Reference no: EM13857483

1. Identify the explanatory (x) and response variable (y) based on the study object.

2. Using Statcrunch, create a scatterplot for the variables Miles and Price. It's important to specify X and Y correctly in regression analysis. Print this graph.

After specifying x and y, then do the followings:

• Click on Graph
• Choose scatterplot
• Choose X and Y variable names from the list of Columns
• Click Compute.

3. Describe the relationship between the two variables on the scatterplot. Make sure to comment on form, direction, strength and possible outliers.

4. Using Statcrunch, find the correlation between Miles and Price.

To find a correlation:

• Click Stat
• Choose Summary Stats → Correlation
• Choose two variable names from the list of Columns (You may need to hold down the ctrl or command key to choose the second one.)
• Click on Compute

5. Is it appropriate to calculate the correlation? Explain.

Perform a complete regression analysis following the instructions below.

To do regression analysis:

• Click on Stat

• Choose Regression → Simple Linear

• Choose X and Y variable names from the list of Columns

• Choose Fitted line plot and Residuals vs. x-values from the list of Graphs (You may need to hold down the ctrl or command key to choose the second one.)

• Click Compute.

• Click on next to see the scatterplot and Residual plot.

6. Using Statcrunch output, write the linear regression equation for predicting Price from Miles.

7. Interpret the slope within the context of this problem. (note: pay attention to measurement units)

8. Does the intercept have a meaningful interpretation here? If yes, provide an interpretation within the context of the problem. If not, briefly explain why.

9. What percent of the variability in Price is accounted for by this linear model?

10. What is the predicted sale price for a Mercedes that has been driven for 19,500 miles? Would this be a valid prediction? Explain your answer.

11. Would it be accurate to use this least-squares regression equation to predict the sale price of another luxury car? Explain your answer.

12. What does the residual plot tell us about the fit? Do you think a linear model is appropriate?

Do not forget to print and turn in the residual plot.

13. Write a brief report about the price and Miles of used Mercedes. Be sure to talk about the variables by name and in the correct units.