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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The number of cases of merlot wine sold by the Connor Owen winery in an eight-year period is as follows: CASES OF MERLOT WINE 276 362 YEAR 2005 2006 2007 2008 2009 2010 2011 2012 404 462 364 506 416 382 Using an exponential smoothing model with an alpha value of 0.30, estimate the smoothed value calculated as of the end of 2012. Use the average demand for 2005 through 2007 as your initial forecast for 2008, and then smooth the forecast forward to 2012. (Round your intermediate calculations and final answer to the nearest whole number.) Answer is complete but not entirely correct. Forecast for 2012 372
Mark Gershon, owner of a musical instrument distributorship, thinks that demand for guitars may be related to the number of television appearances by the popular group Maroon 5 during the previous month. Gershon has collected the data shown in the following table: Maroon 5 TV Appearances Demand for Guitars Y = This exercise contains only parts b, c, and d. b) Using the least-squares regression method, the equation for forecasting is (round your responses to four decimal places): + 4 2 X 5 7 5 9 7 6 7 6 10 7
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)…
The following table shows the past two years of quarterly sales information. Assume that there are both trend and seasonal factors and that the seasonal cycle is one year. Use time series decomposition to forecast quarterly sales for the next year. (Do not round intermediate calculations. Round your answers to the nearest whole number.) Note:- Do not provide handwritten solution. Maintain accuracy and quality in your answer. Take care of plagiarism. Answer completely. You will get up vote for sure.
The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months: Month Jan Feb Mar Apr Number of Accidents 30 48 70 90 Part 2 Using the least-squares regression LOADING... method, the trend equation for forecasting is (round your responses to two decimal places): y = enter your response here + enter your response here x Y=?+?x
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After graduation, you take a position at Top-Slice, a well-known manufacturer of golf balls. One of your duties is to forecast monthly demand for golf balls. Using the following data, you developed a regression model that expresses monthly sales as a function of average temperature for the month: Monthly sales=-202.2+86.5x (average temperature) Click the icon to view the additional data. a. Show how the a and 6 values of -202.2 and 86.5 were calculated. Calculate the slope coefficient, b (enter your responses as whole numbers). (745) 12 12 2 =86.5 More Info March 2017 April May June July August September October November December January 2018 February Monthly Sales 5,300 3,990 5,700 6,520 7,600 7,420 Print 6,710 4,870 3,990 3,900 3,320 2,670 Temperature 51 56 Done 64 80 79 87 83 67 58 41 39 40 X
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…
The monthly sales for Yazici Batteries, Inc., were as follows: Month Sales Jan 19 Feb Mar 17 21 Apr May Jun Jul Aug Sept Oct 12 15 18 15 19 20 20 This exercise contains only parts b and c. b) The forecast for the next month (Jan) using the naive method = 22 sales (round your response to a whole number). Nov Dec 20 22 The forecast for the next period (Jan) using a 3-month moving average approach = 20.67 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).
Aarthi Medicals, a fictitious company, has been monitoringthe sales of the health drink for diabetics. As the demand for the health drink has been steadily increasing, theowner of the store, Nicole Carter, wants to develop goodforecasts for this product to determine how many casesof this drink to order every week form the manufacturer.Nicole has compiled the demand data shown in theaccompanying table for the past 12 weeks. Nicole wantsto evaluate forecasts using the exponential smoothing method using smoothing constants values of α1 = 0.2and α2 = 0.40.Week Demandin Cases1 482 523 494 355 476 537 488 469 5510 5411 5812 5713a Assuming a forecast for Week 2 of 48 cases(f2 = 48), generate forecasts for Weeks 13 usingexponential smoothing for both values of the smoothing constant (α1 = 0.20 and α2 = 0.40).b Compute the forecast error measures of MAD and MSEand determine which value of the smoothing constant provides more accurate forecasts.

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