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Solve real-world statistical problems using the most popular R packages and techniques. 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.
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.
About Packt
Preface
Getting Started with R and Statistics
Univariate and Multivariate Tests for Equality of Means
Linear Regression
Bayesian Regression
Using a model for prediction
Getting ready
How to do it...
How it works...
GLMs in JAGS
Getting ready
How to do it...
How it works...
Nonparametric Methods
Robust Methods
Time Series Analysis
Predictive Models Using the Caret Package
Bayesian Networks and Hidden Markov Models