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Advanced Analytics with R and Tableau

You're reading from   Advanced Analytics with R and Tableau Advanced analytics using data classification, unsupervised learning and data visualization

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781786460110
Length 178 pages
Edition 1st Edition
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Authors (3):
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Roberto Rösler Roberto Rösler
Author Profile Icon Roberto Rösler
Roberto Rösler
Ruben Oliva Ramos Ruben Oliva Ramos
Author Profile Icon Ruben Oliva Ramos
Ruben Oliva Ramos
Jen Stirrup Jen Stirrup
Author Profile Icon Jen Stirrup
Jen Stirrup
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Table of Contents (10) Chapters Close

Preface 1. Advanced Analytics with R and Tableau FREE CHAPTER 2. The Power of R 3. A Methodology for Advanced Analytics Using Tableau and R 4. Prediction with R and Tableau Using Regression 5. Classifying Data with Tableau 6. Advanced Analytics Using Clustering 7. Advanced Analytics with Unsupervised Learning 8. Interpreting Your Results for Your Audience Index

Getting started with regression


Regression means the unbiased prediction of the conditional expected value, using independent variables, and the dependent variable. A dependent variable is the variable that we want to predict. Examples of a dependent variable could be a number such as price, sales, or weight. An independent variable is a characteristic, or feature, that helps to determine the dependent variable. So, for example, the independent variable of weight could help to determine the dependent variable of weight.

Regression analysis can be used in forecasting, time series modeling, and cause and effect relationships.

Simple linear regression

R can help us to build prediction stories with Tableau. Linear regression is a great starting place when you want to predict a number, such as profit, cost, or sales. In simple linear regression, there is only one independent variable x, which predicts a dependent value, y.

Simple linear regression is usually expressed with a line that identifies...

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