Jmp Statistical Software Student Edition' title='Jmp Statistical Software Student Edition' />Wiley Data Mining for Business Analytics Concepts, Techniques, and Applications with XLMiner, 3rd Edition. Foreword xvii. Preface to the Third Edition xix. Preface to the First Edition xxii. Acknowledgments xxiv. Jmp Statistical Software Student Edition' title='Jmp Statistical Software Student Edition' />PART I PRELIMINARIESCHAPTER 1 Introduction 3. What is Business Analytics What is Data Mining Data Mining and Related Terms 5. Big Data 6. 1. 5 Data Science 7. Why Are There So Many Different MethodsExample code and data from SAS Press books and SAS documentation. In addition to the five listed in this title, there are quite a few other options, so how do you choose which statistical software to use The default is to use. Jmp Statistical Software Student Edition' title='Jmp Statistical Software Student Edition' />Whats the Best RSquared for Logistic Regression February 13, 2013 By Paul Allison. One of the most frequent questions I get about logistic regression is How. Terminology and Notation 9. Road Maps to This Book 1. Order of Topics 1. CamtasiaCamtasia Studio 3. Mac, 9. 01 Win Record onscreen activity, customize and edit content, add interactive elements or import media, and share videos. This page provides an annotated, topicbased collection of available resources for statistics, statistical graphics, and computation related to research, data. Coordinates. SAS Institute or SAS, pronounced sass is an American multinational developer of analytics software based in Cary, North Carolina. SAS develops and. The leading humanitarian information source on global crises and disasters. Reliable and timely information from trusted sources. CHAPTER 2 Overview of the Data Mining Process 1. Introduction 1. 42. Core Ideas in Data Mining 1. The Steps in Data Mining 1. Preliminary Steps 2. Predictive Power and Overfitting 2. Building a Predictive Model with XLMiner 3. Using Excel for Data Mining 4. Automating Data Mining Solutions 4. Data Mining Software Tools by Herb Edelstein 4. Problems 4. 5PART II DATA EXPLORATION AND DIMENSION REDUCTIONCHAPTER 3 Data Visualization 5. Uses of Data Visualization 5. Data Examples 5. 2Example 1 Boston Housing Data 5. Example 2 Ridership on Amtrak Trains 5. Basic Charts Bar Charts, Line Graphs, and Scatter Plots 5. Distribution Plots 5. Heatmaps Visualizing Correlations and Missing Values 5. Multi Dimensional Visualization 5. Adding Variables 5. Manipulations 6. 1Reference trend line and labels 6. Scaling up to Large Datasets 6. Multivariate Plot 6. Interactive Visualization 6. Specialized Visualizations 7. Visualizing Networked Data 7. Visualizing Hierarchical Data Treemaps 7. Visualizing Geographical Data Map Charts 7. Summary Major Visualizations and Operations, by Data Mining Goal 7. Prediction 7. 5Classification 7. Time Series Forecasting 7. Unsupervised Learning 7. Problems 7. 7CHAPTER 4 Dimension Reduction 7. Introduction 7. 94. Curse of Dimensionality 8. Practical Considerations 8. Example 1 House Prices in Boston 8. Data Summaries 8. Correlation Analysis 8. Reducing the Number of Categories in Categorical Variables 8. Converting A Categorical Variable to A Numerical Variable 8. Principal Components Analysis 8. Example 2 Breakfast Cereals 8. Principal Components 9. Normalizing the Data 9. Using Principal Components for Classification and Prediction 9. Dimension Reduction Using Regression Models 9. Dimension Reduction Using Classification and Regression Trees 9. Problems 9. 7PART III PERFORMANCE EVALUATIONCHAPTER 5 Evaluating Predictive Performance 1. Introduction 1. 01. Evaluating Predictive Performance 1. Benchmark The Average 1. Prediction Accuracy Measures 1. Judging Classifier Performance 1. Benchmark The Naive Rule 1. Class Separation 1. The Classification Matrix 1. Using the Validation Data 1. Accuracy Measures 1. Cutoff for Classification 1. Performance in Unequal Importance of Classes 1. Asymmetric Misclassification Costs 1. Judging Ranking Performance 1. Oversampling 1. 23. Problems 1. 29. PART IV PREDICTION AND CLASSIFICATION METHODSCHAPTER 6 Multiple Linear Regression 1. Introduction 1. 34. Explanatory vs. Predictive Modeling 1. Estimating the Regression Equation and Prediction 1. Example Predicting the Price of Used Toyota Corolla Cars 1. Variable Selection in Linear Regression 1. Reducing the Number of Predictors 1. How to Reduce the Number of Predictors 1. Problems 1. 47. CHAPTER 7 k Nearest Neighbors k. NN 1. 51. 7. 1 The k NN Classifier categorical outcome 1. Determining Neighbors 1. Classification Rule 1. Example Riding Mowers 1. Choosing k 1. 54. Setting the Cutoff Value 1. NN for a Numerical Response 1. Advantages and Shortcomings of k NN Algorithms 1. Problems 1. 60. CHAPTER 8 The Naive Bayes Classifier 1. Introduction 1. 62. Example 1 Predicting Fraudulent Financial Reporting 1. Applying the Full Exact Bayesian Classifier 1. Advantages and Shortcomings of the Naive Bayes Classifier 1. Advantages and Shortcomings of the naive Bayes Classifier 1. Problems 1. 76. CHAPTER 9 Classification and Regression Trees 1. Introduction 1. 78. Classification Trees 1. Example 1 Riding Mowers 1. Measures of Impurity 1. Evaluating the Performance of a Classification Tree 1. Example 2 Acceptance of Personal Loan 1. Avoiding Overfitting 1. Stopping Tree Growth CHAID 1. Pruning the Tree 1. Classification Rules from Trees 1. Classification Trees for More Than two Classes 1. Regression Trees 1. Prediction 1. 99. Measuring Impurity 2. Evaluating Performance 2. Advantages and Weaknesses of a Tree 2. Improving Prediction Multiple Trees 2. Problems 2. 05. CHAPTER 1. Logistic Regression 2. Introduction 2. 09. The Logistic Regression Model 2. Example Acceptance of Personal Loan 2. Model with a Single Predictor 2. Estimating the Logistic Model from Data 2. Interpreting Results in Terms of Odds 2. Evaluating Classification Performance 2. Variable Selection 2. Example of Complete Analysis Predicting Delayed Flights 2. Data Preprocessing 2. Model Fitting and Estimation 2. Model Interpretation 2. Model Performance 2. Variable Selection 2. Appendix Logistic Regression for Profiling 2. Appendix A Why Linear Regression Is Problematic for a Categorical Response 2. Appendix B Evaluating Explanatory Power 2. Appendix C Logistic Regression for More Than Two Classes 2. Problems 2. 39. CHAPTER 1. Neural Nets 2. 42. Introduction 2. 42. Concept and Structure of a Neural Network 2. Fitting a Network to Data 2. Example 1 Tiny Dataset 2. Computing Output of Nodes 2. Preprocessing the Data 2. Training the Model 2. Example 2 Classifying Accident Severity 2. Avoiding overfitting 2. Using the Output for Prediction and Classification 2. Required User Input 2. Exploring the Relationship Between Predictors and Response 2. Advantages and Weaknesses of Neural Networks 2. Problems 2. 62. CHAPTER 1. Discriminant Analysis 2. Introduction 2. 64. Example 1 Riding Mowers 2. Example 2 Personal Loan Acceptance 2. Distance of an Observation from a Class 2. Fishers Linear Classification Functions 2. Classification Performance of Discriminant Analysis 2. Prior Probabilities 2. Unequal Misclassification Costs 2. Classifying More Than Two Classes 2. Example 3 Medical Dispatch to Accident Scenes 2. Advantages and Weaknesses 2. Problems 2. 79. CHAPTER 1. Combining Methods Ensembles and Uplift Modeling 2. Ensembles 2. 82. Why Ensembles Can Improve Predictive Power 2. Simple Averaging 2. Bagging 2. 86. Boosting 2. Advantages and Weaknesses of Ensembles 2. Uplift Persuasion Modeling 2. A B Testing 2. 87. Uplift 2. 88. Gathering the Data 2. A Simple Model 2. Modeling Individual Uplift 2. Using the Results of an Uplift Model 2. Summary 2. 92. Problems 2. PART V MINING RELATIONSHIPS AMONG RECORDSCHAPTER 1. Manajemen Pemasaran Edisi Milenium 2 Referensi Philip Kotler. Association Rules and Collaborative Filtering 2. Association Rules 2. Discovering Association Rules in Transaction Databases 2. Example 1 Purchases of Phone Faceplates 2. Generating Candidate Rules 2. The Apriori Algorithm 3. Selecting Strong Rules 3. Data Format 3. 03. The Process of Rule Selection 3. Interpreting the Results 3. Rules and Chance 3. Example 2 Rules for Similar Book Purchases 3. Collaborative Filtering. Data Type and Format 3. Example 3 Netflix Prize Contest 3. User Based Collaborative Filtering People Like You 3. Item Based Collaborative Filtering 3. Advantages and Weaknesses of Collaborative Filtering 3. Collaborative Filtering vs. Association Rules 3. Summary 3. 18. Problems 3. CHAPTER 1. 5 Cluster Analysis 3. Introduction 3. 24. Example Public Utilities 3. Measuring Distance Between Two Observations 3. Euclidean Distance 3. Normalizing Numerical Measurements 3. Other Distance Measures for Numerical Data 3. Distance Measures for Categorical Data 3. Distance Measures for Mixed Data 3. Measuring Distance Between Two Clusters 3.