7 Ergebnisse.

Applied Bayesian Statistics provides a broad, but in-depth introduction to Bayesian statistics, both in terms of its basic theoretical underpinnings and its contemporary methods of application. The book is highly applied-more of a "how to" guide-with statistical theory limited to what is needed to understand the basic ideas. The focus is on common models used by social scientists, and extensions to them that the Bayesian approach facilitates. ...

Welcome to the second book in my War Within series: Anger, this is the follow on from book one Hurt, because I have come to understand that anger is the inexorable result of too much hurt.

Introduction to Applied Bayesian Statistics and Estimation for Social Scientists covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research, including the linear regression model, generalized linear models, hierarchical models, and mul...

This book covers applied statistics for the social sciences with upper-level undergraduate students in mind. The chapters are based on lecture notes from an introductory statistics course the author has taught for a number of years. The book integrates statistics into the research process, with early chapters covering basic philosophical issues underpinning the process of scientific research. These include the concepts of deductive reasoning a...

For social scientists, it is often confusing how to determine when missing data is a problem in analyses and how to handle it. This book presents a comprehensive overview of the available methods, focusing on which method should be used for specific problems. It features numerous real and simulated data examples to illustrate how the methods can be applied once the appropriate technique has been determined. It also includes exercises, computer...

This book covers applied statistics for the social sciences with upper-level undergraduate students in mind. The chapters are based on lecture notes from an introductory statistics course the author has taught for a number of years. The book integrates statistics into the research process, with early chapters covering basic philosophical issues underpinning the process of scientific research. These include the concepts of deductive reasoning a...

Introduction to Applied Bayesian Statistics and Estimation for Social Scientists covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research, including the linear regression model, generalized linear models, hierarchical models, and mul...