Book description
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 Description
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.
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.
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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