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.
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by Allen B. Downey - Green Tea Press
Think Bayes is an introduction to Bayesian statistics using computational methods. Contents: Bayes's Theorem; Computational statistics; Tanks and Trains; Urns and Coins; Odds and addends; Hockey; The variability hypothesis; Hypothesis testing.
by Hugh D. Young - McGraw Hill
A concise, highly readable introduction to statistical methods. Even with a limited mathematics background, readers can understand what statistical methods are and how they may be used to obtain the best possible results from experimental data.
by Irving W. Burr - McGraw-Hill
The present book is the outgrowth of a course in statistics for engineers which has been given at Purdue University. The book is written primarily as a text book for junior, senior, and graduate students of engineering and physical science.
by Stan Brown - BrownMath.com
This book is an alternative to the usual textbooks for a one-semester course in statistics. The author tried to make statistics approachable to anyone with high-school math, but it's still a technical subject. There is very little use of formulas.