Topic 2 - Time Series Analysis and FProblem 6-25eBookQuarter=234Consider the following time series datą.고numurusRANVERARMeno:aldummodoYear 1-235Year 2657Year 376 Horizontal Pattern with Seasonalityb. Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtr1-1 if Quarter 1, 0 otherwise; Qtr2 - 1 if Quarter 2, 0 otherwise; Qtr3 - 1 if Quarter 3, 0 otherwise. If required, round your answers to three decimal places for subtractivenegative numbers use a minus sign even if there is a + sign before the blank. (Example: -300)Value-Qtri +Qtr2+Qtr3 +c. Compute the quarterly forecasts for next year.f required, round your answers to two decimal places.Quarter 1 forecast -+Quarter 2 forecast -Quarter 3 forecast -Quarter 4 forecast

Quarter Year 1Year 2Year 31467223633564578

Answered: Consider the following time series…

Practical Management Science

6th Edition

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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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4) A small manufacturing firm has recorded a time series of the cost per unit for the firm's leading product over the past eight years. Year Cost/Unit($) 1 2 3 4 5 6 7 8 20 24.5 28.2 27.5 26.6 30 31 36 a) If you are not given a base value or trend, what would be the forecast for the year 10 made at the end of year 8 (a=0.2, ß=0.4). b) Forecast for the year 9 using 1) 5-month weighted average using 0.1,0.1,0.1,0.2,0.3, with the heaviest weight applied to the recent month 2) Exponential smoothing using a =0.2 and forecast of 21 for year 5 3) If a value was not provided in the question above, what value of a would you choose?
part b
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Suppose these data show the number of gallons of gasoline sold by a gasoline distributor in Bennington, Vermont, over the past 12 weeks. Sales (1,000s of gallons) Week 1 17 2 21 3 19 23 5 18 6 16 7 21 8 19 9 23 10 21 11 16 12 23 (a) Using a weight of - for the most recent observation, for the second most recent observation, and - for third most recent observation, compute a three-week weighted moving average for the time series. (Round your answers to two decimal places.) Time Series Value Weighted Moving Average Forecast Week 1. 17 2 21 19 4 23 5 18 6 16 7 21
Given: Year Demand 1 7 2 9 3 5 4 9 5 Predict the value for Year 5: 2 year moving average What is MSE for 2 year moving average? 2 year moving average using 0.6 (weight for the oldest period) and 0.4(weight for most recent period Exponential smoothing, =0.2 and forecast for Year 1 = 5 Linear trend Which forecast method has the least amount of error using MAD? What is the coefficient?
Compute the seasonal relatives for this data using the SA method. Please show the work, not just the answer. See the screen shot for details.
Quarter 1 2 31 11 14 172 20 23 263 29 32 354 38 41 44Compute seasonal relatives for this quarterly data.a. Use the SA method.
The following table contains sales data for the last eight periods. Forecast the sales for period 9 using a moving average forecast of 3 periods. Period Sales Period Sales 1 24 5 79 2 48 6 69 3 56 7 56 4 69 8 22 a. 61 b. 54 c. 49 d. 68
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Consider the following time series data. Quarter 1 4 R (17 Year 1 4 2 3 5 Year 2 6 6557 3 Year 3 7 6 6 8