1. Construct an exponential smoothing model for the Amazon Revenue Data. a. Use 0.7 as the smoothing constant and b. 7,131 as the initial value C. What is your prediction for Q1, 2021? d. Calculate the MAD, MSE, and MAPE for this time series model. 2. Construct a moving average model (k=3) for the Amazon Revenue Data. 3. Compare the smoothing and moving average models. Which is more accurate?
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2010 1 7,131.00
2010 2 6,566.00
2010 3 7,560.00
2010 4 12,947.00
2011 1 9,857.00
2011 2 9,913.00
2011 3 10,876.00
2011 4 17,431.00
2012 1 13,185.00
2012 2 12,834.00
2012 3 13,806.00
2012 4 21,268.00
2013 1 16,070.00
2013 2 15,704.00
2013 3 17,091.00
2013 4 25,587.00
2014 1 19,741.00
2014 2 19,340.00
2014 3 20,578.00
2014 4 29,329.00
2015 1 22,717.00
2015 2 23,184.00
2015 3 25,358.00
2015 4 35,747.00
2016 1 29,128.00
2016 2 30,404.00
2016 3 32,714.00
2016 4 43,741.00
2017 1 35,714.00
2017 3 43,744.00
2017 4 60,453.00
2018 1 51,042.00
2018 2 52,886.00
2018 3 56,576.00
2018 4 72,383.00
2019 1 59,700.00
2019 2 63,404.00
2019 3 69,982.00
2019 4 87,436.00
2020 1 75,452.00
2020 2 88,912.00
2020 3 96,145.00
2020 4 125,555.00
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- The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?a) The demand forecast for Month 6 would be: A. 565 haircuts B. 574 haircuts C. 578 haircuts D. 584 haircuts b) With Mean Absolute Deviation (MAD) as the criterion, the best forecasting model for this time series data is: A. Naïve approach B. 2-week Simple Moving Average (SMA) C. Weighted Moving Average (WMA) with weights: 0.5, 0.3, 0.2 D. Exponential Smoothing (ES) with alpha = 0.8
- MSE answer for part a = 5.95 is incorrect. Please do recalculations with the correct answer. also the rest of the question was not answered so here it is: Use ? = 0.2 to compute the exponential smoothing values for the time series. Week Time SeriesValue Forecast 1 19 2 14 3 17 4 12 5 18 6 15 Compute MSE. (Round your answer to two decimal places.) MSE = What is the forecast for week 7? (Round your answer to two decimal places.) Use ? = 0.4 to compute the exponential smoothing values for the time series. Week Time SeriesValue Forecast 1 19 2 14 3 17 4 12 5 18 6 15This a 4 part question that am confused about. Please show the work by hand not excel. 4-1 Sales of a particular product (in the thousands of dollars) for the years 2015 through 2018 have been $48,000, $64,000, $67,000 and $83,000 respectively. (a)What sales would you predict for 2019, using simple four-year moving avaerage? (b) What sales would you predict for 2019, using a weighted moving average with weights of 0.50 for the immediate preceding year and 0.3, 0.15, and 0.05 for the three years before that? (I am able to calculate and know the answer for (a) $65,000 and (b) $73,000. What I don't understand is the following : 4-2 Using exponential smoothing with a weight of 0.6 on actual values: (a) If sales are $45,000 and $50,000 for 2017 and 2018, what would you forecast for 2019 ? (The first forecast is equal to the value of the preceding year.) (b) Given this forecast and actual 2019 sales of $53,000, what would you then forecast for 2020? 4-3 In Problem 4-1, taking…1. Using MAD as the criterion, which of the following models would you use for thegiven time series data? Why?A. Naïve approach;B. 5-month SMA model;C. WMA model with weights 0.1, 0.3, and 0.6; orD. ES model with α = 0.5 and a forecast of 3,500 liters in the first month.NOTE: In answering Item 1, mention the whole description of the model; i.e., not just“SMA model”, but “SMA model with n = ...”; not just “WMA model”, but “WMA modelwith weights ...”; not just “ES model”, but “ES model with α = ...”.
- Garfield Industries is expanding its operations throughout the Southeast United States. Garfield anticipates that the expansion will increase sales by $1,000,000 and increase operating costs (excluding depreciation and amortization) by $700,000. Depreciation and amortization expenses will rise by $50,000, interest expense will increase by $150,000, and the company’s tax rate will remain at 40 percent. If the company’s forecast is correct, how much will net income increase or decrease, as a result of the expansion?Problem 1 Forecasting • All analysis and calculations and report must be done in a single (ONE) Excel file.• Put your name at the top of the worksheet.• Make Excel do all of the calculations. (Instructor must be able to see your cell-reference formulas.)• Include report/answers below the forecasting calculations. o Make sure answers are clear, complete and easy to find. o Your report must include: a. Presentation of forecasts b. Explanation of why you chose each of the methods 1. Tom Simpson, Director of the Chamber of Commerce for Exeter township is investigating the past ten years oftourist visits to the area. The following data has been gathered on number of tourists who signed into the localinformation center. Year Number of tourists1 7002 2483 6334 4585 14106 15887 16298 13019 145510 1989 Tom is interested in implementing a forecasting system and is…Sarah has been custom manufacturing sweaters now for 7 years. Her annual sales are shown below. Year Sales 1 178 2 215 3 233 4 301 5 337 6 330 7 361 What are her forecasted sales for year 8 using a 3 year moving average? What are her forecasted sales for year 8 using a 5 year weighted average where w1=.4, w2=.3, w3=.15, w4=.1, w5=.05? What are her forecasted sales for year 8 using exponential smoothing? Assume year 7 forecast was 323. Select your own alpha. Which forecast method is the best for Sarah to use based on MAD? Why?
- Clinic administrator Marc Schniederjans wants you to forecastpatient demand at the clinic for week 7 by using this data. You decide to use a weighted moving average method to find this fore-cast. Your method uses four actual demand levels, with weights of 0.333 on the present period, 0.25 one period ago, 0.25 two peri-ods ago, and 0.167 three periods ago. a) What is the value of your forecast? PXb) If instead the weights were 20, 15, 15, and 10, respectively, howwould the forecast change? Explain why. c) What if the weights were 0.40, 0.30, 0.20, and 0.10, respec-tively? Now what is the forecast for week 7?1 The demand for automobiles at Crescent Auto Dealers for the past 8 weeks is as follows. Week Auto Demand Weights1 9 0.12 11 0.33 8 0.64 125 106 137 78 12a Develop a 3-week moving average forecast for Weeks 4 through 9b Develop a 3-week weighted average forecast…The following two demand sets are to be used to test two different basic exponential smoothingmodels. The first model uses 0.1, and the second uses 0.5. In both cases, the modelshould be initialized with a beginning forecast value of 50; that is, the ESF forecast for period 1made at the end of period 0 is 50 units. In each of the four cases (two models on two demandsets), compute the average forecast error and MAD. What do the results mean?Demand Set I Demand Set IIPeriod Demand Period Demand1 51 1772 46 2833 49 3904 55 4225 52 5106 47 6807 51 7168 48 8199 56 92710 51 10 7911 45 11 7312 52 12 8813 49 13 1514 48 14 2115 43 15 8516 46 16 2217 55 17 8818 53 18 7519 54 19 1420 49 20 16