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A: Sept. 7 forecast = Aug. 31 forecast + alpha (Aug. 31 demand- Aug. 31 forecast) Sept. 7 forecast =…
Q: Explain why forecasting devices such as moving averages, weighted moving averages, and exponential…
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A: Mean absolute percent error is the method to describe the degree of relationship between errors for…
Q: Sales of tablet computers at Ted Glickman's electronics store in Washington, D.C., over the past 10…
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Q: Explain why forecasts are generally wrong.
A: Forecasting is used to predict future changes or demand patterns.
Q: snip
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A: The image given in step 2 gives a detailed solution of the question that has been asked.
Q: Demand and forecast for 5 months are given below: Month Demand Forecast 1 50 46 2 52 52 3…
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Q: What method would you choose of forecasting technique, which requires subjective inputs obtained…
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A: Forecasting is a methodology that uses past information as input to make well-informed predictions…
Q: What is the term for forecasts used for making day-to-day decisions about meeting demand?
A: Forecasting is a technique used to predict future events using quantitative and qualitative methods…
Q: snip
A: The quantitative forecasting techniques require the past relevant data, the absence of this makes…
Q: All forecasts are subject to error. Do you think topmanagers would be concerned about the effectson…
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Q: What effect does the number of cycles in a moving average have on the forecast's responsiveness?
A: In order to estimate potential demand, the Moving Average (MA) projection method uses the MA formula…
Q: What is seasonality?How do we forecast using data that has seasonality?
A: Seasonality in time series data is the occurrence of repetitive up and down cycles in series values…
Q: Forecast sales for the 11th period. For leveling, use exponential smoothing 0.20 and moving average…
A: Use exponential formula = α×Actual demand+(1-α)×previous demand
Q: Describe why such forecasting devices as moving average , weighted averages and exponential…
A: To be determined: why such forecasting devices as moving average , weighted averages and…
Q: Discuss why are forecasts generally wrong
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A: Globalization in business is inevitable is one of the most credible forecasts over the next decade.
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A: Formula: Answer:
Q: What does the term "adaptive forecasting" mean?
A: Forecasting is nothing more than forecasting patterns and making potential forecasts based on…
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- Under what conditions might a firm use multiple forecasting methods?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_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 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 most naive forecast can is quite valuable in leading to an organization’s success because it is most widely understood by senior managers. True or FalseForecasting as you have read isn't an exact science. There can be many intangibles that you just can't predict. Therefore, which forecasting method do you believe is most successful and which one do you think is least effective? Please explain.
- Forecasting as you have read isn't an exact science. There can be many intangibles that you just can't predict. Which forecasting method do you believe is most successful and which one do you think is least effective? Please explain.Demand and forecast for 5 months are given below: Month Demand Forecast 1 50 46 2 52 52 3 46 52 4 45 52 5 52 45 Give your answers to three decimal places. Find MSE of this forecast model Find the tracking signal for month 1 month 2 month 3If a forecast can be made using a quantitative model, a forecaster need not use her personal opinion or subjective judgment anymore. True False
