Book Image

Advanced Analytics with R and Tableau

By : Ruben Oliva Ramos, Jen Stirrup, Roberto Rösler
Book Image

Advanced Analytics with R and Tableau

By: Ruben Oliva Ramos, Jen Stirrup, Roberto Rösler

Overview of this book

Tableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation. Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics. By the end of this book, you will get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.
Table of Contents (16 chapters)
Advanced Analytics with R and Tableau
About the Authors
About the Reviewers
Customer Feedback

Getting started with multiple regression?

Simple linear regression will summarize the relationship between an outcome and a single explanatory element. However, in real life, things are not always so simple! We are going to use the adult dataset from UCI, which focuses on census data with a view to identifying if adults earn above or below fifty thousand dollars a year. The idea is that we can build a model from observations of adult behavior, to see if the individuals earn above or below fifty thousand dollars a year.

Multiple regression builds a model of the data, which is used to make predictions. Multiple regression is a scoring model, which makes a summary. It predicts a value between 0 and 1, which means that it is good for predicting probabilities.

It's possible to imagine multiple regression as modeling the behavior of a coin being tossed in the air. How will the coin land—heads or tails? It is not dependent on just one thing. The reality is that the result will depend on other variables...