Statistics, Mathematical Statistics

The emphasis of this book is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied.

The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming. The student should have a basic understanding of statistical inference such as contained in https://leanpub.com/LittleInferenceBook/.

The book is intended as an upper level undergraduate or introductory graduate textbook in statistical thinking with a likelihood emphasis for students with a good knowledge of calculus and the ability to think abstractly. By "statistical thinking" is meant a focus on ideas that statisticians care about as opposed to technical details of how to put those ideas into practice. The book does contain technical details, but they are not the focus. By "likelihood emphasis" is meant that the likelihood function and likelihood principle are unifying ideas throughout the text.

presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics.

This book is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure.

Networks have found a prominent place in our everyday life. In science, networks have been used to analyze interpersonal social relationships, communication, academic paper co-authorships and citations, protein interaction patterns, and much more.