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Hands-On Exploratory Data Analysis with R

You're reading from  Hands-On Exploratory Data Analysis with R

Product type Book
Published in May 2019
Publisher Packt
ISBN-13 9781789804379
Pages 266 pages
Edition 1st Edition
Languages
Authors (2):
Radhika Datar Radhika Datar
Profile icon Radhika Datar
Harish Garg Harish Garg
Profile icon Harish Garg
View More author details

Table of Contents (17) Chapters

Preface 1. Section 1: Setting Up Data Analysis Environment
2. Setting Up Our Data Analysis Environment 3. Importing Diverse Datasets 4. Examining, Cleaning, and Filtering 5. Visualizing Data Graphically with ggplot2 6. Creating Aesthetically Pleasing Reports with knitr and R Markdown 7. Section 2: Univariate, Time Series, and Multivariate Data
8. Univariate and Control Datasets 9. Time Series Datasets 10. Multivariate Datasets 11. Section 3: Multifactor, Optimization, and Regression Data Problems
12. Multi-Factor Datasets 13. Handling Optimization and Regression Data Problems 14. Section 4: Conclusions
15. Next Steps 16. Other Books You May Enjoy

Hypothesis test

This section is all about hypothesis testing in R. This testing is merely an assumption made by the researcher about the population of data collected in relation to any experiment. As a first step, we will introduce the statistical hypothesis in R, and later, we will cover the decision error in R with a single t-test and directional hypothesis in R.

t-test in R

This is also referred to as the student's t-test, which is a method for comparing two samples. It can usually be implemented to determine whether the samples are proper or different. This is considered a parametric test, and the data should be distributed normally.

R can handle the various versions of the t-test using the t.test() command.

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