For those data ssr and sst determine the coefficient determination

14.5

A consumer organization wants to develop a regression model to predict mileage (as measured by miles per gallon) based on the horsepower of the car’s engine and the weight of the car (in pounds). Data were collected from a sample of 50 recent car models, and the results are organized and stored in Auto.

5. Construct a 95% confidence interval estimate for the mean miles per gallon for cars that have 60 horsepower and weigh 2,000 pounds

6. Construct a 95% prediction interval for the miles per gallon for an individual car that has 60 horsepower and weighs 2,000 pounds

Remote hours (X²)—Total number of hours worked by employees at locations away from the central plant

Data were collected for 26 weeks; these data are organized and stored in Standby.

5. Construct a 95% confidence interval estimate for the mean standby hours for weeks in which the total staff present have 310 people-days and the remote hours are 400.

6. Construct a 95% prediction interval for the standby hours for a single week in which the total staff present have 310 people-days and the remote hours are 400

14.41

The marketing manager of a large supermarket chain faced the business problem of determining the effect on the sales of pet food of shelf space and whether the product was placed at the front (=1) or back (=0) of the aisle. Data are collected from a random sample of equal-sized stores. The results are shown in the following table (and organized and stored in Petfood):

4. Perform a residual analysis on the results and determine whether the regression assumptions are valid.

5. Is there a significant relationship between sales and the two independent variables (shelf space and aisle position) at the 0.05 level of significance?

10. Compute and interpret the adjusted r²

11. Compare r² with the r² value computed in Problem 13.16(a) on page 487

14.43

The owner of a moving company typically has his most experienced manager predict the total number of labor hours that will be required to complete an upcoming move. This approach has proved useful in the past, but the owner has the business objective of developing a more accurate method of predicting labor hours. In a preliminary effort to provide a more accurate method, the owner decided to use the number of cubic feet moved and whether there is an elevator in the apartment building as the independent variables and has collected data for 36 moves in which the origin and destination were within the borough of Manhattan in New York City and the travel time was an insignificant portion of the hours worked. The data are organized and stored in Moving. For (a) through (k), do not include an interaction term.

5. Is there significant relationship between labor hours and the two independent variables (cubic feet moved and whether there is an elevator in the apartment building) at the 0.05 level of significance?

6. At the 0.05 level of significance, determine whether each independent variable makes a contribution to the regression model. Indicate the most appropriate regression model for this set of data.

11. What assumption do you need to make about the slope of labor hours with cubic feet moved?

12. Add an interaction term to the model and, at the 0.05 level of significance, determine whether it makes a significant contribution to the model

2. Interpret the regression coefficient in (a).

3. Predict the end-of –training exam score for a student with a proficiency exam score of 100 who had Web-based training

8. Construct and interpret 95% confidence interval estimate of the population slope for the relationship between end –of- training exam score and type of training method.

9. Compute and interpret the adjusted r²

16.7

The following data (stored in Treasury) represent the three-month Treasury bill rates in the United States from 1991 to 2008:

5. Repeat (c) and (d), using a smoothing coefficient of W = 0.25

6. Compare the results of (d) and (e)

3. What are your forecasts for 2009 to 2010?

4. What conclusions can you reach concerning the trend in GDP?

3. Compute a quadratic trend forecasting equation and plot the results

4. Compute an exponential trend forecasting equation and plot the results

1. Construct the time-series plot

2. Describe the monthly pattern that is evident in the data

7. Interpret the January multiplier

8. What is the predicted value for March 2009?

14.7 Standby

13.4

13.16

In problem 13.4 the marketing manager used shelf space for petfood to predict weekly sales (stored in petfood). For those data SSR = 20,535 and SST = 30,025.