Emphasizing concepts rather than recipes, An Introduction to
Statistical Inference and Its Applications with R provides a clear
exposition of the methods of statistical inference for students who
are comfortable with mathematical notation. Numerous examples, case
studies, and exercises are included. R is used to simplify
computation, create figures, and draw pseudorandom samples-not to
perform entire analyses. After discussing the importance of chance
in experimentation, the text develops basic tools of probability.
The plug-in principle then provides a transition from populations
to samples, motivating a variety of summary statistics and
diagnostic techniques. The heart of the text is a careful
exposition of point estimation, hypothesis testing, and confidence
intervals. The author then explains procedures for 1- and 2-sample
location problems, analysis of variance, goodness-of-fit, and
correlation and regression. He concludes by discussing the role of
simulation in modern statistical inference. Focusing on the
assumptions that underlie popular statistical methods, this
textbook explains how and why these methods are used to analyze
experimental data.
General
Imprint: |
Taylor & Francis
|
Country of origin: |
United Kingdom |
Series: |
Chapman & Hall/CRC Texts in Statistical Science |
Release date: |
2023 |
First published: |
2009 |
Authors: |
Michael W. Trosset
|
Dimensions: |
234 x 156mm (L x W) |
Format: |
Paperback
|
Pages: |
496 |
ISBN-13: |
978-1-03-247772-5 |
Categories: |
Books >
Science & Mathematics >
Mathematics >
Probability & statistics
|
LSN: |
1-03-247772-5 |
Barcode: |
9781032477725 |
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