Isye 4803 advanced optimization - Georgia Tech ISyE

Isye 4803 advanced optimization - Georgia Tech ISyE Helping tech-savvy marketers and data analysts solve real-world business problems with Excel Using data-driven business analytics to understand customers and improve results is a great idea in theory, but in today's busy offices, marketers and analysts need simple, low-cost ways to process and make the most of all that data. Practical exercises in each chapter help you apply and reinforce ques as you learn. Introduction to Mathematical Programming Applications and Algorithms by. Wayne L. Winston, Duxbury Press, 2002 advanced chapters or Optimization in.

OR/MA 504_001 Course Information - NCSU Statistics

OR/MA 504_001 Course Information - NCSU Statistics 138 Summary 142 Exercises 142 8 Revenue Management 143 Estimating Demand for the Bates Motel and Segmenting Customers 144 Handling Uncertainty 150 Markdown Pricing 153 Summary 156 Exercises 156 III Forecasting 159 9 Simple Linear Regression and Correlation 161 Simple Linear Regression 161 Using Correlations to Summarize Linear Relationships 170 Summary 174 Exercises 175 10 Using Multiple Regression to Forecast Sales 177 Introducing Multiple Linear Regression 178 Running a Regression with the Data Analysis Add-In 179 Interpreting the Regression Output 182 Using Qualitative Independent Variables in Regression 186 Modeling Interactions and Nonlinearities 192 Testing Validity of Regression Assumptions 195 Multicollinearity 204 Validation of a Regression 207 Summary 209 Exercises 210 11 Forecasting in the Presence of Special Events 213 Building the Basic Model 213 Summary 222 Exercises 222 12 Modeling Trend and Seasonality 225 Using Moving Averages to Smooth Data and Eliminate Seasonality 225 An Additive Model with Trends and Seasonality 228 A Multiplicative Model with Trend and Seasonality 231 Summary 234 Exercises 234 13 Ratio to Moving Average Forecasting Method 235 Using the Ratio to Moving Average Method 235 Applying the Ratio to Moving Average Method to Monty Data 238 Summary 238 Exercises 239 14 Winter’s Method 241 Parameter Definitions for Winter’s Method 241 Initializing Winter’s Method 243 Estimating the Smoothing Constants 244 Forecasting Future Months 246 Mean Absolute Percentage Error (MAPE) 247 Summary 248 Exercises 248 15 Using Neural Networks to Forecast Sales 249 Regression and Neural Nets 249 Using Neural Networks 250 Using Neural Tools to Predict Sales 253 Using Neural Tools to Forecast Airline Miles 258 Summary 259 Exercises 259 IV What do Customers Want? INFORMATION; COURSE TEXT The text for the course is Introduction to Mathematical Programming, 4th ed. 2003, Wayne Winston and Munirpallam Venkataramanan, Brooks Cole - Thomson Learning. Homework 4 pdf Due Mon.

Chapter 8. Network Models to accompany <em>Introduction</em> to

Chapter 8. Network Models to accompany Introduction to He has won more than 45 teaching awards at Indiana University. To accompany. Introduction to Mathematical Programming Operations Research, Volume 1. 4th edition, by Wayne L. Winston and Munirpallam Venkataramanan.

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Firstday fall 2017.wxp He has also written numerous journal articles and a dozen books, and has developed two online courses for Harvard Business School. Introduction to Mathematical Programming. Daniels 214 M, W. Algorithms, 4th. ed. Wayne L. Winston, Munirpallam Venkataramanan, Duxbury. Publishing Co.

MATH 3170 SU 2012 - Department of Mathematics and

MATH 3170 SU 2012 - Department of Mathematics and Introduction xxiii I Using Excel to Summarize Marketing Data 1 1 Slicing and Dicing Marketing Data with Pivot Tables 3 Analyzing Sales at True Colors Hardware 3 Analyzing Sales at La Petit Bakery 14 Analyzing How Demographics Affect Sales 21 Pulling Data from a Pivot Table with the GETPIVOTDATA Function 25 Summary 27 Exercises 27 2 Using Excel Charts to Summarize Marketing Data 29 Combination Charts 29 Using a Pivot Chart to Summarize Market Research Surveys 36 Ensuring Charts Update Automatiy When New Data is Added 39 Making Chart Labels Dynamic 40 Summarizing Monty Sales-Force Rankings 43 Using Check Boxes to Control Data in a Chart 45 Using Sparklines to Summarize Multiple Data Series 48 Using GETPIVOTDATA to Create the End-of-Week Sales Report 52 Summary 55 Exercises 55 3 Using Excel Functions to Summarize Marketing Data 59 Summarizing Data with a Histogram 59 Using Statistical Functions to Summarize Marketing Data 64 Summary 79 Exercises 80 II Pricing 83 4 Estimating Demand Curves and Using Solver to Optimize Price 85 Estimating Linear and Power Demand Curves 85 Using the Excel Solver to Optimize Price 90 Pricing Using Subjectively Estimated Demand Curves 96 Using Solver Table to Price Multiple Products 99 Summary 103 Exercises 104 5 Price Bundling 107 Why Bundle? To view and/or print PDF files you need to download the Acrobat Reader. The main topics include a Linear Programming the theory and applications of linear. Programming an introduction to the concepts of dynamic programming with a discussion of typical problems and their solutions. by Wayne L. Winston.

