Using R for Introductory Statistics
by John Verzani
Publisher: Chapman & Hall/CRC 2004
Number of pages: 114
The author presents a self-contained treatment of statistical topics and the intricacies of the R software. The book treats exploratory data analysis with more attention than is typical, includes a chapter on simulation, and provides a unified approach to linear models. This text lays the foundation for further study and development in statistics using R.
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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 D Caradog Jones - G Bell
First part of the book is within the understanding of the ordinary person. Part 2 is more mathematical, but the results are explained in such a way that the reader shall gain a general idea of the theory and applications without mastering the proofs.
by David R. Lilja - University of Minnesota
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.
by Ivan Lowe - scientificlanguage.com
Here I present statistics for the ordinary person. Examples are taken from ordinary life. The book begins with basic concepts behind the statistics and never gets harder than simple arithmetic. The course is presented as a series of key ideas.