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An Academic Advisor Wants to Predict the Typical Starting Salary

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An academic advisor wants to predict the typical starting salary of a graduate at a top business school using the GMAT score of the school as a predictor variable. A simple linear regression of SALARY versus GMAT using 25 data points is shown below. β^0=92040β^1=228 s=3213r2=.66r=.81df=23t=6.67\hat { \beta } _ { 0 } = - 92040 \hat { \beta } 1 = 228 \mathrm {~s} = 3213 \mathrm { r } ^ { 2 } = .66 \quad r = .81 \mathrm { df } = 23 \quad t = 6.67
Give a practical interpretation of r2=.66r ^ { 2 } = .66 .
A) 66%66 \% of the sample variation in SALARY can be explained by using GMAT in a straight -line model.
B) We expect to predict SALARY to within 2[.66]2 [ \sqrt { .66 } ] of its true value using GMAT in a straight-line model.
C) We estimate SALARY to increase $.66\$ .66 for every 1 -point increase in GMAT.
D) We can predict SALARY correctly 66%66 \% of the time using GMAT in a straight-line model.

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Definitions:

Ending Inventory

The total value of goods available for sale at the end of an accounting period, after all sales and purchases have been accounted for.

Accounting Period

A specific time frame for which financial statements are prepared, to measure the performance and financial position of a business.

Net Income

The total profit of a company after all expenses and taxes have been subtracted from revenue, indicating the company's financial performance over a specific period.

Specific Identification Method

An inventory costing method in which costs are assigned to individual items, enabling precise cost determination of goods sold and ending inventory.

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