Which time-series forecasting method works best if the company assumes that product demand will decrease over time? A. Weighted moving average B. Linear trend C. Moving average D. Exponential smoothing
Q: a) Using exponential smoothing, with a = .6, then trend analysis, and finally linear regression,…
A: a)
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A: ANSWER IS AS BELOW:
Q: Which of the following measures of forecast fit may correctly be used to compare different forecast…
A: Answer as follows:
Q: Why is reliable forecasting so important for businesses using a continuous replenishment inventory…
A: Continuous Replenishment is a method in which a supplier receives regular updates on real-time sales…
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A: Forecasting can be defined as the technique which uses past data to estimate future events.…
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Q: Using 2-months moving average forecast sales from 5 to 11 months Using 4-months weighted moving…
A: A moving average is a statistical method to calculate the average change in a data series over time.…
Q: hat is one of the biggest challenges to overcome in new product forecasting? a. Selecting the best…
A: challenges in new product forecasting lack of historical data is one of the biggest challenge as…
Q: ind Naïve Forecast, Moving Average and Weighted Average based on the data given at the below table.…
A: In the question, we have monthly data for the year 2018, I would apply forecasting techniques to get…
Q: If the forecasted value of the time series variable for one period is 28.5 and the actual value…
A: Forecasted value = 28.5 Actual value = 32
Q: 3. You are using a 3 period moving average to calculate your forecast. Demand for period 1 was 90…
A: Given information:Demand period 1=90unitsDemand period 2=80unitsDemand period4=90unitsHere we have…
Q: What is meant by the term tracking the forecast? In which two ways can forecasts go wrong?
A: Tracking the forecast means comparing the actual demand with the forecasted demand. It is used to…
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A: Qualitative data forecasting techniques mainly describes the characteristics and qualities of the…
Q: is based on the principle of using only the last observation in a sequence of stable data as a…
A: Option C is correct. The naive forecast is based on the principle of using only the last observation…
Q: Why is accurate forecasting so important to companies thatuse a continuous replenishment inventory…
A: Continuous Replenishment is a method by which a supplier is told day by day of real deals or…
Q: orecast demand for each week, including week 10, using exponential smoothing with a 5 .5 (initial…
A: Exponential smoothing is forecasting method which identifying the farecasting value based on the…
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A: Trend Forecasting is the process to know the future buying habits of consumers by researching and…
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A: Forecasting is a form of making knowledgeable predictions by utilizing historical data as the major…
Q: How can the Forecast technique be improved?
A: Forecasting is a tool or technique which is used to predict future demand, risk and to analyze the…
Q: Distinguish between Planning and Forecasting. Answer must briefly.
A: Future demand is the forecasted demand for the products and services expected from the customers.
Q: AD, M Moving Average Model (past 3 Weeks), A. B. Exponential Smoothing Model (a = 0.2) с. What is…
A: Forecasting is a technique used to predict future outcomes on the basis of past data. In businesses…
Q: Thus historical demand for periods is 70, 60, 80 , and respectivelyWhat is the two-period weighted…
A: Using the 2 period weighted moving average method F(5) = 0.5*Actual (4) + 0.5*Actual(3) = 0.5*90 +…
Q: Problem 1: Sheet 1 a) Find on the Internet a time series, which contains at least 20 entries.…
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Q: Calculate (a) MAD and (b) MSE for the following forecast versus actual sales figures:…
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A:
Q: Using exponential smoothing with a weight ex of 0.6 on actual values: (a) if sales are $45,000 and…
A: Therefore, the forecast in 2012 is $48,000.
Q: Given below are the demands of a certain product over the past 10 weeks. Week | 1 | 2 | 3| 4 | 5 6 |…
A: Since you have submitted multiple questions with multiple subparts as per guidelines I have answered…
Q: How would you conduct a trend analysis? Provide an example.
A: “Since you have asked multiple questions, we will solve the first question for you. If you want any…
Q: Explain why is accurate forecasting so important to companies that use a continuous replenishment…
A: Forecasting is the practice of making future assumptions based on historical and current data, most…
Q: (3) A weighted average using.60 for August, .30 for July, and .10 for June (4) Exponential smoothing…
A: Forecasting is the process of prediction in which sales demand is estimated using historic…
Q: 2. Using the same table above, compute the weighted moving average forecast for demand for four…
A: Forecasting is a technique used to predict future outcomes on the basis of past data. In businesses…
Q: What is Use a naive method to make a forecast?
A: Naïve method of forecasting is a simple forecasting method where the sales or demand of the previous…
Q: Given the actual demand of 300, the previous forecast of 300, and an alpha of 0.048 , which one of…
A: The actual demand =300 The previous forecast = 300 Alpha = 0.048
Q: October 5 a) Forecast the demand for the week of October 12 using a 3-week moving average. b) Use a…
A:
Q: Forecast for the 11 quarter using exponential smoothing technique.
A:
Q: Given an actual demand this period of 105, a forecast value for this period of 100, and an alpha of…
A: n exponential smoothing forecast, Next forecast = Previous forecast+α(Actual-Previous forecast)
Q: Forecast based on averages. Given the following data: Period Number of Complaints 1 70 2 75 3…
A: Forecast for Period 6 using weighted average = 0.5*Demand of Period 5 + 0.3*Demand of Period 4 +…
Q: Q2: Daily high temperature in St. Louis for the last week were as follows: 93, 94, 93, 95, 96, 88,…
A: Formula:
Q: Justify exponential smoothing's superiority to moving averages as a forecasting method
A: In today's climate, at which events keep changing, the quantile approach is superior.
Q: a. Naive b. A four-period moving average c. Exponential smoothing with a = .30; use 20 for week 2…
A: Forecasting is the process of prediction in which sales demand is estimated using historic…
Q: Local city government statistics show the rate of new driver’s license applications to be as…
A:
Q: Using the double exponential smoothing forecast, is it possible to forecast a demand thatis bigger…
A: Forecasting is a prediction method that can use historical data and current market trends and…
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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 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.Under what conditions might a firm use multiple forecasting methods?
- 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_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_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?
- 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_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?
- 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.Management of a home appliance store wants to understand the growth pattern of the monthly sales of a new technology device over the past two years. The managers have recorded the relevant data in the file P13_05.xlsx. Have the sales of this device been growing linearly over the past 24 months? By examining the results of a linear trend line, explain why or why not.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?