4.29. The demand for all-terrain bicycles had been steadily increasing since 1989. In the followingtable are the sales per quarter of mountain bikes produced by Canyon and Cactus Cycles sincethe second quarter of 1989.(a) Plot the data and verify that the model for linear trend is acceptable. Draw a line through theplotted data and obtain a visual estimate of a and b.(b) Estimate the parameters of the model in preparation for double exponential smoothing.(c) Forecast sales for the third quarter of 1996 using these initial estimates.(d) Forecast sales for the fourth quarter of 1996 using alpha 0.3 and beta = 0.25 if sales inthe third quarter of 1996 were equal to 234.(e) Use linear regression to estimate the parameters and forecast the third quarter of 1996. Observation1234561891011121314Year1989199019911992Quarter234123412341234Demand1673615743446868738493128100130148ObservationBARDGING1617181920212223242526272829Year Quarter Demand1993114714213415918116816818818618918422420722319941995FORECASTING 13319962341234123412

“Since you have posted a question with multiple sub-parts, we will solve the first three sub-parts…

Answered: . The demand for all-terrain bicycles…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter2: Introduction To Spreadsheet Modeling

Section: Chapter Questions

Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...

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Here are the data for the past 21 months for actual sales of a particular product: LAST YEAR THIS YEAR January 315 February 418 March 390 April 430 May 400 [ June 480 July 430 August 320 September 385 October 530 November 592 555 December 300 400 350 460 390 395 355 245 340 Develop a forecast for the fourth quarter using a three-quarter, weighted moving average. Weight the most recent quarter 0.50, the second most recent 0.25, and the third 0.25. Do the problem using quarters, as opposed to forecasting separate months. (Round your answer to 2 decimal places.) Forecast for the fourth quarter
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Below is a table containing data on product demand for the most recent three months along with the forecasts that had been made for those three previous months. Calculate the MAE (or MAD). Month Demand Forecast 1 308 310 2 388 390 3 344 342
Here are the actual tabulated demands for an item for a nine-month period (January through September). Your supervisor wants to test two forecasting methods to see which method was better over this period. MONTH АCTUAL January February March April 124 125 154 174 May 161 June 180 July August September 144 138 142 a. Forecast April through September using a three-month moving average. (Round your answers to 2 decimal places.) Three-Month Moving Average 134.33 Month April May 151.00 June 163.00 July 171.66 August 161.66 September 154.00 b. Use simple exponential smoothing with an alpha of 0.40 to estimate April through September, using the average of January through March as the initial forecast for April. (Round your answers to 2 decimal places.) Exponential Smoothing Month April 142.20 May 154.92 June 157.35 July 166.41 August 157.45 September 149.67
376.98 says its wrong
The following table gives the number of pints of type A blood used at Damascus Hospital in the past 6 weeks: Week Of Pints Used August 31 September 7 September 14 September 21 September 28 345 372 408 383 368 October 5 378 a) The forecasted demand for the week of October 12 using a 3-week moving average = 376.33 pints (round your response to two decimal places). b) Using a 3-week weighted moving average, with weights of 0.15, 0.25, and 0.60, using 0.60 for the most recent week, the forecasted demand for the week of October 12 = 376.25 pints (round your response to two decimal places and remember to use the weights in propriate order– the largest weight applies to most recent period and smallest weight applies to oldest period.) c) If the forecasted demand for the week of August 31 is 345 and a = 0.25, using exponential smoothing, develop the forecast for each of the weeks with the known demand and the forecast for the week of October 12 (round your responses to two decimal places).…
Given the table below, complete the missing cell values applying the Exponential Smoothing method of forecasting and Mean Absolute Deviation. Answer also the other 2 related questions below the table. Numbers with decimal should take 2 decimal places. Actual Quarter Tonnage Unloaded 1 2 3 4 5 6 7 8 175 160 170 160 160 170 180 200 Forecast for a=3 175 175.0 170.5 167.2 165.1 166.6 170.6 Sum of absolute deviation = Absolute Deviation for a = .3 Other questions: (1) What is the MAD for a = .3? (2) Which smoothing constant would you prefer? 0.00 15.00 0.50 10.35 4.93 13.45 29.41 80.89 Forecast for a = .6 175 166.0 168.4 163.4 161.3 166.5 174.6 Absolute Deviation for a = .6 0.00 15.00 4.00 8.40 3.36 8.66 13.46 25.38
Month Demand Forecast Error 1 20 2 18 3 21 4 25 5 24 6 27 7 22 8 30 9 23 10 20 11 29 12 22 Mean Abs Error Problem 6: The demand manager of Maverick Jeans is responsible for ensuring sufficient warehouse space for the finished jeans that come from the production plants. It has occasionally been necessary to rent public warehouse space, something that Maverick would like to avoid. In order to estimate the space requirements the demand manager is evaluating moving-average forecasts. The demand (in 1,000 case units) for the last fiscal year is shown below. Bias MAD (mean error) Month 1 2 3 4 5 6 7 8 9 10 11 12 Demand 20 18 21 25 24 27 22 30 23 20 29 22 Use a three-month moving average to estimate the month-in- advance forecast of demand for months 4-12. Calculate the Bias and MAD. (Note: Adjust all cell values to two decimal points.)
Demand for oil changes at Garcia's Garage has been as follows: Month January February March April May June July August Number of Oil Changes 38 55 56 60 58 01 70 52 a. Use simple linear regression analysis to develop a forecasting model for monthly demand. In this application, the dependent variable, Y, is monthly demand and the independent variable, X, is the month. For January, let X=1; for February, let X 2; and so on. The forecasting model is given by the equation Y=X (Enter your responses rounded to two decimal places)
Use regression or simple exponential smoothing with the following data. For exponential smoothing, assume the forecast for year 1 was the same as the actual and use an alpha of 0.7. (use excel to show formauls used) Year Sales 1 290 2 340 3 400 4 410 5 400 1. What is the tracking signal for the forecast?
Can you please solve all parts of the question manually. Thank you!
Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data: Week 6 weeks ago 5 weeks ago 4 weeks ago 3 weeks ago 2 weeks ago Last week # Students 83 110 95 80 65 50 What is this week's forecast using trend-adjusted (double) smoothing with alpha = .5 and beta = .1, if the forecast for last week was 65, the forecast for two weeks ago was 75, and the trend estimate for last week's forecast was -5? 49.3 78.7 51.3 50.6 65.4

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