Statistics For Management 1. Introduction 2. Statistical Survey 3. Classification‚ Tabulation & Presentation of data 4. Measures used to summarise data 5. Probabilities 6. Theoretical Distributions 7. Sampling & Sampling Distributions 8. Estimation 9. Testing of Hypothesis in case of large & small samples 10. Chi-Square 11. F-Distribution and Analysis of variance (ANOVA) 12. Simple correlation and Regression 13. Business Forecasting 14. Time Series Analysis 15 . Index Numbers Indian
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1 100 3 130 2 110 4 140 5 160 ══════════════════════════════════════════════ perform a regression analysis and forecast sales for the next two years: Exponential Smoothing 3.33 The Sporting Charge Company buys large quantities of copper that is used in its manufactured products. Bill Bray is developing a forecasting system for copper prices. He has accumulated this historical data: Copper
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Forecasting Models: Associative and Time Series Forecasting involves using past data to generate a number‚ set of numbers‚ or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning. Time Series and Associative models are both quantitative forecast techniques are more objective than qualitative techniques such as the Delphi Technique and market research. Time Series Models Based on the assumption that history will repeat itself‚
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Executive Summary Greaves Brewery is a growing beer operation based out of Trinidad. The purchasing manager for the brewery finds himself struggling in finding a balance between ordering enough bottles to support sales; yet minimizing over ordering to avoid issues associated with growth decelerating trend from an off year‚ continued impact from government excise tax‚ tourism‚ and growth of exports particularly the USA. In addition to previously mentioned concerns ordering the right number
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equipment would remain in effect‚ as shown below. Forecasting is the “art and science of predicting future events” (Heizer & Render‚ 2010). There are different methods available for forecasting; the focus will be on least-squares method and exponential smoothing with trend adjustment. One important thing that will be analyzed is how to measure forecast error by using three of the most popular measures. Forecast error shows “how well the model performed against itself using past data” (Heizer &
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UNIT 6 DEMAND ESTIMATION AND FORECASTING Objectives By studying this unit‚ you should be able to: identify a wide range of demand estimation and forecasting methods; apply these methods and to understand the meaning of the results; understand the nature of a demand function; identify the strengths and weaknesses of the different methods; understand that demand estimation and forecasting is about minimising risk. Structure 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 Introduction Estimating Demand Using
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value close to 0 implies positive auto-correlation Durbin Watson value close to 4 implies negative auto-correlation 20. Relationship between F and R2 F = (R2/1- R2) x ((n-(k+1))/k) FORECASTING 1. Exponential Smoothing 2. Double Exponential Smoothing 3. Theil’s Coeff U1 is bounded between 0 and 1‚ with values closer to zero indicating greater accuracy. If U2 = 1‚ there is no difference between naïve forecast and the forecasting technique If U2 < 1‚ the
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Forecasting Forecast can help managers by reducing some of the uncertainty‚ thereby enabling them to develop more meaningful plans than they might otherwise. A forecast is a statement about the future. Features common to all forecasts 1. The same underlying causal system that existed in the past will continue to exist in the future. 2. Forecasts are rarely perfect; actual results usually differ from predicted values. 3. Forecasts for groups of items tend to be more accurate than forecasts
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implement the results 11. Define time series. A forecasting technique that uses a series of past data points to make a forecast. 12. What effect does the value of the smoothing constant have on the weight given to the recent values? The smoothing constant is the weighting factor used in an exponential smoothing forecast‚ a number greater than or equal to 0 and less than or equal to 1. It can be changed to give more weight to recent data or more weight to past data.
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1 Chapter Introduction to Operations Management True/False 1. Operations managers are responsible for assessing consumer wants and needs and selling and promoting the organization’s goods or services. Answer: False Page: 4 Difficulty: Easy 2. Often‚ the collective success or failure of companies’ operations functions will impact the ability of a nation to compete with other nations. Answer: True Page: 4 Difficulty: Easy 3. Companies are either producing goods or delivering services. This means that
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