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Statistical Tableau

How to Use Statistical Models and Decision Science in Tableau

Paperback Engels 2024 1e druk 9781098151799
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Samenvatting

In today's data-driven world, understanding statistical models is crucial for effective analysis and decision making. Whether you're a beginner or an experienced user, this book equips you with the foundational knowledge to grasp and implement statistical models within Tableau. Gain the confidence to speak fluently about the models you employ, driving adoption of your insights and analysis across your organization.

As AI continues to revolutionize industries, possessing the skills to leverage statistical models is no longer optional—it's a necessity. Stay ahead of the curve and harness the full potential of your data by mastering the ability to interpret and utilize the insights generated by these models.

Whether you're a data enthusiast, analyst, or business professional, this book empowers you to navigate the ever-evolving landscape of data analytics with confidence and proficiency. Start your journey toward data mastery today.

In this book, you will learn:
- The basics of foundational statistical modeling with Tableau
- How to prove your analysis is statistically significant
- How to calculate and interpret confidence intervals
- Best practices for incorporating statistics into data visualizations
- How to connect external analytics resources from Tableau using R and Python

Specificaties

ISBN13:9781098151799
Taal:Engels
Bindwijze:paperback
Aantal pagina's:325
Uitgever:O'Reilly
Druk:1
Verschijningsdatum:17-5-2024
Hoofdrubriek:IT-management / ICT
ISSN:

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Inhoudsopgave

Preface
This Book’s Purpose
This Book’s Audience
This Book’s Structure
Conventions Used in This Book
O’Reilly Online Learning
How to Contact Us
Acknowledgments

1. Introduction
Introduction to Tableau
Common Terms of the Authoring Interface of Tableau Desktop
Example of the Step-by-Step Instructions Throughout This Book
Introduction to Statistics
Common Statistical Terms
Practical Application Through a Case Study
Data Visualization and Statistics
Summary

2. Overview of the Analytics Pane
What Is the Analytics Pane?
Implementing Summarization Options from the Analytics Pane
Implementing Model Options from the Analytics Pane
Implementing Custom Options from the Analytics Pane
Summary

3. Benchmarking in Tableau
What Is a Benchmark?
Internal Benchmarking
External Benchmarking
Implementing Benchmarks in Tableau
Static Reference Line
Dynamic Reference Line Using User-Driven Parameter
Dynamic Reference Line Using Level-of-Detail Calculation
Median with Quartiles
Summary

4. Understanding Normal Distribution Using Histograms
Types of Distribution
Uniform Distribution
Bernoulli Distribution
Exponential Distribution
Normal Distribution
Normal Distribution and Skewness
Understanding Skewness
Accounting for Skewness
How to Visualize Distributions in Tableau Using Histograms
Parametric Models
Nonparametric Models
Summary

5. Understanding Confidence Intervals
What Is a Confidence Interval?
How to Calculate Confidence Intervals
Interpreting the Results
How to Calculate Confidence Intervals in Tableau
Check the Confidence Interval You Solved by Hand
Implement Confidence Intervals on a Sample Dataset
Summary

6. Anomaly Detection on Normally Distributed Data
Understanding Standard Deviations
How to Implement Standard Deviations in Tableau to Find Anomalies
Understanding Median with Quartiles
How to Use Median with Quartiles in Tableau to Find Anomalies
Understanding Z-Score Tests
How to Use Z-Scores in Tableau to Find Anomalies
Summary

7. Anomaly Detection on Nonnormalized Data
Understanding Median Absolute Deviation
How to Implement Median Absolute Deviations in Tableau
Understanding Modified Z-Score
How to Implement Modified Z-Scores in Tableau
Understanding Tukey’s Fences
How to Implement Tukey’s Fences in Tableau
Summary

8. Linear Regression in Tableau
Linear Regression Model
Linear Regression Expressed Mathematically
Simple Linear Regression Example
Assumptions of Linear Regression Models
Implementing Linear Regression in Tableau
R-squared
P-value
Interpreting the Detailed Summary Statistics
Summary

9. Polynomial Regression in Tableau
What Is Polynomial Regression?
Polynomial Regression Equation
Polynomial Regression Expressed Mathematically
Choosing the Right Model
How to Implement Polynomial Regression in Tableau
Summary

10. Forecasting in Tableau
What Is Exponential Smoothing?
Exponential Smoothing Equations
How to Implement Forecast Models in Tableau
Forecast Options
Describe Forecast
Summary

11. Clustering in Tableau
What Is K-Means Clustering?
K-Means Conceptual Example
K-Means Example with Real Values
Supervised Versus Unsupervised Learning Methods
Supervised Learning
Unsupervised Learning
How to Implement Clustering in Tableau
Summary

12. Creating an External Connection to R Using Tableau
What Is R Code?
Key Features
What Is RStudio?
Installing R and RStudio
Installing R
Installing RStudio
Establishing an External Connection in Tableau
Summary

13. Creating an External Connection to Python Using Tableau
What Is Python?
Key Features
What Is Anaconda?
Installing Python and Anaconda
Establishing an External Connection to Python in Tableau
Summary

14. Understanding Multiple Linear Regression in R and Python
What Is Multiple Linear Regression?
Multiple Linear Regression Equation
How to Implement Multiple Linear Regression in R
How to Implement Multiple Linear Regression in Python
Summary

15. Using External Connections in Tableau
Script Functions in Tableau
Passing Arguments in a Tableau Script Function
Table Calculations and Knowing the Level of Detail
Example of Using an External Connection to R from Tableau
Example of Using an External Connection to Python from Tableau
Summary

Index
About the Author

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