by Miguel A. Hernan, James M. Robins
Publisher: Chapman & Hall/CRC 2015
Number of pages: 352
The book provides a cohesive presentation of concepts of, and methods for, causal inference. We expect that the book will be of interest to anyone interested in causal inference, e.g., epidemiologists, statisticians, psychologists, economists, sociologists, other social scientists... The book is geared towards graduate students and practitioners.
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(multiple PDF files)
The goal of this handbook is to help scientists and engineers incorporate statistical methods in their work as efficiently as possible. Many parts of the book feature case studies or examples with computations from the free downloadable software.
by Brian S Blais - Save The Broccoli Publishing
This is a new approach to an introductory statistical inference textbook, motivated by probability theory as logic. It is targeted to the typical Statistics 101 college student, and covers the topics typically covered in the first semester.
by Robert B. Ash - University of Illinois
These notes are based on a course that the author gave at UIUC. No prior knowledge of statistics is assumed. A standard first course in probability is a prerequisite, but the first 8 lectures review results that are important in statistics.
by Benjamin Yakir - The Hebrew University of Jerusalem
This is an introduction to statistics, with R, without calculus. The target audience for this book is college students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more.