George Kyparisis owns a company that manufactures sailboats. Actual demand for George's sailboats during each of the past four seasons was as follows: Year Season 2 3 4 Winter 1,400 1,200 1,000 960 Spring 1,500 1,440 1,620 1,540 Summer 1,000 2,140 2,020 1,960 Fall 680 810 690 500 George has forecasted that annual demand for his sailboats in year 5 will equal 6,000 sailboats. Based on the given data and using the multiplicative seasonal model, the demand level for George's sailboats in the spring of year 5 will be sailboats (enter a whole number).

George Kyparisis owns a company that manufactures sailboats. Actual demand for George's sailboats during each of the past four seasons was as follows: Year Season 2 3 4 Winter 1,400 1,200 1,000 960 Spring 1,500 1,440 1,620 1,540 Summer 1,000 2,140 2,020 1,960 Fall 680 810 690 500 George has forecasted that annual demand for his sailboats in year 5 will equal 6,000 sailboats. Based on the given data and using the multiplicative seasonal model, the demand level for George's sailboats in the spring of year 5 will be sailboats (enter a whole number).

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.3: Simple Regression Models

Problem 5P: Management of a home appliance store wants to understand the growth pattern of the monthly sales of...

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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?
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