Auto Parts Sales Forecast Course: QNT 5040 LEARNING OUTCOME/S: (see syllabus) Date: June 6‚ 2014 PURPOSE: To facilitate effective decision making under uncertain conditions by quantifying risk. Name of Student: VALIDITY: Best practices in Forecasting Name of Faculty: Dr. Yurova COMPANION DOCUMENTS: Email sent separately Earning maximum points in each box in ‘PROFICIENT’ column and / or points in columns to the right of ‘PROFICIENT’ meets standard. > Performance Criteria
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make it a linear programming model. 4. Learn how to solve two variable linear programming models by the graphical solution procedure. 5. Understand the importance of extreme points in obtaining the optimal solution. 6. Know the use and interpretation of slack and surplus variables. 7. Be able to interpret the computer solution of a linear programming problem. 8. Understand how alternative optimal solutions‚ infeasibility and unboundedness can occur in linear programming
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Mathematical modeling is commonly used to predict the behavior of phenomena in the environment. Basically‚ it involves analyzing a set of points from given data by plotting them‚ finding a line of "best fit" through these points‚ and then using the resulting graph to evaluate any given point. Models are useful in hypothesizing the future behavior of populations‚ investments‚ businesses‚ and many other things that are characterized by fluctuations. A mathematical model usually describes a
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document all content not your own‚ whether the attributions are for direct quotes‚ a chart‚ a picture or paraphrased content. Epagogix: Predictive Analytics for the Movies Prepared for MSIT 3820 / MSPC 3920 Business Intelligence Pat Gillogly‚ Instructor Mary A. Student May 8‚ 2015 Introduction: The purpose of this paper is to explore the use of predictive analytics‚ and specifically artificial neural networks (ANN)‚ by UK-based industry analyst Epagogix‚ to determine the economic viability of
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Data Mining 95-791 Spring 2013 Lecture #8 Predictive analytics: Regression Artur Dubrawski awd@cs.cmu.edu This unit • Good-old correlation scores revisited • Locally weighted regression – As an approximator of non-linear functions – As a framework for active/purposive acquisition of data 95-791 Data Mining Lecture #8 Slide 2 Copyright © 2000-2013 Artur Dubrawski Correlational scores of association between attributes of data • • • • Linear Rank Quadratic …. Would not it be
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Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar‚ but more general term. Both might refer to formal statistical methods employing time series‚ cross-sectional or longitudinal data‚ or alternatively to less formal judgemental methods. Usage can differ between areas of application: for example‚ in hydrology
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Portfolio Optimization Questions Risk Management Dr. Castro Fall 2002 Assume you are the manager of a risky portfolio with an expected rate of return of 18 % and a standard deviation of 28%. The T-bill rate is 8%. 1. Your client chooses to invest 70% of a portfolio in your fund and 30% in a T-bill money market fund. What is the expected value and standard deviation of the rate of return on his portfolio? 2. Suppose that your risky portfolio includes the following investments
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Optimization methods in portfolio management and option hedging ∗ Huyˆn PHAM e Laboratoire de Probabilit´s et e Mod`les Al´atoires e e CNRS‚ UMR 7599 Universit´ Paris 7 e e-mail: pham@math.jussieu.fr and Institut Universitaire de France April 24‚ 2007 Abstract These lecture notes give an introduction to modern‚ continuous-time portfolio management and option hedging. We present the stochastic control method to portfolio optimization‚ which covers Merton’s pioneering work. The
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Forecasting forms an integral process within an organisation where data from several sources are processed and integrated to manipulate projections for different departments. Finance develops a long term forecast to evaluate the investment needs and capital; Marketing develops a mid-term forecast projecting sales; Operations produces a forecast to make decisions on short term scheduling‚ inventory management and long term capacity planning and Human Resources uses the forecast to evaluate personnel
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Business Forecasting Coursework Introduction The data of this coursework are business investment in the quarterly series in the manufacturing sector from 1994 to the second quarter of 2008 in UK. In the coursework‚ firstly analyze the former 50 data to forecast the latter 8 ones and then compare with the real data to see if the forecasting model is a good fit or not. As adopting two different approaches to make the forecasting work‚ including regression with Dummy Variables method and Box-Jenkins
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