Quarterly demands is given for the past 3 years: Winter Spring Summer Fall Year 1 4800 4500 4100 5500 Year 2 5700 3800 4500 6000 Year 3 6000 4600 4900 6500 a) Develop a seasonally adjusted forecast. (Use a linear trend line model to develop a forecast estimate.) b) Forecast demands for year 4 (Winter, Spring, Summer, and Fall). c) Assess Part (a) model accuracy using MSE.
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- Under what conditions might a firm use multiple forecasting methods?The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.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?
- 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 file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
- The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?The file P13_28.xlsx contains monthly retail sales of U.S. liquor stores. a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?1. The number of bushels of apples sold at a roadside fruit stand over a 12-day period were PROBLEMS as follows: Day Numbar Sold Day Number Sold 25 35 31 29 33 32 38 10 40 37 32 34 11 37 12 If a two-period moving average has been used to forecast sales, what were the daily forecasts starting with the forecast for day 3? If a four-period moving average has been used, what were the forecasts for eacn uay starting with day 5? Plot the original data and each set of forecasts on the same graph. Which forecast has the greater tendency to smooth? Which forecast has the better ability to respond quickly to changes? What does use of the term sales instead of demand imply? b. C. 2. If exponential smoothing with a = .4 had been used to forecast daily sales for apples in Problem 1, determine what the daily forecasts would have been. Then, plot the original data, the exponential forecasts, and a set of naive forecasts on the same graph. Based on a visual comparison, is the naive more accurate or…
- 1. Compute three-period moving average and forecasting errors for the following time series: Period (t): 1 2 3 4 5 6 7 8 9 10 Value (Xt): 15 27 20 14 25 11 15 20 25 22 compute mean absolute deviation (MAD) and mean square error (MSE) and interpret the obtained results. 2. What do you understand by the analysis of time series, explain in details? Give a brief introduction of forecasting.Consider the following time series data: Week 1 2 3 4 5 6 Value 18 13 16 11 17 14 2) Refer to the time series data in Exercise 1. Using the average of all the historical data as a forecast for the next period. Compute the following measures of forecast accuracy i)Mean absolute error ii)Mean squared error iii)Mean absolute percentage error iv)What is the forecast for week 7?The demand (in number of units) for Apple iPad over the past 6 months at BestBuy is summarized below. Month Nov 2019 Dec 2019 Demand 45 48 Jan 2020 50 Feb 2020 Mar 2020 Apr 2020 42 46 51 Consider the following three forecasting methods: • Two-month weighted moving average, with weights 6 and 2 (more weight assigned to more recent data) Exponential smoothing with a = 0.7. Let the initial forecast for Nov 2019 be 46. • A trend line projection in the form ŷ = a+bx . To simplify computations, transform the value of x (time) to simpler numbers – designate Nov 2019 as x=1, Dec 2019 as x= 2, etc. (a ) For each of the above methods, forecast the demand of Apple iPad for May 2020. (b) Consider only the two-month weighted moving average method, compute the MAD measure and the MSE measure using the data from Jan 2020. (c) Use the trend line to forecast the demand of Apple iPad for Dec 2020. Give your opinion regarding the reliability of the forecast.