Linear Regression Using R: An Introduction to Data Modeling
by David R. Lilja
Publisher: University of Minnesota 2016
Number of pages: 91
The book presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models. Key modeling and programming concepts are intuitively described using the R programming language.
Home page url
Download or read it online for free here:
by Ryan Martin - University of Illinois at Chicago
Table of contents: Statistics and Sampling Distributions; Point Estimation Basics; Likelihood and Maximum Likelihood Estimation; Sufficiency and Minimum Variance Estimation; Hypothesis Testing; Bayesian Statistic; What Else is There to Learn?
by Miguel A. Hernan, James M. Robins - Chapman & Hall/CRC
The book provides a cohesive presentation of concepts of, and methods for, causal inference. It will be of interest to anyone interested in causal inference, e.g., epidemiologists, statisticians, psychologists, economists, sociologists, and others.
by Howard J. Seltman - Carnegie Mellon University
This book is intended as required reading material for the course Experimental Design for the Behavioral and Social Sciences, a second level statistics course for undergraduate students in the College of Humanities and Social Sciences...
by D.M. Diez, C.D. Barr, M. Cetinkaya-Rundel - OpenIntro
OpenIntro Statistics is intended for introductory statistics courses at the high school through university levels. There are a large selection of exercises at the end of each chapter useful for practice or homework assignments.