Use R!Series



1 Introduction .................................................................. 11.1 Analytics and Business ................................................. 11.2 Goal of This Book ...................................................... 31.3 Who Should Read This Book? ......................................... 41.4 What This Book Is Not ................................................. 51.4.1 This Is Not a Statistics Book ................................... 51.4.2 This Is Not a Data Mining Book ............................... 51.5 What This Book Is ...................................................... 51.6 Structure of This Book.................................................. 61.7 Using This Book in a Course ........................................... 72 Exploring and Discovering Data ........................................... 92.1 Basic Data Summaries and Visualizations: House Price Data ........ 102.2 Data Transformations and Trellis Graphs: Direct Marketing Data ... 202.3 Time Series Graphs: Soft Drink Sales Data............................ 252.4 Spatial Graphs: Online Purchase Preferences Data.................... 282.5 Graphs for Categorical Responses: Consumer-to-Consumer Loan Data ................................................... 302.6 Graphs for Panel Data: Customer Loyalty Data ....................... 343 Data Modeling I – Basics .................................................... 413.1 Introduction:Why Do We Need Models? ............................. 423.2 Fitting and Interpreting a Regression Model:Least Squares Regression ............................................... 473.2.1 The Idea of Least Squares Regression ......................... 483.2.2 Interpreting a First Simple Regression Model ................. 503.2.3 Evaluating a Regression Model ................................ 523.2.4 Comparing Regression Models ................................ 553.3 Identifying and Selecting Important Predictors: Statistical Inference 573.3.1 The Signal-to-Noise Ratio...................................... 573.3.2 Testing for Statistical Signi?cance ............................. 593.3.3 Gauging Practical Importance.................................. 593.4 Data Case: Understanding Customers’ Spending PatternsUsing Basic Regression ................................................. 614 Data Modeling II – Making Models More Flexible ...................... 674.1 More Flexible Models (1): Dummy Variablesand Interaction Terms ................................................... 684.1.1 Dummy Variables ............................................... 714.1.2 Interaction Terms ............................................... 774.2 More Flexible Models (2): Nonlinear Relationshipsand Data Transformations .............................................. 814.2.1 Data Transformations ........................................... 854.2.2 Interpreting Nonlinear Regression Models .................... 884.3 Data Case: Using Interaction Terms and DataTransformations to Better Understand Customers’Spending Patterns ....................................................... 915 Data Modeling III – Making Models More Selective .................... 1015.1 Models with Too Many Predictors: Multicollinearityand Variable Selection .................................................. 1015.1.1 Multicollinearity ................................................ 1055.1.2 Curing Multicollinearity and Variable Selection .............. 1075.2 Data Case: Using Variable Selection of FinancialIndicators to Predict a Company’s Stock Price ........................ 1116 Data Modeling IV-Fine-Tuning Your Model .............................. 1256.1 Assessing the Quality of a Model: Predictive Power6.1.2 Measuring the Predictive Power of a Model ................... 1326.2 Exploring and Modeling Complex Relationships:Nonparametric Regression and Regression Trees ..................... 1426.2.1 Uncovering Interactions with Regression Trees ............... 1436.2.2 Modeling Nonlinear Relationships UsingNonparametric Regression ..................................... 1526.3 Data Case: Fine-Tuning Stock Price Prediction Models .............. 1617 Appendix: Introduction to the Statistical Software R.................... 1677.1 How and Where Do I Get R? ........................................... 1687.2 How Do I Get Started with R? ......................................... 1687.3 Basic R Manipulations .................................................. 1707.4 Loading Data, Executing Functions, and Exporting Results .......... 1717.5 R Libraries............................................................... 176Contents xi7.6 Graphical User Interfaces ............................................... 1797.6.1 Rcmdr ........................................................... 1797.6.2 Rattle ............................................................ 181Index ............................................................................... 185