Total Revenues | 20X1 | $15‚000‚000 | 20X2 | $14‚250‚000 | 20X3 | $14‚000‚000 | 20X4 | $13‚500‚000 | Forecasting the total revenues for fiscal year 20X5 I will use the moving averages‚ weighted moving averages‚ exponential smoothing‚ and time series regression. Moving Averages Fiscal Year | Total Revenues
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be divided into two categories: time series models and causal models. Quantitative Methods Time Series Models Causal Models Time series models look at past patterns of data and attempt to predict the future based upon the underlying patterns contained within those data. Causal models assume that the variable being forecasted is related to other variables in the environment. They try to project based upon those associations. TIME SERIES MODELS Model Description Naïve
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OPR1010 Operations Management (Winter 2010) In-class Assignment 2: Forecasting Directions: ( We will check the answers during the supplemental session on Feb. 18. (Participation points will be considered for volunteers. (This is not a take-home assignment. You do not have to turn in the answers. (Use MS-Excel for Questions 1 through 4. Q-1. The Polish General’s Pizza Parlor is a small restaurant catering to patrons with a taste for European Pizza. One of its specialties is Polish Prize
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Financial and Economic Forecasting The Civilian Unemployment Rate By: Doug Hanig Due: 5/15/12 Doug Hanig Professor Hecht Finc-411 3/12/12 Part 1 A. Civilian Unemployment Rate (FRED Database) Government Agency: US Department of Labor: Bureau of Labor statistics B. The government would be interested in this forecast for many reasons. By forecasting the civilian unemployment rate‚ the government can have an idea of how stable
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seem better? 4.18. Consider the following actual (A1) and forecast (F1) demand levels for a product: Time Period (t) Actual Demand (A1) Forecast Demand (F1) 1 50 50 2 42 50 3 56 48 4 46 50 5 49 The first forecast F1 was derived by observing A1 and setting F1 equal to A1. Subsequent forecasts were derived by exponential smoothing. Using the exponential smoothing method‚ find the forecast time for period 5. (Hint: You need to first find the smoothing constant‚ α.) To find α: 50= 50 + α(42-50)
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for a division with a brand new product‚ but it would not be as helpful or efficient for a division where there has been steady growth and established products for the past five years. The other suggestion I have would be to not begin the market analysis with reviewing the sales in each division of the company. If he would like to know more about the market‚ it would make more sense to use the build-up approach to conduct primary research. It would benefit the company because certain divisions are
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annual inventory times the average inventory dived by 2)d) The total annual ordering cost• Cost to place order is $50 x 4= $200e) The total annual cost• $3 per unit x 1300units= $3‚900• $3‚900 x 4 times a year = $15‚600• Cost to place order is $50 x 4= $200• $15‚600+200= $15‚800 total annual cost6. A local nursery‚ Greens‚ uses 1560 bags of plant food annually. Greens works 52 weeks per year. Itcosts $10 to place an order for plant food. The annual holding cost rate is $5 per bag. Lead time isone week
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co-owner Amit wants to forecast by exponential smoothing by initially setting February’s forecast equal to January’s sales with α=1. Co-owner Barbara wants to use a three-period moving average. 1. Is there a strong lineal trend in sales over time? 2. Fill in the table with what Amit and Barbara each forecast for May and the earlier months‚ as relevant. 3. Assume that May’s actual sales figure turns out to be 405. Complete the table’s columns and then calculate the mean absolute
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University of Bristol - School of Economics‚ Finance and Management QUANTITATIVE METHODS FOR FINANCE AND INVESTMENT (EFIMM005) Review Questions Question 1: Concepts a. Define a stochastic process. Give an example in Finance of a quantity that can be modelled as a stochastic process. b. Define a stationary stochastic process. c. Consider a stochastic process {Yt ‚ t = 1‚ ..‚ T }. Define the partial autocorrelation function (pacf) associated to this process. d. Explain the difference between estimator
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Northcutt Bikes Case Answers 1 Q1: Demand Data Plot 2 Q1: Plot Shows There is seasonality There is a trend Forecast should take into account both 3 Construction of base indices Year: January February March April May June July August September October November December Mean Demand: 2008 0.53 0.74 0.88 1.09 1.10 1.60 1.29 1.19 1.00 1.09 0.73 0.74 2009 0.72 0.74 0.84 1.00 1.16 1.57 0.94 1.30 1.13 0.74 0.99 0.88 818.42 990.50 2010 0.59 0.95 0.79 1.18 1.15 1.39 1.35 1.43 0.91 0
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