1. Would you expect a positive or negative correlation between the model mileage and price?

2 .Is mileage the independent or dependent variable?

3.What is your regression equation for the best fit line? Be sure to confirm your equation from Excel with your TI-84.

4. dentify the values for the following from Excel.

1. Slope of regression line:

1. y-intercept of regression line:

1. Coefficient of determination (r²):

1. Linear correlation coefficient (r):

1. Interpret the linear correlation coefficient (r). Does it agree with the strength and slope of your scatter plot?

1. Determine if there is a significant negative linear correlation at the 10% level of significance.
   1. State the null and alternate hypotheses to test for a significant negative linear correlation.

1. Determine the critical value and sketch (or describe) the rejection region.

1. Record the test statistic (t) from your Excel printout.

1. Use the traditional method to make a conclusion, explain why, and interpret in context.

1. Based on your conclusion from #4d, is this regression equation appropriate for predicting the price of a Civic? If so, for which mileages is it appropriate?

1. If appropriate, use the regression equation to determine the sale price for a Honda Civic with 100,000 miles on it.

Interpret the coefficient of determination (r²) in context.