# R Statistics Cookbook

 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 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 448 13 hours 26 minutes 9781789802566 29 Mar 2019
 Introduction The univariate t-test The Fisher-Behrens problem Paired t-test Calculating ANOVA sum of squares and F tests Two-way ANOVA Type I, Type II, and Type III sum of squares Random effects Repeated measures Multivariate t-test MANOVA
 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
 Introduction Getting the posterior density in STAN Formulating a linear regression model Assigning the priors Doing MCMC the manual way Evaluating convergence with CODA Bayesian variable selection Using a model for prediction GLMs in JAGS