R Statistics Cookbook

R Statistics Cookbook

eBook Details:

  • Paperback: 448 pages
  • Publisher: WOW! eBook (March 29, 2019)
  • Language: English
  • ISBN-10: 1789802563
  • ISBN-13: 978-1789802566

eBook Description:

R Statistics Cookbook: Solve real-world statistical problems using the most popular R packages and techniques

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.

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

By the end of this R Statistics Cookbook 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.


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