The following table shows the past two years of quarterly sales information.Assume that there are both trend and seasonal factors and that the seasonalcycle is one year.QUARTER125678AWN348SALES219237208171169Quarter9101112192157132Use regression and seasonal indexes to forecast quarterly sales for the next year.Note: Do not round intermediate calculations. Round your answers to thenearest whole number.Forecast

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Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter2: Introduction To Spreadsheet Modeling

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

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National Standard, Inc. sells radio frequency identification (RFID) tags. Monthly demand for a seven-month period is reported below: Sales (1000 units) Forecast Observation Month Yt Ft 1 February 19 2 March 18 3 April 15 4 May 20 5 June 18 6 July 22 7 August 20 8 September ? Use Excel to plot the data and forecast September sales using the following methods: The naïve forecast A three-month moving average Exponential smoothing with a smoothing coefficient of α = 0.2, assuming a February forecast of 19 A 3-month weighted moving average, with weights 0.60, 0.3, and 0.1. With 0.6 applied to the most recent past.
Through the analysis of a customer base's buying behavior, a company might see that this customer base has not been buying a lot of products recently. After scanning through this data, the company might think to market to this subset of consumers differently, in order to best communicate how this company's products might benefit this group specifically. What type of CRM is described? O a. Operational CRM O b. Analytical CRM O c. Strategic CRM d. Collaborative CRM
Given the following data for demand at the XYZ Company, calculate the monthly forecast for 2003 using a 3-month moving average. Calculate the Forecast, Error, MAD (mean absolute percentage error), Bias and the TS (tracking signal) . Period Demand Forecast Error MAD Bias TS 2-Oct 2012 800 　　　　　 2-Nov 2012 1000 　　　　　 2-Dec 2012 950 　　　　　 2-Jan 2013 1100 2-Feb 2013 930 2-Mar 2013 1020 2-Apr 2013 870
4 .Substitute for Problem # 4 on in attachment)-Calculate the MAD,revised MAD and the Revised Forecast for months 1-6 for the following three groups,where the Forecast stands for Forecast Demand.Has your forecast improved ? Tell me how would you go about improving the revised forecast( for each of the three groups)without actually doing so? Estate Planning Group Auditing and Accounting Group Business Consulting Group Month Actual Demand Forecast Actual Demand Forecast Actual Demand Forecast 1 100 125 270 230 140 135 2 90 125 240 230 130 135 3 110 125 280 230 160 135 4 115 125 260 230 180 135 5 130 125 300 230 200 135 6 115 125 220 230 190 135
The following data relate the sales figures of the barin Mark Kaltenbach’s small bed-and-breakfast inn in Portand, tothe number of guests registered that week:WEEK GUESTS BAR SALES1 16 $3302 12 2703 18 3804 14 300 a) Perform a linear regression that relates bar sales to guests (notto time).b) If the forecast is for 20 guests next week, what are the salesexpected to be? PX
The following data are for calculator sales in units at an electronics store over the past nine weeks: Week Sales Week Sales 1 45 53 2 50 7 59 3 44 8. 59 4 51 9 64 5 57 Use trend projection with regression to forecast sales for weeks 10 - 13. What are the error measures (CFE, MSE, o, MAD, and MAPE) for this forecasting procedure? How about ? Obtain the trend projection with regression forecast for weeks 10 – 13. (Enter your responses rounded to two decimal places.) Period Forecast, F, 10 64.81 11 67.06 12 69.31 13 71.56 Obtain the error measures. (Enter your responses rounded to two decimal places.) CFE MSE MAD МАРЕ
Please help with the correct answers in details: Centerville Bikes and Stuff (CBS) sells motorcycles and accessories. The number of helmets sold by CBS per week for the past six weeks follows. Week 1 2 3 4 5 6 Value 19 12 17 10 18 15 Using the naive method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. (a) mean absolute error MAE = (b) mean squared error MSE = (c) mean absolute percentage error (Round your answer to two decimal places.) MAPE = % (d) What is the forecast for week 7?
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Use exponential smoothing with trend adjustment to forecast demand for period 11. Let α = 0.5, � = 0.3, and let the initial trend value be 12 and the initial forecast be 200
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The following tabulations are actual sales of units for six months and a starting forecast in January. ACTUAL 124 FORECAST 104 120 133 109 86 105 January February March April May June a. Calculate forecasts for the remaining five months using simple exponential smoothing with a = 0.3. Note: Round your answers to the nearest whole number. February March April May June Saved Forecasts b. Calculate the MAD for all the forecasts, including January's. Note: Round your answer to 1 decimal place. MAD
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Use regression and seasonal indexes to forecast quarterly sales for the next year. Note: Do not round intermediate calculations. Round your answers to the nearest whole number. Quarter 9 10 11 12 Forecast