Regression Analysis: Concepts and Applications focuses on thinking clearly about and solving practical statistical problems. The approach leads from the theoretical (meaning TTconceptualUU not TTmathematicalTT) to the applied, the idea being that samples (using theory) tell the investigator what needs to be known about populations (for application).
Chapter exercises provide practice with and enhance understanding of the analysis of each model. The book concludes with a review of SEM guidelines for reporting research.
Where Angels Fear to Tread: Interaction Effects in Multiple Regression : a Research Note
Introduction to the logistic regression model -- Multiple logistic regression -- Interpretation of the fitted logistic regression model -- Model-building strategies and methods for logistic regression -- Assessing the fit of the model -- ...
Introduction to Linear Regression Analysis
Modern topics added include classification and regression analysis (CART), neural networks, and the bootstrap, among others.· Expanded topics include robust regression, nonlinear regression, GLMs, and others· Problems and data sets have ...
Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression.
The book begins with an excellent introduction to Stata and then provides a general treatment of estimation, testing, fit, and interpretation in this class of models.
This book is designed to provide a conceptually-oriented introduction to multiple regression.
The first book to provide a unified framework for both single-level and multilevel modeling of ordinal categorical data, Applied Ordinal Logistic Regression Using Stata helps readers learn how to conduct analyses, interpret the results from ...
Multiple Regression and Causal Analysis