R Statistics Cookbook

More Information
  • 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

R is a popular programming language for developing statistical software. This book will be a useful guide to solving common and not-so-common challenges in statistics. With this book, you'll be equipped to confidently perform essential statistical procedures across your organization with the help of cutting-edge statistical tools.

You'll start by implementing data modeling, data analysis, and machine learning to solve real-world 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 well-versed with a wide array of statistical techniques in R that are extensively used in the data science industry.

  • 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 t-tests, chi-squared tests, and regression analysis using modern statistical techniques
Page Count 448
Course Length 13 hours 26 minutes
ISBN 9781789802566
Date Of Publication 29 Mar 2019
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 
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 
The standard model and ANOVA 
Some useful plots for mixed effects models 
Nonlinear mixed effects models 
Crossed and nested designs 
Robust mixed effects models with robustlmm 
Choosing the best linear mixed model
Mixed generalized linear models 


Francisco Juretig

Francisco Juretig has worked for over a decade in a variety of industries such as retail, gambling and finance deploying data-science solutions. He has written several R packages, and is a frequent contributor to the open source community.