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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Statistics is used in almost every field of research. We will learn about subjects in modern statistics and some applications of statistics. We will also lay out some of the background mathematical concepts required to begin studying statistics.
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 Henry Lewis Rietz - Open Court Pub. Co
The book shifts the emphasis in the study of statistics in the direction of the consideration of the underlying theory involved in certain important methods of statistical analysis, and introduces mathematical statistics to a wider range of readers.
The goal of this handbook is to help scientists and engineers incorporate statistical methods in their work as efficiently as possible. Many parts of the book feature case studies or examples with computations from the free downloadable software.