Chapter 4 The Simplex Algorithm and Goal <em>Programming</em> to

Chapter 4 The Simplex Algorithm and Goal Programming to 261 16 Conjoint Analysis 263 Products, Attributes, and Levels 263 Full Profile Conjoint Analysis 265 Using Evolutionary Solver to Generate Product Profiles 272 Developing a Conjoint Simulator 277 Examining Other Forms of Conjoint Analysis 279 Summary 281 Exercises 281 17 Logistic Regression 285 Why Logistic Regression Is Necessary 286 Logistic Regression Model 289 Maximum Likelihood Estimate of Logistic Regression Model 290 Using Stat Tools to Estimate and Test Logistic Regression Hypotheses 293 Performing a Logistic Regression with Count Data 298 Summary 300 Exercises 300 18 Discrete Choice Analysis 303 Random Utility Theory 303 Discrete Choice Analysis of Chocolate Preferences 305 Incorporating Price and Brand Equity into Discrete Choice Analysis 309 Dynamic Discrete Choice 315 Independence of Irrelevant Alternatives (IIA) Assumption 316 Discrete Choice and Price Elasticity 317 Summary 318 Exercises 319 V Customer Value 325 19 Calculating Lifetime Customer Value 327 Basic Customer Value Template 328 Measuring Sensitivity Analysis with Two-way Tables 330 An Explicit Formula for the Multiplier r 331 Varying Margins 331 DIRECTV, Customer Value, and Friday Nht Lhts (FNL)333 Estimating the Chance a Customer Is Still Active 334 Going Beyond the Basic Customer Lifetime Value Model 335 Summary 336 Exercises 336 20 Using Customer Value to Value a Business 339 A Primer on Valuation 339 Using Customer Value to Value a Business 340 Measuring Sensitivity Analysis with a One-way Table 343 Using Customer Value to Estimate a Firm’s Market Value 344 Summary 344 Exercises 345 21 Customer Value, Monte Carlo Simulation, and Marketing Decision Making 347 A Markov Chain Model of Customer Value 347 Using Monte Carlo Simulation to Predict Success of a Marketing Initiative 353 Summary 359 Exercises 360 22 Allocating Marketing Resources between Customer Acquisition and Retention 347 Modeling the Relationship between Spending and Customer Acquisition and Retention 365 Basic Model for Optimizing Retention and Acquisition Spending 368 An Improvement in the Basic Model 371 Summary 373 Exercises 374 VI Market Segmentation 375 23 Cluster Analysis 377 Clustering U. Cities 378 Using Conjoint Analysis to Segment a Market 386 Summary 391 Exercises 391 24 Collaborative Filtering 393 User-Based Collaborative Filtering 393 Item-Based Filtering 398 Comparing Item- and User-Based Collaborative Filtering 400 The Netflix Competition 401 Summary 401 Exercises 402 25 Using Classification Trees for Segmentation 403 Introducing Decision Trees 403 Constructing a Decision Tree 404 Pruning Trees and CART 409 Summary 410 Exercises 410 VII Forecasting New Product Sales 413 26 Using S Curves to Forecast Sales of a New Product 415 Examining S Curves 415 Fitting the Pearl or Logistic Curve418 Fitting an S Curve with Seasonality 420 Fitting the Gompertz Curve 422 Pearl Curve versus Gompertz Curve 425 Summary 425 Exercises 425 27 The Bass Diffusion Model 427 Introducing the Bass Model 427 Estimating the Bass Model 428 Using the Bass Model to Forecast New Product Sales 431 Deflating Intentions Data 434 Using the Bass Model to Simulate Sales of a New Product 435 Modifications of the Bass Model 437 Summary 438 Exercises 438 28 Using the Copernican Principle to Predict Duration of Future Sales 439 Using the Copernican Principle 439 Simulating Remaining Life of Product 440 Summary 441 Exercises 441 VIII Retailing 443 29 Market Basket Analysis and Lift 445 Computing Lift for Two Products 445 Computing Three-Way Lifts 449 A Data Mining Legend Debunked! Introduction to Mathematical Programming Operations Research, Volume 1. 4th edition, by Wayne L. Winston and Munirpallam Venkataramanan. Lewis Ntaimo.

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