Let’s go through the questions step by step based on the given scatterplot, equation, and options:

a. Independent and Dependent Variables

• The independent variable is median starting salary (plotted on the x-axis).
• The dependent variable is median mid-career salary (plotted on the y-axis).

b. Why is median salary used instead of mean?

The best choice is A. Salary distributions are usually skewed, making the median a more meaningful measure of center.
Explanation: Median is less affected by extreme values (outliers) compared to the mean, making it a better measure for skewed data.

c. Estimate from the graph for a $40,000 starting salary:

From the scatterplot, for a starting median salary of $40,000, trace upward to the line of best fit. The y-value looks to be approximately $70,000. (This is an estimate—round to the nearest thousand.)

d. Using the equation to calculate mid-career salary:

The equation is:
$\text{Mid-Career Salary} = -12,570 + 1.994 \times \text{Start Median Salary}$
Substitute $\text{Start Median Salary} = 40,000$:
$\text{Mid-Career Salary} = -12,570 + 1.994 \times 40,000 = -12,570 + 79,760 = 67,190$
Rounding to the nearest thousand: $67,000.

e. Other factors affecting mid-career salary (Select all that apply):

Some other factors that could influence mid-career salary include:

• Level of education required for jobs associated with the major.
• Prestige of the college or university attended.
• Location of the college or university.
• Field of study or major.
• Demand for graduates in the field of study.

Would you like detailed explanations for any specific part of this solution?

5 Related Questions:

1. Why does the slope of the regression equation matter when analyzing relationships?
2. What are other advantages of using medians in skewed distributions?
3. How does the coefficient of 1.994 in the regression equation influence the prediction?
4. What steps would you take to graphically validate the line of best fit?
5. How might external economic conditions (e.g., inflation, job market demand) affect the mid-career salary?

Tip:

When using a regression equation, always check if the values you're substituting fall within the range of the data. Predictions outside this range might not be reliable!