Solve real-world 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 t-tests, chi-squared tests, and regression analysis using modern statistical techniques
Book DescriptionR 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.
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 forIf 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.
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 t-tests, chi-squared tests, and regression analysis using modern statistical techniques
Book DescriptionR 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.
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 forIf 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.
Share This Book: