| |
This book provides in introduction into data mining methods and models, including association rules, clustering, K-nearest neighbor, statistical inference, neural networks, linear and logistic regression, and multivariate analysis. It presents a unified approach based on CRISP methodology (involves Strategic Risk Assessment based on Organizational Modelling). Table of... Morecontents : fluential Observations · The Regression Model · Inference in Regression · Verifying the Regression Assumptions · An Example: The Baseball Data Set · An Example: The California Data Set · Transformations to Achieve Linearity Multiple Regression and Model Building · An Example of Multiple Regression · The Multiple Regression Model · Inference in Multiple Regression · Regression with Categorical Predictors · Multicollinearity · Variable Selection Methods · An Application of Variable Selection Methods · Mallows’ C p Statistic · Variable Selection Criteria · Using the Principal Components as Predictors in Multiple Regression Logistic Regression · A Simple Example of Logistic Regression · Maximum Likelihood Estimation · Interpreting Logistic Regression Output · Inference: Are the Predictors Significant? · Interpreting the Logistic Regression Model · Interpreting a Logistic Regression Model for a Dichotomous Predictor · Interpreting a Logistic Regression Model for a Polychotomous Predictor · Interpreting a Logistic Regression Model for a Continuous Predictor · The Assumption of Linearity · The Zero-Cell Problem · Multiple Logistic Regression · Introducing Higher Order terms to Handle Non-Linearity · Validating the Logistic Regression Model · WEKA: Hands-On Analysis Using Logistic Regression Na Market Description : · Graduate and advanced undergraduate students of computer science and statistics · MBA Students · Managers · CEOs · CFOs About Author : Daniel T. Larose received his PhD in statistics from the University of Connecticut. Currently he is an associate professor of statistics in the Department of Mathematical Sciences, and Director of Data Mining@CCSU, at Central Connecticut State University. He has also worked in the areas of biostatistics, statistics, and data management at Bristol-Myers Squibb Pharmaceutical Research Center, Wesleyan University, and United Technologies Corporation. He was written and contributed work to two books, and published 9 articles, on the topic of data mining and statistics.
ISBN : 9788126507764
|
| |
|