Both time series and causal forecasting assume that the past relationship between demand and the independent variable(s) will continue on into the future. True False
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A: Forecasting is the process of making assumptions of the future on the basis of past and present data…
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A:
Q: Explain the term for forecast that is used for making day to day decisions about meeting demand
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A: The time series plot shows a horizontal pattern.
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A: alpha of 1.0 leads to an exponential smoothing forecast similar to a naive forecast.
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A: The life cycle of a product defines the different stages from its beginning to its end in the market…
Q: Obtain the linear trend equation for the following data on new checking accounts at Fair Savings…
A: Linear trend equation is given by: y = a+bx here, a = intercept and b = slope Formulas used: a = ∑y…
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A: Tracking signal, as the name suggests, is a way to evaluate the forecast in comparison to actual…
Q: DEMAND FORECAST WITH LINEAR REGRESSION Historical demand for a product is: Period MONTH DEMAND x2 XY…
A: Period (x) Month Demand (y) 1 January 12 2 February 11 3 March 15 4 April 12 5 May 16 6…
Q: When calculating the moving average forecast using Excel, ____ is entered into the “Interval” box in…
A: The moving averages method uses the average of the most recent data values in the time series as the…
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A: Using the given Forecasting model the forecast for air conditioner at various level of temperature…
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A: Forecasting is a technique of predicting future events based on historical data and projecting them…
Q: A time-series forecasting model uses a series of past data points to make a forecast. True False
A: Forecasting is the process of prediction in which sales demand is estimated using historic…
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A: Forecast accuracy is important because it ensures the reliability and validity of data. Forecasting…
Q: Moving Average method is always superior to Weighted moving average method for time series forecast
A: The moving method average is
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A: Below is the solution:-
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: 2-The correlation between rate and base are called the dynamic forecast. Select one: O True O False
A: Correlation is described as the relationship that exists between two different variables…
Q: Apply 4-period SMA and 4-period WMA (0.1, 0.2, 0.3, 0.4) to the given data. Determine the MAD, MSE,…
A: THE ANSWER IS AS BELOW:
Q: snip
A: Forecasting is a technique of estimating or predicting future trends with the help of surveyed data…
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A: Y = 40 + 4.20x Where, Y = Demand for Air Conditioners X = Outside temperature
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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…
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A: The accuracy of the forecast can be determined by comparing the actual or real values with the…
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: Qualitative forecasts and causal forecasts are not particularly useful as inputs to inventory and…
A: Qualitative forecasts and casual forecasts are not specifically helpful as inputs to the inventory…
Q: discuss
A: The answer to this question is false.
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A: The given question is about exponential smoothing.
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A: The equation for exponential smoothing is- Ft = F t-1 + α(A t-1 – Ft-1) Ft = the exponentially…
Q: Which of the following is true about naive forecasting? a. It involves a two-period shift between…
A: Naive Forecasting is an estimation technique in which the actual value of the last period is used as…
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A: Exponential smoothing is a forecasting method for a data with 1 variable that can be used to…
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Q: The moving average forecast method should only be used with time series data demonstrating a linear…
A: A moving average, which is indeed the average of any subset of values, is a method for gaining a…
Q: State and explain the weakness of standard forecasting technique in forecasting approaches
A: To be determined: the weakness of standard forecasting technique
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A: Adjusted exponential smoothing is a forecasting methodology that employs measurable and historical…
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,
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A: Note: - Since we can answer only up to three subparts we will answer the first three(1, 2, and 3)…
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- 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 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.
- 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 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_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?Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P13_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo.
- 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.A time-series forecasting model uses a series of past data points to make a forecast. True False
