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Mastering Scientific Computing with R

You're reading from   Mastering Scientific Computing with R Employ professional quantitative methods to answer scientific questions with a powerful open source data analysis environment

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Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781783555253
Length 432 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (12) Chapters Close

Preface 1. Programming with R FREE CHAPTER 2. Statistical Methods with R 3. Linear Models 4. Nonlinear Methods 5. Linear Algebra 6. Principal Component Analysis and the Common Factor Model 7. Structural Equation Modeling and Confirmatory Factor Analysis 8. Simulations 9. Optimization 10. Advanced Data Management Index

Chapter 2. Statistical Methods with R

This chapter will present an overview of how to summarize your data and get useful statistical information for downstream analysis. We will also show you how to plot and get statistical information from probability distributions and how to test the fit of your sample distribution to well-defined probability distributions. We will also go over some of the functions used to perform hypothesis testing including the Student's t-test, Wilcoxon rank-sum test, z-test, chi-squared test, Fisher's exact test, and F-test.

Before we begin, we will load the gene expression profiling data from the E-GEOD-19577 study entitled "MLL partner genes confer distinct biological and clinical signatures of pediatric AML, an AIEOP study" from the ArrayExpress website to use as a sample dataset for some of our examples. For simplicity, we will not go into the details of how the data was generated, except to mention that the study evaluates the expression...

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