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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by Pete Kaslik
Contents: Statistical Reasoning; Obtaining Useful Evidence; Examining the Evidence Using Graphs and Statistics; Inferential Theory; Testing Hypotheses; Confidence Intervals and Sample Size; Analysis of Bivariate Quantitative Data; Chi Square; etc.
by Walter Antoniotti - 21st Century Learning Products
Walter Antoniotti's book is for people with a limited mathematics background who want to learn the material covered in a traditional college statistics course and for people who want to learn statistics to enhance their career.
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 Borek Puza - ANU Press
A book on statistical methods for analysing a wide variety of data. Topics: bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, finite population inference, biased sampling and nonignorable nonresponse, etc.