To calculate a forecast's percent error, the forecast error is divided by what?
Q: Canton Supplies, Inc., is a service firm that employs approximately 100 people. Because of the…
A: Find the given details below: Given details: Month ($ 1000) 1 186 2 219 3 216 4 270…
Q: snip
A: Answer: It is important to measure the accuracy of forecasts, for any forecasting technique and…
Q: Explain how do we measure accuracy of a forecasting model
A: We utilize the following criteria to determine a prediction model's efficiency:
Q: Refer to Problem 4.2. Develop a forecast for years 2 through 12 using exponential smoothing with a =…
A: Given data is Alpha = 0.4 Forecast for year 1 = 6
Q: What is the forecast using exponential smoothing with alpha = .6? 2. If we decide to…
A: ANSWER IS AS FOLLOWS:
Q: Discuss the basic assumptions made when using time series forecasting techniques as opposed to…
A: Several assumptions are made during the Time Series Initial Phase.
Q: Complete the forecasting worksheets for: Naïve Average Moving Average Weighted Moving Average…
A: Weighted Moving Average using the weights of .8, .15, and .05 ExponA using and an alpha level of .75…
Q: The Grand Bakery produces 60 special sourdough rolls every day. Any rolls that are not sold each day…
A: Forecasting is the process of prediction in which sales demand is estimated using historic…
Q: Which of the following smoothing constant would make an exponential smoothing forecast equivalent to…
A: alpha of 1.0 leads to an exponential smoothing forecast similar to a naive forecast.
Q: What are the basic assumptions made when using time series forecasting techniques as opposed to…
A: Stationarity: The first assumption is that the series of data points are stationary. The series is…
Q: Explain the trade-off between responsiveness and stability in a forecasting system that uses…
A: Time Series Data: statistic knowledge is outlined as during an amount of your time,…
Q: 2
A: Forecasting is a strategy that uses previous data as inputs to generate informed predictions about…
Q: A police station had to deploy a police officer for an emergency multiple times in the last four…
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: Describe and evaluate the method of forecasting based on a time series analysis when a trend is…
A: Forecasting is the practice of estimating the size of unknown future events and generating different…
Q: Explain why forecasts are generally wrong.
A: Forecasting is used to predict future changes or demand patterns.
Q: Compute a 3-month weighted average forecast for months 4 through 9. Assign weights of 0.55, 0.33 and…
A: Forecasting is a technique used to predict future outcomes on the basis of past data. There are…
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: What is a time series and the rationale for forecasting based on a time series analysis?
A: Forecasting refers to the prediction of the future based on some evidence or a strong base.…
Q: What does the term biased mean in reference to a particular forecasting technique?
A: The forecasting techniques are used for predicting the future demand and sales of the product. The…
Q: If the Tracking Signal for your forecast was consistently positive, what could you then say this…
A: If the tracking signal of the forecast is always positive, then it is bias and consistently too low.…
Q: Is there anything that can be done to boost the Forecast technique
A: Forecasting is a technique for forecasting potential demand, assessing risk, and analysing patterns.…
Q: Three popular measures of forecast accuracy are:a) total error, average error, and mean error.b)…
A: Forecast accuracy is important because it ensures the reliability and validity of data. Forecasting…
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.
Q: Explain what ex-post and ex-ante forecasts are, and how one can evaluate the accuracy of forecast of…
A: Ex Post Forecast, Ex Ante Forecast Ex post is forecasting using data that has been collected after…
Q: exponential functions for trend data. Assume an initial exponential Forecast of 620 units in period…
A: Below is the solution:-
Q: A production company was able to hit the following sales order for the past 3 months: 45% of 420pcs…
A: Given data is
Q: Sunrise is planning its purchases of ingredients for bread production. If bread demand had been…
A: Exponential smoothing could be a statistic statement technique for univariate information that may…
Q: What benefits does exponential smoothing have over moving averages as a forecasting tool?
A: As a forecasting function, exponential smoothing has the following benefits over running averages:…
Q: State the assumptions made when using a time series forecasting techniques
A: Numerous estimates are taken in statistical analysis.
Q: What implications do forecast errors have for the search for ultrasophisticated statistical…
A: Forecasting is the process of making predictions for the future based on the past and present data.…
Q: Suppose you are working for a baking company in Bangladesh. What are the relevant factors you will…
A: Forecasting is the activity of making estimations of future activities based on past and present…
Q: Mary, Susan, and Sarah are running a beach boutique on the board walk of Ocean City. Their favorite…
A: Find the Given details below: Given details: Person Forecast data Mary 318 Susan 518…
Q: Choose the type of forecasting technique (survey, Delphi, averaging, seasonal, naive, trend,…
A: Seasonal forecast is a type where the prediction is done only in that particular season. This is…
Q: exponential smoothing superior to moving averages
A: Remarkable smoothing is a general guideline method for smoothing time arrangement information…
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: An example of the Quantitative Method of forecasting is
A: Businesses and salespeople can use quantitative forecasting, an objective, data-based process, to…
Q: mon forecasting techniques.
A: It is possible to describe forecasting as a method of making predictions about the future based on…
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: Forecasts are generally wrong.a. Why are forecasts generally wrong?b. Explain the term “wrong” as it…
A: Forecasting generally means predicting or estimating something for future events. It is also about…
Q: A check-processing center uses exponential smoothing to forecast the number of incoming checks each…
A: The forecasting technique is used to make predictions based on past and present data. Use in the…
Q: A restaurant wants to forecast its weekly sales. Historical data (in dollars) for 15 weeks are…
A: Forecasting refers to the statistical technique used for predicting the future demand and sales of…
Q: Generate forecasts for data with diff erent patterns, such as level, trend, and seasonality and…
A: Solution Introduction with Generate Forecasting for data Forecasting is a logical extension of the…
Q: The following table shows predicted product demand using your particular forecasting method along…
A: From the above given information, we have to compute the tracking signal of each period using the…
Q: A concert promoter is forecasting this year's attendance for one of his concerts based on the…
A: The concept of Operation Management: Operation management is the management that applies to a…
Q: a. Calculate the simple three-month moving average forecast for periods 4 to 12.
A: Since you have asked multiple questions, we will solve the first question for you. If you want any…
Q: How can you evaluate the accuracy of a forecast model? explain in detail
A: Forecasting is the process of making assumptions of the future on the basis of past and present data…
Q: It has been said that forecasting using exponential smoothing is like driving a car by looking in…
A: Exponential smoothing is a time series forecasting technique for univariate data that can be…
snip
Step by step
Solved in 2 steps
- 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 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_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 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_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_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?
- 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.At the beginning of each week, a machine is in one of four conditions: 1 = excellent; 2 = good; 3 = average; 4 = bad. The weekly revenue earned by a machine in state 1, 2, 3, or 4 is 100, 90, 50, or 10, respectively. After observing the condition of the machine at the beginning of the week, the company has the option, for a cost of 200, of instantaneously replacing the machine with an excellent machine. The quality of the machine deteriorates over time, as shown in the file P10 41.xlsx. Four maintenance policies are under consideration: Policy 1: Never replace a machine. Policy 2: Immediately replace a bad machine. Policy 3: Immediately replace a bad or average machine. Policy 4: Immediately replace a bad, average, or good machine. Simulate each of these policies for 50 weeks (using at least 250 iterations each) to determine the policy that maximizes expected weekly profit. Assume that the machine at the beginning of week 1 is excellent.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.