Question one The president of State University wants to forecast student enrollments for this academic year based on the following historical data: Year 5 years ago 4 years ago 3 years ago 2 years ago Last year Enrollments 15,000 16,000 18,000 20,000 21,000 a- What is the forecast for this year using a 3-year weighted moving average where the weights are halved in a descending order from newest to oldest ? b. What is the forecast for this year using the trend line for these data? c. using the MAD find which forecasting technique may be appropriate for this quetion

Question one The president of State University wants to forecast student enrollments for this academic year based on the following historical data: Year 5 years ago 4 years ago 3 years ago 2 years ago Last year Enrollments 15,000 16,000 18,000 20,000 21,000 a- What is the forecast for this year using a 3-year weighted moving average where the weights are halved in a descending order from newest to oldest ? b. What is the forecast for this year using the trend line for these data? c. using the MAD find which forecasting technique may be appropriate for this quetion

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.6: Moving Averages Models

Problem 22P: The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six...

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Under what conditions might a firm use multiple forecasting methods?
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 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.
The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?
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?
12-1. The Hartley-Davis motorcycle dealer in the Minneapolis- St. Paul area wants to be able to forecast accurately the de- mand for the Roadhog Super motorcycle during the next month. From sales records, the dealer has accumulated the data in the following table for the past year. Month January February March April Мay June Motorcycle Sales 7 10 8 7 12 July August September 10 11 12 October 10 November December 14 16 a Compute a three-month moving average forecast of demand for April through January (of the next year). b. Compore a five-month moving average forecast for June drough January. c. Compare the two forecasts computed in parts (a) and (b) using MAD. Which one should the dealer use for January of dhe next year?
1. Forecasting stationary series The company Handy Inc. produces a solar-powered electronic calculator that has experienced the following monthly sales history for the first four months of the year (in thousands of units): January February. March. April sales 23.3. 72.3. 30,3. 15.5 a) Find the forecast for May using the Moving Averages method with N = 3. b) Assume that the forecast for January was 25. Determine the one-step ahead forecast for February through May, using exponential smoothing with a smoothing constant of α = 0.15. c) Repeatthecalculationinproblembforavalueofα=0.40. d) Compare the MAD (Mean Absolute Deviation) and MSE (Mean Squared Error) for the forecast for February to April in problems b and c. Comment the accuracy of the forecasts. e) The demand in May turned out to be 30. Which of the three forecasting method were most accurate for this prediction? 2. Regression analysis 3 000 000 2 500 000 2 000…
3. A mobile phone store owner wants to predict the demand for mobile phones in October based on the following historical sales data: Month- April May June. July August September Number of phones sold. 100, 140- 110. 150. 120. 160- a. What is this month's forecast using Naive approach b. Using 3-Month Moving Average, develop forecasts for October's demand c. Using 5-Month Moving Average, develop forecasts for October's demand d. When making moving average forecasts, is it better to use a larger time span? -
4. A local moving company has collected data on the number of moves they have been asked to perform over the past two years. Moving is highly seasonal, so the owner/operator, who is both burly and highly educated, decides to apply the multiplicative seasonal method to forecast the number of customers for the coming year. The equation for the trend line of yearly sales is F1 = 100 + 60t. Please forecast demand for each quarter in Year 3. (Round the forecasts to whole numbers and show all calculations t Complete the table below and forecast the sales of Year 3 by quarter. Copy the table below, paste to the answer box and fill in your answers. You need to take a picture of your work and upload the picture in next question. Year 1 Year 2 Year 3 Quarter Demand Quarter Demand Quarter Demand 1 28 1 45 1 43 60 2 120 140 4 49 55 4 Total 240 Total 300 Total Average Average Average For the toolbar, press ALT+F10 (PC) or ALT+FN+F10 (Mac).
Week CheeseburgerSales 1 354 2 344 3 368 4 317 5 361 Based on historical observations over the past five weeks, make a forecast for the next period using the following methods: simple average, three-period moving average, and exponential smoothing with α = 0.4, given a forecast of 326 cheeseburgers for the first week. (Round your answers to 2 decimal places,e.g. 250.25) Simple average: F6 = enter forecast using a simple average 3-Period moving average: F6 = enter forecast using 3-Period moving average Exponential smoothing: F6 = enter forecast using exponential smoothing If actual sales for week 6 turn out to be 365, compare the three forecasts using MAD. Which method performed best?(Calculate your answers using the error only in period 6.) MAD (simple average) = enter forecast using a simple average of MAD MAD (3-period moving average) = enter forecast using 3-period moving average of MAD MAD (exponential smoothing) = enter forecast using exponential…
Choose the type of forecasting technique (survey, Delphi, averaging, seasonal, naive, trend, orassociative) that would be most appropriate for predicting:c. Demand for vacations on the moon.
The demand (in number of units) for Apple iPad over the past 6 months at BestBuy is summarized below. Month Nov 2019 Dec 2019 Demand 45 48 Jan 2020 50 Feb 2020 Mar 2020 Apr 2020 42 46 51 Consider the following three forecasting methods: • Two-month weighted moving average, with weights 6 and 2 (more weight assigned to more recent data) Exponential smoothing with a = 0.7. Let the initial forecast for Nov 2019 be 46. • A trend line projection in the form ŷ = a+bx . To simplify computations, transform the value of x (time) to simpler numbers – designate Nov 2019 as x=1, Dec 2019 as x= 2, etc. (a ) For each of the above methods, forecast the demand of Apple iPad for May 2020. (b) Consider only the two-month weighted moving average method, compute the MAD measure and the MSE measure using the data from Jan 2020. (c) Use the trend line to forecast the demand of Apple iPad for Dec 2020. Give your opinion regarding the reliability of the forecast.