Use regression analysis and seasonal indexes to forecast this summer’s demand. Note: Do not round intermediate calculations. Round your answer to the nearest whole number.
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The following are historical demand data:
YEAR | SEASON | ACTUAL DEMAND |
---|---|---|
2 years ago | Spring | 205 |
Summer | 140 | |
Fall | 375 | |
Winter | 575 | |
last year | Spring | 475 |
Summer | 275 | |
Fall | 685 | |
Winter | 965 |
Use
Note: Do not round intermediate calculations. Round your answer to the nearest whole number.
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- What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?The following are historical demand data: YEAR SEASON ACTUALDEMAND 2 years ago Spring 205 Summer 151 Fall 379 Winter 560 last year Spring 481 Summer 268 Fall 679 Winter 959 Use regression analysis on deseasonalized demand to forecast next summer’s demand. (Round your answer to the nearest whole number.)Consider the following data:Monthly Profit of a Gym: Month & Profit: Jan-12: 5700 Feb-12: 5453 March-12: 5034 April-12: 4717 May-12: 5185 June-12: 5638 July-12: 6519 Aug-12: 6182 Sept-12: 5913 What are the MAD, MSE and MAPE scores for the three-period weighted moving average forecast? Round any intermediate calculations, if necessary, to no less than six decimal places, and round your final answer to one decimal place. MAD: MSE: MAPE as %: link to excel sheet for forumla to assist: http://www.livebinders.com/play/play?id=2368073
- Consider the following data:Monthly Profit of an Auto Repair Shop Month Jan-14 Feb-14 Mar-14 Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Profit ($) 17,527 17,677 16,455 18,642 20,163 17,350 19,127 18,622 20,422 Step 3 of 4 : Determine the exponential smoothing forecast for the next time period using a smoothing constant of 0.35. If necessary, round your answer to one decimal place. I have been stuck on this for 2 days now. I do not understand exponential smoothing forecast at all and the textbook is not helpful. I have tried looking at the textbook, using the step-by-step learning tools, and youtube videos and it still isn't helping.Monthly sales for a six month period are as follows: Month Sales Jan 18,000 Feb 22,000 Mar 16,000 Apr 18,000 May 20,000 June 24,000 Compute the sales forecast for July using the following approaches: (1) Four-month moving average; (2) Weighted three-month moving average using .50 for June, .30 for May and .20 for April; (3) Exponential smoothing with α (smoothing constant) equal to .40, assuming a February forecast of 18,000Here are the data for the past 21 months for actual sales of a particular product: LAST YEAR THIS YEAR January 300 275 February 400 375 March 425 350 April 450 425 May 400 400 June 460 350 July 400 350 August 300 275 September 375 350 October 500 November 550 December 500 Develop a forecast for the fourth quarter using a three-quarter, weighted moving average. Weight the most recent quarter 0.5, the second most recent 0.25, and the third 0.25. Solve the problem using quarters, as opposed to forecasting separate months. Note: Round your answer to 2 decimal places.
- Consider the following data: Monthly Profit of a Gym Month Jan-12 Feb-12 Mar-12 Apr-12 May-12 Jun-12 Jul-12 Aug-12 Sep-12 Profit ($) 6,350 6,103 5,424 5,237 5,380 6,158 7,819 6,351 6,057 Step 4 of 5 : What are the MAD, MSE and MAPE scores for the exponentially smoothed forecast? Round any intermediate calculations, if necessary, to no less than six decimal places, and round your final answer to one decimal place.The problem is based on the following data given. Observations of the demand for a certain part stocked at a parts supply depot during the calendar year 2013 were ( as shown ). Using a four-month moving average, determine the two-step-ahead forecast for July through December 2013. (Hint: The two-step-ahead forecast for July is based on the observed demands in February through May.)Mac Corp. is considering a project that has the following cash flow data. What is the project’s internal rate of return (IRR)? Year 0 1 2 3 Cash flow -$1,000 $425 $425 $425
- The following table contains the demand from the last 10 months: MONTH ACTUAL DEMAND 1 31 2 34 3 33 4 35 5 37 6 36 7 38 8 40 9 40 10 41 a. Calculate the single exponential smoothing forecast for these data using an α of 0.30 and an initial forecast ( F1�1 ) of 31. Note: Round your intermediate calculations and answers to 2 decimal places. b. Calculate the exponential smoothing with trend forecast for these data using an α of 0.30, a δ of 0.30, an initial trend forecast ( T1�1 ) of 1, and an initial exponentially smoothed forecast ( F1�1 ) of 30. Note: Round your intermediate calculations and answers to 2 decimal places. c-1. Calculate the mean absolute deviation (MAD) for the last nine months of forecasts. Note: Round your intermediate calculations and answers to 2 decimal places. c-2. Which is best? multiple choice Exponential smoothing with trend forecast Single exponential smoothing forecastHere 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 ACTUAL January 110 February 130 March 150 April 170 May 160 June 180 July 140 August 130 September 140 a. Forecast April through September using a three-month moving average. Note: Round your answers to 1 decimal place. b. Use simple exponential smoothing with an alpha of 0.3 to estimate April through September, using the average of January through March as the initial forecast for April. Note: Do not round intermediate calculations. Round your answers to 2 decimal places. c-1. Calculate MAD for each method. Note: Round your answers to 1 decimal place. c-2. Use MAD to decide which method produced the better forecast over the six-month period. multiple choice Exponential smoothing. Three-month moving average.For the Hawkins Company, the monthly percentages of all shipments received on time over the past 12 months are 80, 82, 84, 83, 83, 84, 85, 84, 82, 83, 84, and 83. a. Compute for a 3 month moving average forecast. b. Compute for the exponential smoothing forecast for a=0.2. c. Which provides the better forecast using MSE as the measure of model accuracy? d. What is the forecast for next month?