N Attendance at Orlando's newest Disneylike attraction, Lego World, has been as follows:Guests(in thousands)63104149776383QuarterWinter Year 1Spring Year 1Summer Year 1Fall Year 1Winter Year 2Spring Year 2Based on the given attendance, the seasonal indices for each of the seasons are (round your responses to three decimal places):SeasonIndex0.706WinterSpringGuests(in thousands)QuarterSummer Year 2Fall Year 2Winter Year 3Spring Year 3Summer Year 3Fall Year 3124529915121099

Based on the given data related to attendance of guests (in thousands), the data is presented in…

Answered: ttendance at Orlando's newest…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section: Chapter Questions

Problem 42P: The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars)...

See similar textbooks

Similar questions

The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?
The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?
Under what conditions might a firm use multiple forecasting methods?
At the beginning of each week, a machine is in one of four conditions: 1 = excellent; 2 = good; 3 = average; 4 = bad. The weekly revenue earned by a machine in state 1, 2, 3, or 4 is 100, 90, 50, or 10, respectively. After observing the condition of the machine at the beginning of the week, the company has the option, for a cost of 200, of instantaneously replacing the machine with an excellent machine. The quality of the machine deteriorates over time, as shown in the file P10 41.xlsx. Four maintenance policies are under consideration: Policy 1: Never replace a machine. Policy 2: Immediately replace a bad machine. Policy 3: Immediately replace a bad or average machine. Policy 4: Immediately replace a bad, average, or good machine. Simulate each of these policies for 50 weeks (using at least 250 iterations each) to determine the policy that maximizes expected weekly profit. Assume that the machine at the beginning of week 1 is excellent.
Do the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.
The management of a technology company is trying to determine the variable that best explains the variation of employee salaries using a sample of 52 full-time employees; see the file P13_08.xlsx. Estimate simple linear regression equations to identify which of the following has the strongest linear relationship with annual salary: the employees gender, age, number of years of relevant work experience prior to employment at the company, number of years of employment at the company, or number of years of post secondary education. Provide support for your conclusion.
The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.
Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P13_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo.
Attendance at Orlando's newest Disneylike attraction, Lego World, has been as follows: Quarter Winter Year 1 Spring Year 1 Summer Year 11 Fall Year 1 Guests (in thousands) 68 99 149 75 65 84 Quarter Summer Year 2 Fall Year 2 Winter Winter Year 3 Spring Year 3 Summer Year 3 Fall Year 3 Guests (in thousands) 120 51 99 151 Winter Year 2 207 Spring Year 2 97 Based on the given attendance, the seasonal indices for each of the seasons are (round your responses to three decimal places): Season Index
Attendance at Orlando's newest Disneylike attraction, Lego World, has been as follows Quarter Guests (in thousands) Quarter Summer Year 2 Winter Year 1 58 99 Fall Year 2 149 Winter Year 3 Spring Year 1 Summer Year 1 Fal Year 1 Winter Year 2 75 Spring Year 3 63 Summer Year 3 Spring Year 21 82 Fall Year 3 Based on the given attendance, the seasonal indices for each of the seasons are (round your responses to three decimal places) Season Index Winter Spring Summer Fall 0000 Guests (in thousands) 125 54 99 146 210 99
Attendance at Orlando's newest Disneylike attraction, Lego World, has been as follows: Quarter Guests (in thousands) Quarter Guests (in thousands) Winter Year 1 63 Summer Year 2 120 Spring Year 1 99 Fall Year 2 54 Summer Year 1 149 Winter Year 3 94 Fall Year 1 75 Spring Year 3 151 Winter Year 2 64 Summer Year 3 210 Spring Year 2 83 Fall Year 3 99 Part 2 Based on the given attendance, the seasonal indices for each of the seasons are (round your responses to three decimal places): Season Index Winter enter your response here
Attendance at Orlando's newest Disneylike attraction, Lego World, has been as follows: Guests (in thousands) Quarter Winter Year 1 Spring Year 1 Summer Year 1 Fall Year 1 Winter Year 2 Spring Year 2 63 94 168 75 64 83 Season Quarter Winter Summer Year 2 Fall Year 2 Winter Year 3 Spring Year 3 Summer Year 3 Fall Year 3 Based on the given attendance, the seasonal indices for each of the seasons are (round your responses to three decimal places): Guests (in thousands) Index 124 51 94 151 210 99

SEE MORE QUESTIONS

Recommended textbooks for you

Practical Management Science

Operations Management

ISBN:

9781337406659

Author:

WINSTON, Wayne L.

Publisher:

Cengage,

Contemporary Marketing

Marketing

ISBN:

9780357033777

Author:

Louis E. Boone, David L. Kurtz

Publisher:

Cengage Learning

Marketing

ISBN:

9780357033791

Author:

Pride, William M

Publisher:

South Western Educational Publishing

Practical Management Science

Operations Management

ISBN:

9781337406659

Author:

WINSTON, Wayne L.

Publisher:

Cengage,

Contemporary Marketing

Marketing

ISBN:

9780357033777

Author:

Louis E. Boone, David L. Kurtz

Publisher:

Cengage Learning

Marketing

ISBN:

9780357033791

Author:

Pride, William M

Publisher:

South Western Educational Publishing

SEE MORE TEXTBOOKS