predicted values. D) We can explain 68.57 \% of the variation in the average starting salaries around their mean using the model that includes the average GMAT score and the tuition for the MBA program.$

Interpret the coefficient of determination value shown in the printout.

Definitions:

Systematic Risk

The risk inherent to the entire market or market segment, also known as non-diversifiable risk or market risk.

Market Risk Premium

The higher return an investor foresees from investing in a market portfolio with associated risks as opposed to choosing completely safe assets.

Risky Asset

An asset that carries a higher degree of risk of loss, but also offers a higher potential return.

Risk-free Asset

An investment with zero risk of financial loss, theoretically providing guaranteed returns, such as government bonds.