R Statistics Cookbook
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.
|Course Length||13 hours 26 minutes|
|Date Of Publication||29 Mar 2019|
|Computing ordinary least squares estimates|
|Reporting results with the sjPlot package|
|Finding correlation between the features|
|Implementing sandwich estimators|
|Working with LASSO|
|Leverage, residuals, and influence|
|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|
|Spectral decomposition of time series|