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 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 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 Peter Young - arXiv
These notes discuss, in a style intended for physicists, how to average data and fit it to some functional form. I try to make clear what is being calculated, what assumptions are being made, and to give a derivation of results.
by R. Dennis Cook, Sanford Weisberg - Chapman & Hall
In this monograph, we present a detailed account of the residual based methods that we have found to be most useful, and brief summaries of other selected methods. Our emphasis is on graphical methods rather than on formal testing.