A diary product manufacturing company uses simple exponential smoothing with trend to forecast monthly sales of its product P1. At the end of November, thecompany wants to forecast sales for December. The trend through October has been 11 additional boxes of P1 sold per month. Average sales have been 58boxes of P1 per month. The demand for November was 69 boxes of P1. The company uses a = 0.15 and B 0.20.%3Da)Find a forecast including trend for the month of December.b)If the demand for December was 71 boxes of P1, find forecast including trend for the month of January next year.

Given data is Alpha = 0.15 Beta = 0.20

Answered: A diary product manufacturing company…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section: Chapter Questions

Problem 34P: A small computer chip manufacturer wants to forecast monthly ozperating costs as a function of the...

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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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