<p></p><p></p><p> <br/></p><p> </p><p></p><p></p><p>1. Dimension Reduction Methods. <br/><br/>Need for Dimension Reduction in Data Mining. <br/><br/>Principal Components Analysis. <br/><br/>Factor Analysis. <br/><br/>User-Defined Composites. <br/><br/>2. Regression Modeling. <br/><br/>Example of Simple Linear Regression. <br/><br/>Least-Squares Estimates. <br/><br/>Coefficient or Determination. <br/><br/>Correlation Coefficient. <br/><br/>The ANOVA Table. <br/><br/>Outliers, High Leverage Points, and Influential Observations. <br/><br/>The Regression Model. <br/><br/>Inference in Regression. <br/><br/>Verifying the Regression Assumptions. <br/><br/>An Example: The Baseball Data Set. <br/><br/>An Example: The California Data Set. <br/><br/>Transformations to Achieve Linearity. <br/><br/>3. Multiple Regression and Model Building. <br/><br/>An Example of Multiple Regression. <br/><br/>The Multiple Regression Model. <br/><br/>Inference in Multiple Regression. <br/><br/>Regression with Categorical Predictors. <br/><br/>Multicollinearity. <br/><br/>Variable Selection Methods. <br/><br/>An Application of Variable Selection Methods. <br/><br/>Mallows’ C p Statistic. <br/><br/>Variable Selection Criteria. <br/><br/>Using the Principal Components as Predictors in Multiple Regression. <br/><br/>4. Logistic Regression. <br/><br/>A Simple Example of Logistic Regression. <br/><br/>Maximum Likelihood Estimation. <br/><br/>Interpreting Logistic Regression Output. <br/><br/>Inference: Are the Predictors Significant? <br/><br/>Interpreting the Logistic Regression Model. <br/><br/>Interpreting a Logistic Regression Model for a Dichotomous Predictor. <br/><br/>Interpreting a Logistic Regression Model for a Polychotomous Predictor. <br/><br/>Interpreting a Logistic Regression Model for a Continuous Predictor. <br/><br/>The Assumption of Linearity. <br/><br/>The Zero-Cell Problem. <br/><br/>Multiple Logistic Regression. <br/><br/>Introducing Higher Order terms to Handle Non-Linearity. <br/><br/>Validating the Logistic Regression Model. <br/><br/>WEKA: Hands-On Analysis Using Logistic Regression. <br/><br/>5. Naïve Bayes and Bayesian Networks. <br/><br/>The Bayesian Approach. <br/><br/>The Maximum a Posteriori (MAP) Classification. <br/><br/>The Posterior Odds Ratio. <br/><br/>Balancing the Data. <br/><br/>Naïve Bayes Classification. <br/><br/>Numeric Predictors for Naïve Bayes Classification. <br/><br/>WEKA: Hands-On Analysis Using Naïve Bayes. <br/><br/>Bayesian Belief Networks. <br/><br/>Using the Bayesian Network to Find Probabilities. <br/><br/>WEKA: Hands-On Analysis Using Bayes Net. <br/><br/>6. Genetic Algorithms. <br/><br/>Introduction to Genetic Algorithms. <br/><br/>The Basic Framework of a Genetic Algorithm. <br/><br/>A Simple Example of Genetic Algorithms at Work. <br/><br/>Modifications and Enhancements: Selection. <br/><br/>Modifications and enhancements: Crossover. <br/><br/>Genetic Algorithms for Real-Valued Variables. <br/><br/>Using Genetic Algorithms to Train a Neural Network. <br/><br/>WEKA: Hands-On Analysis Using Genetic Algorithms. <br/><br/>7. Case Study: Modeling Response to Direct-Mail Marketing. <br/><br/>The Cross-Industry Standard Process for Data Mining: CRISP-DM. <br/><br/>Business Understanding Phase. <br/><br/>Data Understanding and Data Preparation Phases. <br/><br/>The Modeling Phase and the Evaluation Phase. </p>