Consider the following time series data.2|345Week1Value191214101613(a) Construct a time series plot. What type of pattern exists in the data?O The data appear to follow a seasonal pattern.O The data appear to follow a horizontal pattern.The data appear to follow a trend pattern.O The data appear to follow a cyclical pattern.(b) Develop the three-week moving average for this time series. (Round your answers to two decimal places.)Time SeriesValueWeekForecast11912314101613Compute MSE. (Round your answer to two decimal places.)MSE =What is the forecast for week 7? (c) Use a = 0.2 to compute the exponential smoothing values for the time series.Time SeriesValueWeekForecast119212314410516613Compute MSE. (Round your answer to two decimal places.)MSE =What Is the forecast for week 7? (Round your answer to two decimal places.)(d) Compare the three-week moving average forecast with the exponentlal smoothing forecast using a = 0.2. Which appears to provide the better forecast based on MSE? Explaln.O The three-week moving average providesbetter forecast since It has a smaller MSE than the smoothing approach using a = 0.2.O The exponential smoothing using a = 0.2 provides a better forecast since it has a smaller MSE than the three-week moving average approach.O The exponential smoothing using a = 0.2 provides a better forecast since it has a larger MSE than the three-week moving average approach.O The three-week moving average provides a better forecast since it has a larger MSE than the smoothing approach using a = 0.2.(e) Use trial and error to find a value of the exponential smoothing coefficient a that results in a smaller MSE than what you calculated for a = 0.2.

3-period moving average forecast (Ft)= At-1+At-2+At-33 Exponential smoothing Forecast…

Answered: Consider the following time series…

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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Which of the following is TRUE regarding the two smoothing constants of the Forecast Including Trend (FIT) model? Select one: a. One constant smoothes the regression intercept, whereas the other smoothes the regression slope. b. One constant is positive, while the other is negative. c. They are called MAD and cumulative error. d. Their values are determined independently. e. Alpha is always smaller than beta.
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Sales for the past 6 months at Dalworth Company are given in the following table: Month Sales July 52 August 61 September 53 October 35 November 31 December 28 What is the Mean Absolute Deviation (MAD) for August through December forecasts produced by using exponential smoothing with α = 0.4? (Note: Do not round the forecasts, and choose the closest answer.) Multiple Choice 39.64 8.75 15.28 11.94
b. Compute 2s control limits for each forecast. (Do not round your intermediate calculations. Round your answers to 2 decimal places.) Forecast Method 1 Method 2 Control Limits
The following table shows a company's annual revenue (in billions of dollars) for 2009 to 2014. Year Period (t) Revenue ($ billions) 2009 1 23.8 2010 29.2 2011 37.9 2012 4 50.3 2013 59.8 2014 6 66.6 (a) Construct a time series plot. 80 80 70 70 70 70 60 60 50 50 50 30 30 30 30- 20 20 20 20 10 10 10 - 10- 4. 6 7 4 6 4 5 6 7 1 2. 4 6 7 Period Period Period Period What type of pattern exists in the data? O The time series plot shows an upward linear trend. O The time series plot shows a downward curvilinear trend. O The time series plot shows a downward linear trend. O The time series plot shows an upward curvilinear trend. (b) Develop a linear trend equation for this time series to forecast revenue (in billions of dollars). (Round your numerical values to three decimal places.) T = (c) What is the average revenue increase per year (in billions of dollars) that this company has been realizing? (Round your answer to three decimal places.) billion Revenue ($ billions) Revenue ($ billions)…
Over one year, a company sold the following numbers of lawn mowers. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Sales 238 220 195 245 345 380 270 220 280 120 110 85 Compute the one-step-ahead 3-month and 6-month moving-average forecasts for July through December. Compute the MAD for the forecasts obtained in part (a). What effect does increasing N from 3 to 6 have on the forecasts? Use the arithmetic average of the first six months of data as a baseline to initialize the exponential smoothing. Compute the one-step-ahead exponential smoothing forecasts for July through December, assuming α = 0.20. Compare the accuracy of the forecasts obtained in part (c) with the one-step ahead six-month moving-average forecasts obtained in part (a).
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.
help please answer in text form with proper workings and explanation for each and every part and steps with concept and introduction no AI no copy paste remember answer must be in proper format with all working
The monthly sales for Yazici Batteries, Inc., were as follows: Jan Feb Mar Apr May Jun Jul Aug Sept Oct 21 23 17 14 11 16 16 18 Nov Dec 20 20 20 24 Month Sales This exercise contains only parts b and c b) The forecast for the next month (Jan) using the naive method=sales (round your response to a whole number). The forecast for the next period (Jan) using a 3-month moving average approach = sales (round your response to two decimal places). The forecast for the next period (Jan) using a 6-month weighted average with weights of 0.10, 0.10, 0.10, 0.20, 0.20, and 0.30, where the heaviest weights are applied to the most recent month= sales (round your response to one decimal place) Using exponential smoothing with a = 0.30 and a September forecast of 21.00, the forecast for the next period (Jan) = sales (round your response to two decimal places). Using a method of trend projection, the forecast for the next month (Jan) = sales (round your response to two decimal places). c) The method…

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