Book description
Solve realworld statistical problems using the most popular R packages and techniques
Key Features
 Learn how to apply statistical methods to your everyday research with handy recipes
 Foster your analytical skills and interpret research across industries and business verticals
 Perform ttests, chisquared tests, and regression analysis using modern statistical techniques
Book Description
R is a popular programming language for developing statistical software. This book will be a useful guide to solving common and notsocommon challenges in statistics. With this book, you'll be equipped to confidently perform essential statistical procedures across your organization with the help of cuttingedge statistical tools.
You'll start by implementing data modeling, data analysis, and machine learning to solve realworld problems. You'll then understand how to work with nonparametric methods, mixed effects models, and hidden Markov models. This book contains recipes that will guide you in performing univariate and multivariate hypothesis tests, several regression techniques, and using robust techniques to minimize the impact of outliers in data.You'll also learn how to use the caret package for performing machine learning in R. Furthermore, this book will help you understand how to interpret charts and plots to get insights for better decision making.
By the end of this book, you will be able to apply your skills to statistical computations using R 3.5. You will also become wellversed with a wide array of statistical techniques in R that are extensively used in the data science industry.
What you will learn
 Become well versed with recipes that will help you interpret plots with R
 Formulate advanced statistical models in R to understand its concepts
 Perform Bayesian regression to predict models and input missing data
 Use time series analysis for modelling and forecasting temporal data
 Implement a range of regression techniques for efficient data modelling
 Get to grips with robust statistics and hidden Markov models
 Explore ANOVA (Analysis of Variance) and perform hypothesis testing
Who this book is for
If you are a quantitative researcher, statistician, data analyst, or data scientist looking to tackle various challenges in statistics, this book is what you need! Proficiency in R programming and basic knowledge of linear algebra is necessary to follow along the recipes covered in this book.
Publisher resources
Table of contents
 Title Page
 Copyright and Credits
 About Packt
 Contributors
 Preface

Getting Started with R and Statistics
 Introduction
 Technical requirements
 Maximum likelihood estimation
 Calculating densities, quantiles, and CDFs
 Creating barplots using ggplot
 Generating random numbers from multiple distributions
 Complex data processing with dplyr
 3D visualization with the plot3d package
 Formatting tabular data with the formattable package
 Simple random sampling
 Creating diagrams via the DiagrammeR package
 C++ in R via the Rcpp package
 Interactive plots with the ggplot GUI package
 Animations with the gganimate package
 Using R6 classes
 Modeling sequences with the TraMineR package
 Clustering sequences with the TraMineR package
 Displaying geographical data with the leaflet package
 Univariate and Multivariate Tests for Equality of Means

Linear Regression
 Introduction
 Computing ordinary least squares estimates
 Reporting results with the sjPlot package
 Finding correlation between the features
 Testing hypothesis
 Testing homoscedasticity
 Implementing sandwich estimators
 Variable selection
 Ridge regression
 Working with LASSO
 Leverage, residuals, and influence
 Bayesian Regression
 Nonparametric Methods
 Robust Methods

Time Series Analysis
 Introduction
 The general ARIMA model
 Seasonality and SARIMAX models
 Choosing the best model with the forecast package
 Vector autoregressions (VARs)
 Facebook's automatic Prophet forecasting
 Modeling count temporal data
 Imputing missing values in time series
 Anomaly detection
 Spectral decomposition of time series
 Mixed Effects Models
 Predictive Models Using the Caret Package
 Bayesian Networks and Hidden Markov Models
 Other Books You May Enjoy
Product information
 Title: R Statistics Cookbook
 Author(s):
 Release date: March 2019
 Publisher(s): Packt Publishing
 ISBN: 9781789802566
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