Weighted Moving Averages Fiscal Year Expenses Weight Weighted Score 20X2 $5‚500‚000 1 $5‚500‚000 20X3 $6‚000‚000 2 $12‚000‚000 20X4 $6‚750‚000 3 $20‚250‚000 __ ___________ 6 $37‚750‚000 20X5 $37‚750‚000 /6 = $6‚291‚667 Exponential Smoothing NF = $6‚300‚000 + 0.95($6‚750‚000 - $6‚300‚000) = $6‚300‚000 + 0.95(45‚000) = $6‚300‚000 + (42‚750) = $6‚342‚750 Exercise 9.3 The following data represent total revenues (from all sources) for the Palmdale Human
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together. There are various types of forecasting methods such as: Qualitative study‚ Time series analysis‚ Causal method etc. For this particular assignment‚ we have used some methods of Time series analysis like Simple Moving Average‚ Single Exponential Smoothing‚ and Regression Analysis etc. Various models‚ mostly quantitative time series models have been used to determine the forecasted future monthly sales quantity of Laptops for the month of November 2013. For the simplicity of the work‚ actual
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Quant Formula Study Guide MISCELLANEOUS‚ COMMONLY USED FORMULAS Finite population correction factor: Multiply SE of sample mean by fpc to make the correction ------------------------------------------------- Independent samples of same population with same standard deviation (variances are equal). Confidence interval: df for t-multiple is (df1 + df2)‚ or (n1 – 1) + (n2 - 1) Pooled estimate of common standard deviation: SE of difference between two sample means -------------------------------------------------
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CASE ANALYSIS: WILKINS‚ A ZURN COMPANY: DEMAND FORECASTING Submitted By Group 3: Arunava Maity‚ Firoj Kumar Meher‚ Parvez Izhar‚ Pooja Sharma The Case Scope: Section 1: Identification of current forecasting techniques used in the demand forecasting of existing and new products. Section 2: Idenitification of a better forecasting technique which can ease the process and improve the reliability and accuracy of the sales forecast. The Case Background Notes: Wilkins Regulator Company had
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Question 1 .5 out of 5 points The __________ is the maximum value that one would be willing to pay for additional information Answer Selected Answer: expected value of perfect information . Question 2 .0 out of 5 points The credit scores of a certain population are approximately normally distributed with a mean of 645 points and a standard deviation of 65 points. The credit score of an individual should belong to the top 5% of the credit scores in order to qualify for a home loan
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accurate in our forecasting. Forecasting is an uncertain process and therefore a high accuracy is demanded. There are many forecasting techniques in the world. In general‚ they can be classified into three types: casual model‚ time-series model and smoothing techniques. Undoubtedly‚ they are of different features and thus are suitable for prediction under certain circumstances. For casual model‚ the most commonly used technique is simple linear regression model. In order to study the seasonal effect
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PERSPECTIVE Aniruddh Kr Singh Debadyuti Das The present paper attempts to find out the forecasted passenger traffic movement of Lufthansa Airlines on quarterly basis at a global level by employing four forecasting methods namely moving average‚ exponential smoothing‚ Holt’s model and Winter’s model with the help of published data pertaining to passenger traffic movement of Lufthansa Airlines. The study has also found out the forecasting errors of all the four methods through Absolute error (AE)‚ Mean squared
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JCrew on Blackboard under the Assignments link. 2. Get a 4 point Moving Average for the data using Time Series Analysis. 3. Highlight the Revenue column and the 4MA column. Insert /Line. 4. Go back to the data. Time Series Analysis/ Exponential Smoothing. Use alpha of .7. 5. Highlight Revenue and Smoothed and Insert /Line. 6. Go back to the data. Time Series Analysis/ Trendline / pick Exp Ln. Check the Scatterplot and all boxes on the right side. 7. Finally‚ go back to the data
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methods are: 1) Smoothing Methods 2) Trend Projection 3) Trend Projection‚ adjusted for seasonal influence (Multiplicative Model) 2 Smoothing Methods Smoothing methods are used to average out the irregular components of the time series in cases where the time series: is fairly stable‚ and has no significant trend‚ seasonal‚ or cyclical effects. • • Four common smoothing methods: 1) 2) 3) 4) Moving Average Weighted Moving Averages Exponential Smoothing Centered Moving
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suggest that forecast accuracy can be improved by either damping or ignoring altogether trends which have a low probability of persistence. This paper develops an exponential smoothing model designed to damp erratic trends. The model is tested using the sample of 1‚001 time series first analyzed by Makridakis et al. Compared to smoothing models based on a linear trend‚ the model improves forecast accuracy‚ particularly at long leadtimes. The model also compares favorably to
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