The plot of the time series helps to decide about the best model to be used for forecasting O a True Ob. False
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A: Given Information: Sales in September: 120 units Forecast in September: 100 units Alpha = 0.3…
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A: There is a difference between forecasting and finding the accuracy of the forecast and one might…
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A: MSE stands for Mean squared error. It calculates the average squared difference between the forecast…
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A: Since you have submitted a question with multiple sub-parts as per guidelines we have answered the…
Q: Mean Absolute Deviation (MAD) is the always the best in assessing a forecast model accuracy
A: Answer is option (A)
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A: Exponential smoothing is a time series forecasting method. It calculates a weighted average of past…
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A: Forecast = Actual demand of previous period*alpha+(1-alpha)*forecast of previous period Error =…
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A: Exponential smoothing is a forecasting method which identify the forecasting value using the…
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A: Forecasting is the process of estimation in which future demand is determined using previous or…
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A: Given Smoothing constant a=0.3 Forecast for September = 100 cameras Sales in September = 120 cameras
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A: Simple Linear Regression Y = a + b*X a = Intercept b = Slope X = Independent Variable (Year )
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A: Given information:
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A: F(t) = F(t-1) + (Alpha * (A(t-1) - F(t-1))) Where F(t-1) is the forecast for the previous period and…
Q: Using your own words, describe the drawbacks of the moving average forecasting model and the…
A: Definitions Moving average: - A forecast which is made by taking the average or weighted average of…
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A: Formulae used: (i) Exponential Smoothing:F(t+1)=α At +(1-α) Ft where, F(t-1)= forecast for the…
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A: In order to increase the responsiveness of the forecast model using exponential smoothing, we need…
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A: Naive forecasting is an forecast estimation technique in which the current period forecast is equal…
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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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Q: Explain what are the benefits of exponential smoothing over moving average forecasting
A: The table below gives a prediction of the advantages of moving average over exponential smoothing.
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A: Forecasting is a technique used to predict future outcomes on the basis of past data. In business…
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A: Forecasting is the process of making assumptions of the future on the basis of past and present…
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A: Forecasting involves victimization of past knowledge to come up with a variety, set of numbers, or…
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A: Ans// D) Weighted moving average Time series forecasting makes the prediction about the future by…
Q: Forecast bias is useful to determine a. Seasonality b. Trends c. if forecast error is…
A: A forecast bias happens when there are differences between actual outcomes and previously generated…
Q: exponential smoothing superior to moving averages
A: Remarkable smoothing is a general guideline method for smoothing time arrangement information…
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A: Forecast for Friday using naive approach = Actual demand of previous period(Thursday) = 12.
Q: Briefly mention the five characteristics of data patterns in time series method of forecasting.
A: Time series forecasting happens when making a scientific projection based on documented or…
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A: Absolute error is the error between the forecasted value and the actual value of the set.
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A: A) Month Units Sold May 1500 June 1400 July 1800 August 1500…
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A: Forecasting in the business management is described as the process through the probable demand in…
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A: The equation for exponential smoothing is- Ft = F t-1 + α(A t-1 – Ft-1) Ft = the exponentially…
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A: To be determined: Forecasting with exponential smoothing has been compared to driving a car while…
Q: 2. Simple moving average using Excel: Calculate demand forecast for weeks 6-20 using 5 week simple…
A: Forecasting is predicting the demand/sales in advance for future periods. Moving average forecast is…
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A: Forecasting refers to the statistical technique used for predicting the future demand and sales of…
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A: Find the answer below: The Correct answer is a) a naïve forecast
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A: Seasonal adjustment is a strategy for information smoothing that is utilized to foresee monetary…
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A: As specified, I have solved the second question for you. Kindly find it's answer ahead and post the…
Q: Forecasting The manager of a popular tourist resort wants to use the manual trend projection…
A: The benefit of the forecasting technique is 1) Cost reduction2) Inventory reduction and management…
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A:
Q: FORECASTING - Linear Regression General instruction: Solve the following problem as directed. Show…
A: The excel output for the above mentioned problem is as follows,
Q: The problem below looks at forecasting methodologies to determine which forecasting model results in…
A: Formulae: For 3 period moving average (SMA) Simple moving average(Ft) = At-1+At-2+At-33Weighted…
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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 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_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 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_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?A small computer chip manufacturer wants to forecast monthly ozperating costs as a function of the number of units produced during a month. The company has collected the 16 months of data in the file P13_34.xlsx. a. Determine an equation that can be used to predict monthly production costs from units produced. Are there any outliers? b. How could the regression line obtained in part a be used to determine whether the company was efficient or inefficient during any particular month?The management of a technology company is trying to determine the variable that best explains the variation of employee salaries using a sample of 52 full-time employees; see the file P13_08.xlsx. Estimate simple linear regression equations to identify which of the following has the strongest linear relationship with annual salary: the employees gender, age, number of years of relevant work experience prior to employment at the company, number of years of employment at the company, or number of years of post secondary education. Provide support for your conclusion.