Discover the story of your data using the essential elements of associations and correlations
Get a comprehensive introduction to associations and correlations
Explore multivariate analysis, understand its limitations, and discover the assumptions on which it's based
Gain insights into the various ways of preparing your data for analysis and visualization
Associations and correlations are ways of describing how a pair of variables change together as a result of their connection. By knowing the various available techniques, you can easily and accurately discover and visualize the relationships in your data.
This book begins by showing you how to classify your data into the four distinct types that you are likely to have in your dataset. Then, with easy-to-understand examples, you'll learn when to use the various univariate and multivariate statistical tests. You'll also discover what to do when your
univariate and multivariate results do not match. As the book progresses, it describes why univariate and multivariate techniques should be used as a tag team, and also introduces you to the techniques of visualizing the story of your data.
By the end of the book, you'll know exactly how to select the most appropriate univariate and multivariate tests, and be able to use a single strategic framework to discover the true story of your data.
What you will learn
Identify a dataset that's fit for analysis using its basic features
Understand the importance of associations and correlations
Use multivariate and univariate statistical tests to confirm relationships
Classify data as qualitative or quantitative and then into the four subtypes
Build a visual representation of all the relationships in the dataset
Automate associations and correlations with CorrelViz
Who this book is for
This is a book for beginners – if you're a novice data analyst or data scientist, then this is a great place to start. Experienced data analysts might also find value in this title, as it will recap the basics and strengthen your understanding of key concepts. This book focuses on introducing
the essential elements of association and correlation analysis.
Solve real-world statistical problems using the most popular R packages and techniques
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
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.