The hypothesis test in R is an assumption made by the researcher to examine the population of the data collected for any experiment and consideration. In the first step, we will introduce the statistical hypothesis in R, and then cover the decision error in R, comprising one and two sample t-tests, and a u-test that entails a simple hypothesis and correlation. We will focus on covariance, comprising directional hypotheses, which is explained in a later section.
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Hands-On Exploratory Data Analysis with R
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Hands-On Exploratory Data Analysis with R
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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)
Preface
Free Chapter
Section 1: Setting Up Data Analysis Environment
Setting Up Our Data Analysis Environment
Importing Diverse Datasets
Examining, Cleaning, and Filtering
Visualizing Data Graphically with ggplot2
Creating Aesthetically Pleasing Reports with knitr and R Markdown
Section 2: Univariate, Time Series, and Multivariate Data
Univariate and Control Datasets
Time Series Datasets
Multivariate Datasets
Section 3: Multifactor, Optimization, and Regression Data Problems
Multi-Factor Datasets
Handling Optimization and Regression Data Problems
Section 4: Conclusions
Next Steps
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