Mean Absolute Deviation (MAD) is the always the best in assessing a forecast model accuracy
Q: a. Obtain the linear trend equation for the following data on new checking accounts at Fair…
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A: Find the given details below: Given details: Month ($ 1000) 1 186 2 219 3 216 4 270…
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Q: Choose the type of forecasting technique (survey, Delphi, averaging, seasonal, naive, trend,…
A: The types of forecasting technique (survey, Delphi, averaging, seasonal, naive, trend,…
Q: which of the following is a technique used to determine forecasting accuracy A. Mean Absolute…
A: There is a difference between forecasting and finding the accuracy of the forecast and one might…
Q: Given forecast errors of 4, 8, and −3, what is the mean absolute deviation (MAD) and mean square…
A: Forecast errors = 4,8 and -3 Absolute errors = 4, 8 and 3
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A: alpha of 1.0 leads to an exponential smoothing forecast similar to a naive forecast.
Q: Which of the following is used to describe the degree of forecast error? a. Median and Mode b. Mean…
A: Mean absolute percent error is the method to describe the degree of relationship between errors for…
Q: d) Calculate the trend projection with regression forecast for periods 7 through 10. The regression…
A: Forecasting is the ability to predict future happenings using different forecasting methods.
Q: snip
A: An exponential smoothing forecast becomes more responsive to changes in a data series when its alpha…
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A: In order to increase the responsiveness of the forecast model using exponential smoothing, we need…
Q: If the tracking signal for your forecast was consistently positive, you could then say this about…
A: Tracking signal, as the name suggests, is a way to evaluate the forecast in comparison to actual…
Q: Do you think that hard rock cafe makes use of time horizons when forecasting?
A: The forecast horizon is that the duration of your time into the destiny that forecasts are to be…
Q: You discovered that the forecasting error falls beyond the acceptable ranges in the past three…
A: Find the given details below: Given details: Period Actual Forecast (A-F) Error (A) (F)…
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A: In this question, we have the table data for an 8 periods duration, for each period, we have actual…
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A: Forecasting: Forecasting can be termed as prediction of future sales or demand of a product. It is a…
Q: Exercise # 2 –Calculating MADt, Revised MADt,Error,and the Revised Error :Has the Forecast Improved…
A: 2 period moving average forecast for revising: -
Q: Three popular measures of forecast accuracy are:a) total error, average error, and mean error.b)…
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A: Given data is
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A: Forecasting is a methodology that uses historical data as inputs to make informed predictions of…
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A: Forecasting is a tool that uses historical data as inputs that are predictive in deciding the path…
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A: Month (2020) Sales May 50…
Q: snip
A: To calculate a forecast’s percent error, the forecast error is divided by actual values.
Q: c. Using simple exponential smoothing, what would your forecast be for this month if the…
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Q: What is the connection between using a tracking signal and statistical control limits for forecast…
A: The monitoring indicator is a statistic that is used to determine whether the actual demand fits the…
Q: Ordinary least squares technique or linear regression analysis
A: THE ANSWER IS AS BELOW:
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A: Mean absolute deviation determines the prediction's accuracy by averaging the specified inaccuracy…
Q: What is the purpose of establishing control limits for forecast errors?
A: Forecast errors are described as the difference between the forecast of a particular period and that…
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A: Forecast for Friday using naive approach = Actual demand of previous period(Thursday) = 12.
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A: Qualitative forecasts and casual forecasts are not specifically helpful as inputs to the inventory…
Q: What is the mean absolute deviation (MAD)? Why is it useful in forecasting?
A: Mean absolute deviation helps in understanding how accurate and reliable your forecast are . And it…
Q: Exponential Smoothing gives always better results than any other similar method used for time-series…
A: Forecasting in the business management is described as the process through the probable demand in…
Q: snip
A: A moving average forecast becomes less responsive to change in a data series when more data points…
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A: M5TV Appearances(X) Demand for Guitars (Y) XY X2 Y2 3 2 6 9 4 3 5 15 9 25 8 6 48 64 36 5 4…
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Q: a) Calculate the forecasted registrations for years 2 through 12 using exponential smoothing, with a…
A: ANSWER IS AS FOLLOWS:
Q: Forecasting The manager of a popular tourist resort wants to use the manual trend projection…
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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?
- 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_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_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?
- 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_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?Do the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.
- The file P13_25.xlsx contains the quarterly numbers of applications for home mortgage loans at a branch office of Northern Central Bank. 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? Is it guaranteed to produce better forecasts for the future?2. What is the Mean Absolute Deviation (MAD)? 3. Use the following set of data to calculate the Mean Absolute Deviation (MAD) for the following set of data. Month Actual (At) Forecast (Ft) Forecast Error Absolute (Deviation) Forecast Eror January February 45 45 42 50 March 34 45 April May 48 40 38 45 MAD =The owner of Leisure Boutique in a local mall is reviewing their forecasting model for errors. Month Actual Forecasted March 64 63 April 66 67 May 70 71 June 78 75 July 80 79 August 84 83 September 92 87 October 94 91 What is the cumulative forecast error? Group of answer choices a) -16 b) 16 c) 12 d) -12