Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Tableau 2019.x Cookbook

You're reading from   Tableau 2019.x Cookbook Over 115 recipes to build end-to-end analytical solutions using Tableau

Arrow left icon
Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789533385
Length 670 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (6):
Arrow left icon
Tania Lincoln Tania Lincoln
Author Profile Icon Tania Lincoln
Tania Lincoln
Slaven Bogdanovic Slaven Bogdanovic
Author Profile Icon Slaven Bogdanovic
Slaven Bogdanovic
Teodora Matic Teodora Matic
Author Profile Icon Teodora Matic
Teodora Matic
Rintaro Sugimura Rintaro Sugimura
Author Profile Icon Rintaro Sugimura
Rintaro Sugimura
Dmitry Anoshin Dmitry Anoshin
Author Profile Icon Dmitry Anoshin
Dmitry Anoshin
Dmitrii Shirokov Dmitrii Shirokov
Author Profile Icon Dmitrii Shirokov
Dmitrii Shirokov
+2 more Show less
Arrow right icon
View More author details
Toc

Table of Contents (18) Chapters Close

Preface 1. Getting Started with Tableau Software 2. Data Manipulation FREE CHAPTER 3. Tableau Extracts 4. Tableau Desktop Advanced Calculations 5. Tableau Desktop Advanced Filtering 6. Building Dashboards 7. Telling a Story with Tableau 8. Tableau Visualization 9. Tableau Advanced Visualization 10. Tableau for Big Data 11. Forecasting with Tableau 12. Advanced Analytics with Tableau 13. Deploy Tableau Server 14. Tableau Troubleshooting 15. Preparing Data for Analysis with Tableau Prep 16. ETL Best Practices for Tableau 17. Other Books You May Enjoy

Basic forecasting and statistical inference

The aim of this recipe is to introduce a basic forecasting method that relies on linear regression. We are going to use a built-in Tableau facility for linear regression. Simply put, regression analysis helps us discover predictors of a variable that we are interested in. We model the relationship between potential predictors and our variable of interest. Once we establish the model of the relationship between predictors and our variable, we can use it for further predictions.

To perform for casting, we will use the hormonal_response_to_excercise.csv dataset. This dataset comes from a health behavior study that aimed to explore the factors influencing cortisol response while exerting the maximal, peak effort during physical exercise (the Cortmax variable in our dataset).

Our first task is to explore how effectively we can predict the...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime