The accompanying dataset provides data on the monthly usage of natural gas (in millions of cubic feet) for a certain region over two years. Implement the Holt-Winters multiplicative seasonalitymodel with no trend to find the forecast for periods 13-26, where x = 0.6 and y=0.9. Then find the MAD for periods 13-24.Click the icon to view the monthly use of natural gas data.Use the Holt-Winters multiplicative seasonality model with no trend to find the forecast for periods 13-18, periods 19-24, and then for periods 25 and 26.(Type integers or decimals rounded to two decimal places as needed.)PeriodForecast131415161718C...☐☐☐☐☐☐Natural Gas UsageMonth Period Gas Usage1234JanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDec56TORINESTRANN7891011121314151617181920212223242512271571385333302928418719623125224013534342727293986188-X

Holt-Winter’s multiplicative method is a forecasting technique that considers both trend and…

Answered: The accompanying dataset provides data…

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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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).
Forecast bias is useful to determine a. Seasonality b. Trends c. if forecast error is truly random d. the exponential smoothing factor
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)
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
If month one has 240,000 demands, month two 250,080 demands, month three 325,00 demands, month four 370,000 demands, month five 420,000 demands and month six 509,00 demands. Use a forecast for the first month of 240,000, an initial trend forecast of 50,000, and smoothing parameters of 0.35 for both demand smoothing and trend smoothing. Compute the forecasts and trends using double exponential smoothing
The accompanying dataset provides data on the monthly usage of natural gas (in millions of cubic feet) for a certain region over two years. Implement the Holt-Winters multiplicative seasonality model with no trend to find the forecast for periods 13-26, where x = 0.5 and y = 0.8. Then find the MAD for periods 13-24. Click the icon to view the monthly use of natural gas data. Use the Holt-Winters multiplicative seasonality model with no trend to find the forecast for periods 13-18, periods 19-24, and then for periods 25 and 26. (Type integers or decimals rounded to two decimal places as needed.) Forecast Period 13 14 15 16 17 18 Natural Gas Usage Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Period Gas Usage 1 2 8699 A WI 3 4 5 7 9 10 11 12 13 14 1516718192021 22 23 24 241 221 155 137 54 34 29 27 30 40 90 201 229 238 242 136 34 34 28 26 27 40 86 187
Amazon finds that sales in July are much greater relative to other months because of the Amazon Prime days in that month. Assuming that an exponential smoothing model is used for forecasting sales, what should the company best do to make the forecast for the next month (Aug) more reflective of reality? Please pick from the provided options. F t+1 = αDt + (1-α) Ft increase value of alpha decrease value of alpha stay with the same alpha decrease the expected forecast for the current month (Ft) On hearing our forecast, our clients probe - how sure are we about the forecast? Which measure of errror (among those stated below) should we best use to respond? a. Root MSE (RMSE) b. Mean absolute percentage error (MAPE) c. Mean forecast error (Avg. error) d. Cumulative forecast error (CFE)
35 The last seven weeks of demand at a new car dealer are shown below. Use a three-period weighted-moving average forecast to determine a forecast for the 8th week using weights of 3, 2, and 1 (where the most recent week receives the highest weight). (Round all forecasts to the nearest whole unit.) Calculate the MAD for this forecast (covering all weeks in which error comparisons can be made). What does the MAD indicate? Week Sales1 252 303 274 315 276 297 30
Forecasting with Linear Regression The company VID-CO-19 has manufactured swim suits for men, women, and children for 10 years. The company is located in Boston, Massachusetts. Swim suits have always had a higher demand during the months of April, May, June, July, August, and September. The demand has always been lower during the months of October, November, December, January, February, and March. TETT The real demand, based on sales of year 20X1 is the following (it starts with December of the previous year): Period Bimester Demand Season Average December-January February-March 1 100 90 2 200 180 3 April-May June-July August-September 900 910 4 3,000 2,100 4,000 4,900 October-November 300 350 VID-CO-19 has hired you as an Operations Management expert to help them to forecast demand for year 20X2 using the Linear Regression Method. What is the TOTAL "seasonalized" or "deseasonalized' forecasted demand for Quito-Ecuador in year 20X2? (write your answer rounded to two decimals)
In a retail store, the actual sales of a particular product (in thousands of units) over the past few months are as follows: Month Sales 1 16 2 22 3 18 4 20 5 23 Using exponential smoothing method with α (smoothing constant) of 0.75 and the given forecast for month 1 equal to 10, what is the forecast for month 6?
Given the following historical data, what is the simple three-period moving average forecast for period 6? Period Value 1 73 2 68 3 65 4 72 5 67
Develop a forecast for the next month using a four-month moving average method. Use the Excel Functions SLOPE and INTERCEPT to write the linear regression prediction equation with Months as the independent variable and sales as the dependent variable. Use the prediction equation to estimate the number of sales in month 9.

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