b. Calculate the MAD for all the forecasts, including January's.MAD The following tabulations are actual sales of units for six months and a starting forecast in January.FORECAST80JanuaryFebruaryMarchAprilMayJuneACTUAL10094106806894a. Calculate forecasts for the remaining five months using simple exponential smoothing with a = 0.2.FebruaryMarchAprilMayJuneForecasts

Exponential smoothing is a forecasting model which considering the actual demand and forecasting…

Answered: The following tabulations are actual…

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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1. Discuss the differences between Qualitative and Quantitative forecasting models. How do Associative and Time Series techniques differ? 2. What is the Mean Absolute Deviation (MAD)? 3. Use the following set of data to calculate the Mean Absolute Deviation (MAD) for the following set of data. Actual (A:) Forecast (Ft) Forecast Error Absolute (Deviation) Month Forecast Eror January February March 45 45 42 50 34 45 April 48 40 Мay 38 45 MAD =
a) Apply moving average and exponential smoothing to generate two forecasts of the closing price for the entire data period. Set appropriate values for the forecast parameters (e.g., k and a). b) Plot and compare the actual and predicted data values you calculated in part (b). Provide one chart that displays actual, moving average, and exponential smoothing lines. Hint: Calculate MSE, MAD, or MAPE to compare the different forecasts. Develop your own forecast from scratch. Do NOT use Excel’s Data Analysis or Forecast Sheets.
Please solve the attached question.
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
Demand for oil changes at Garcia's Garage has been as follows: Number of Oil Changes January 44 February 49 March 66 IT April 59 May 53 June 58 59 63 Month July August 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 Janu The forecasting model is given by the equation Y=+x. (Enter your responses rounded to two decimal places) J Show Transcribed Text >. Use the model to forecast demand for September, October, and November. Here, X=9, 10, and 11, respectively. (Enter your responses rounded to two decimal places.) Forecast for the number of Oil Changes Show Transcribed Text Month September October November can you do all the parts please corrrect
Please consider the following information and make forecasts for January, February, March, and April of Year 2. Use second-order polynomial regression to make forecasts. Adjusted seasonal index Actual sales Deseasonalized sales 836 825 Year 1 Year 2 January February March April May June July August September October November December January February March April 0.72 0.75 1:22 1.48 1 1.23 0.7 0.78 0.92 1.13 1.28 1.33 0.72 0.75 1.22 1.48 1220 1319 1020 1184 685 641 875 1008 1068 1141
Use the data set below to answer the following questions: [Gunakan set data di bawah untuk menjawab soalan-soalan berikut:] Table 1: Monthly sales unit IJadual 1: Unit jualan bulanau) Month Sales Units [Bulan] 1 [Unit Jualan] 1100 2 1200 3 1450 4 1800 2100 2700 i) Calculate the three-period simple moving average forecasts for months 4, 5 and 6. (Hitunskau rawalan pALata bergerak nudak weNEgUrakan empat-tenroh untuk bulau 5 dan 6.) Calculate the four-period weighted moving average forecasts for months 5 and 6 usıng weights of 0.05, 0.15, 0.30, and 0.50 from the earliest period to the latest period, respectively.
can you make an operations management analysis for this forecasting
The following are monthly actual and forecast demand levels for May through December for units of a product manufactured by the Deborah Bishop Company in Des Moines: Month May June July August September October Actual Demand 105 82 110 112 108 106 130 120 Forecast Demand 100 108 97 98 108 102 105 107 November December For the given forecast, the tracking signal = MADS (round your response to two decimal places).
The following are monthly actual and forecast demand levels for May through December for units of a product manufactured by the Deborah Bishop Company in Des Moines: Month May June July August September October For the given forecast, the tracking signal = = MADS (round your response to two decimal places). November December Actual Demand 100 80 1/10 115 105 110 125 120 Forecast Demand 100 104 99 101 104 104 105 109
2
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)

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