27247 cmsec a 10000 gram cat is beyond the range of

Info icon This preview shows pages 30–32. Sign up to view the full content.

272.47 cm/sec. A 10,000-gram cat is beyond the range of the data and the linear relationship may not hold outside of this range. Determine the predicted takeoff velocity and the residual value for the cat with the largest mass. Also interpret what this residual value means. A 7930-gram cat has observed takeoff velocity of 286.3 predicted takeoff velocity of 297.72, residual is −11 .4 . The predicted takeoff velocity is 11.4 cm/sec lower than the predicted takeoff velocity. Chapter 10.3 Exercise Question 17 Reconsider the previous two exercises. Answer the following based on the scatterplot presented above. Do not bother to perform any calculations. Which cat has the largest predicted value for its takeoff velocity? The cat with body mass 2,660 grams Which cat has the smallest predicted value for its takeoff velocity? The cat with body mass 7,930 grams Which cat has the largest residual value? The cat with body mass 5,600 grams Which cat has the smallest residual value? The cat with body mass 3,550 grams Chapter 10.3 Exercise Question 18 Reconsider the previous three exercises and the CatJumping data file. Investigate the association between the response variable (takeoff velocity) and the other explanatory variables (hind limb length, muscle mass, percent body fat). Select the explanatory variable that has the strongest association with the response. Describe this association. Percent body fat has the strongest association with takeoff velocity. It is a strong negative linear relationship. Report the equation of the least squares line for predicting the cat's takeoff velocity using this explanatory variable.
Image of page 30

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

Predicted takeoff velocity=397.65-1.95 (bodyfat) Interpret the value of the slope coefficient. For each 1 percentage point increase in body fat the predicted takeoff velocity decreases 1.95 cm/sec. Determine and interpret the value of ࠵? S . ࠵? S = 0 .424 ; 42.4% of the variability in takeoff velocities can be explained by the linear relationship with percent body fat. Chapter 10.3 Exercise Question 19 Honda Civic pricing The data in the file UsedHondaCivics come from a sample of used Honda Civics listed for sale online in July 2006. The variables recorded are age (calculated as 2006 minus year of manufacture) and price. Identify the observational units. Produce a scatterplot of price vs. age. Describe the association revealed in the graph. Determine the least squares line for predicting price from age and produce a scatterplot with the least squares line superimposed. Report and interpret the value of the slope coefficient. What percentage of the variability in car prices is explained by knowing the car's age? Chapter 10.3 Exercise Question 20 Textbook prices Two Cal Poly freshmen gathered data on a random sample of textbooks from the campus bookstore in November of 2006. Two of the variables recorded were the price of the book and the number of pages that it contained. These data are in the file TextbookPrices .
Image of page 31
Image of page 32
This is the end of the preview. Sign up to access the rest of the document.
  • Summer '18
  • Null hypothesis, Statistical hypothesis testing, Correlation and dependence, Pearson product-moment correlation coefficient

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern