Using the given table compute for Mean Absolute Deviation (MAD). Using the given table compute for Mean Square Error (MSE). Using the given table compute for MAPD/MAPE.
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- 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 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 forecasting method has produced the following over the past five months. What is the mean absolute percentage error (MAPE)? Actual 11 OA. 0.00% B. 31.87% 5178 11 13 Forecast 13 10 30668 $7Practice Problem: Usins the data on a hospital’s revenue: (1) Use simple exponential smoothing to make predictions for the hospital’s revenues during the next four quarters with α = 0.30. (2) Use Trend-Adjusted Exponential Smoothing(i.e. Holt’s method) to make forecasts for the hospital’s revenues during the next four quarters. Assume α = 0.05, β = 0.65, initial revenue forecast = 1209285.75 and initial trend forecast = 15714. (3) Using least-squares regression with seasonal index decomposing method, forecast the hospital’s revenues during the next four quarters.14.5. For the data in Exercise 14.4, use an a of 0.1 to make a forecast for July F Ft-1 + 0.1(At-1-Ft-1) Months January 15 February 18 March 22 23 27 26 April May June Demand Smoothing unit (At) (St) July 15 15.3 15.97 16.67 17.7 18.53 Forecast for July= 18.53 Forecast (Ft) 15 15.3 15.97 16.67 17.7 18.53
- Q1) Forecasting is both art and science. Describe the situations where we use Qualitative forecasting techniques and quantitative forecasting techniquesList the specific weaknesses of each of these approaches to developing a forecast:c. Committee of managers or executives.Here are the data for the past 21 months for actual sales of a particular product: LAST YEAR THIS YEAR January 315 February 418 March 390 April 430 May 400 [ June 480 July 430 August 320 September 385 October 530 November 592 555 December 300 400 350 460 390 395 355 245 340 Develop a forecast for the fourth quarter using a three-quarter, weighted moving average. Weight the most recent quarter 0.50, the second most recent 0.25, and the third 0.25. Do the problem using quarters, as opposed to forecasting separate months. (Round your answer to 2 decimal places.) Forecast for the fourth quarter
- 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.You have the following data: Actual Forecast 1 50 50 2 46 3 52 4 51 5 48 6 45 7 52 8 46 9 51 10 48 Compute the forecast using exponential smoothing with Using MSE, which of the above gives the best forecast of "Actual"? A) 0.8 B) 0.6 C) 0.4 D) 0.1 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.3. A mobile phone store owner wants to predict the demand for mobile phones in October based on the following historical sales data: Month- April May June. July August September Number of phones sold. 100, 140- 110. 150. 120. 160- a. What is this month's forecast using Naive approach b. Using 3-Month Moving Average, develop forecasts for October's demand c. Using 5-Month Moving Average, develop forecasts for October's demand d. When making moving average forecasts, is it better to use a larger time span? -
