Let’s say that we wanted to be able to predict the Braking distance in feet for a car based on its weight in pounds. Using this sample data, perform a simple-linear regression to determine the line-of-best fit. Use the Weight as your x (independent) variable and braking distance as your y (response) variable. Use 4 places after the decimal in your answer.
13. Paste your results here:
Answer the following questions related to this simple regression
14. What is the equation of the line-of-best fit? Insert the values for bo and b1 from above into y = bo + b1x.
15. What is the slope of the line? What does it tell you about the relationship between the Weight (Pounds) and Braking distance (Feet) data? Be sure to specify the proper units.
16. What is the y-intercept of the line? What does it tell you about the relationship between the Weight and Braking distance?
17. What would you predict the Braking distance would be for a car that Weighs 2650 pounds? Show your calculation.
18. Let’s say you want to buy a muscle car that Weighs 4250 pounds. What effect would you predict this would have on the braking distance of the car? Relate this to the Braking distance you found for a car weighing 2650 pounds in the previous question.
19. Find the coefficient of determination (R2 value) for this data. What does this tell you about this relationship?
[Hint: see definition on Page 311.]
Part V. Multiple Regression
Let’s say that we wanted to be able to predict the city miles per gallon for a car using
· Weight in pounds
· Length in inches
Using this sample data, perform a multiple-regression using Weight, Length, Cylinder, City. Select City (Column 8) as your dependent variable.
20. Paste your results here:
21. What is the equation of the line-of-best fit? The form of the equation is Y = bo + b1X1 + b2X2 + b3X3 (fill in values for bo, b1, b2, and b3).
[Round coefficients to 3 decimal places.]
22. What would you predict for the City MPG earnings of a car whose
· Weight is 3410 pounds
· LENGTH is 130 inches
· Cylinders is 6
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