Sales of tablet computers at Marika Gonzalez's electronics store in Washington, D.C., over the past 10 weeks areshown in the table below:Week21Demand 19 21329Week1 2Demand. 19 21Forecast 19.01438329525a) The forecast for weeks 2 through 10 using exponential smoothing with a = 0.50 and a week 1 initial forecast of 19.0are (round your responses to two decimal places);4387628 365258 9 1024 24 306 728368 9 10242430

Exponential smoothing is a forecasting model that considers the actual demand and forecasting demand…

Answered: Sales of tablet computers at Marika…

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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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.
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.
Use simple exponential smoothing with a -0.8 to forecast electric scooter sales at Guelph- Humber Inc. for July. Assume that the forecast for May was for 45 electric scooters. Round your answer to 1 decimal Electric Scooter Sales Month May 42 June 47 July 45 August 40 Weekly demand for medical masks for the last five weeks at Chopper Drug Store has been as follows: 92, 96, 100, 100 and 102 (listed from the oldest to most recent). Suppose a naive forecast was used to forecast demand. What would the MAD be for this situation? Enrollment in a particular Yoga class at Guelph Fitness Centre for the last five months has been 80, 86, 88, 88 and 90(listed from the oldest to most recent). Suppose a two-month mov average was used to forecast enrollment. What would the MSE be for this situation?
The number of cases of merlot wine sold by the Connor Owen winery in an eight-year period is as follows: YEAR CASES OF MERLOT WINE 1 270 2 356 3 398 4 456 5 358 6 500 7 410 8 376 Using an exponential smoothing model with an alpha value of 0.20, estimate the smoothed value calculated as of the end of year 8. Use the average demand for years 1 through 3 as your initial forecast for year 4, and then smooth the forecast forward to year 8. Note: Round your intermediate calculations and final answer to the nearest whole number.
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
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).
Tom Simpson, Director of the Chamber of Commerce for Exeter township is investigating the past ten years of tourist visits to the area. The following data has been gathered on number of tourists who signed into the local information center. Year Number of tourists 1 700 2 248 3 633 4 458 5 1410 6 1588 7 1629 8 1301 9 1455 10 1989 What is the forecasts for Year 11 using the simple moving method? (show in calculations in excel file)
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
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)
14 Community General Hospital finds itself treating many bicycle accident victims. Data from the last seven 24-hour periods is shown below: Day Bicycle Victims 1 8 2 14 3 8 4 14 5 18 6 15 With an alpha value of 0.31 and a starting forecast in day 3 equal to the 21 , what is the exponentially smoothed forecast for day 4? (Round to two decimal places) 15 What is the exponentially smoothed forecast for day 5? (Round to two decimal places)
A police station had to deploy a police officer for an emergency multiple times in the last four evenings. The t below shows the number of emergencies each evening. Weekday Number of calls each day Monday 10 Tuesday 2 Wednesday 12 Thursday 12 (Round your answer to 1 decimal place.) What would be their forecast for the emergencies on Friday using a two-day moving average approach? Forecast for Friday calls

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