Book Image

Hands-On Exploratory Data Analysis with R

By : Radhika Datar, Harish Garg
Book Image

Hands-On Exploratory Data Analysis with R

By: Radhika Datar, Harish Garg

Overview of this book

Hands-On Exploratory Data Analysis with R will help you build a strong foundation in data analysis and get well-versed with elementary ways to analyze data. You will learn how to understand your data and summarize its characteristics. You'll also study the structure of your data, and you'll explore graphical and numerical techniques using the R language. This book covers the entire exploratory data analysis (EDA) process—data collection, generating statistics, distribution, and invalidating the hypothesis. As you progress through the book, you will set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using DOE Scatter Plot and SML2010 for multifactor, optimization, and regression data problems. By the end of this book, you will be able to successfully carry out a preliminary investigation on any dataset, uncover hidden insights, and present your results in a business context.
Table of Contents (17 chapters)
Free Chapter
1
Section 1: Setting Up Data Analysis Environment
7
Section 2: Univariate, Time Series, and Multivariate Data
11
Section 3: Multifactor, Optimization, and Regression Data Problems
14
Section 4: Conclusions

Bartlett's test

Bartlett's test is useful when executing a comparison between two or more samples to specify whether they are taken from populations with equal variance. Bartlett's test works successfully for normally distributed data. This test includes a null hypothesis, with a calculation of equal variances, and the alternative hypothesis, where variances are not considered equal. This test is considered useful for checking the assumptions regarding variance analysis.

The user can perform Bartlett's test with the bartlett.test function in R. The normal syntax for this is as follows :

> bartlett.test(values~groups, dataset)  

Here, the parameters refer to the following:

  • values: The name of the variable containing the data value
  • groups: The name of the variable that specifies which sample each value belongs to

If the data is in an unstacked form (with...