Welcome
Think Outside the Box(plot)

Jerry Valerio

Datavangelist

Tableau
Jerry Valerio

Senior Manager, Sales Consulting

gvalerio@tableau.com

Gerard is a data engineer, data evangelist, and data strategist 

with customer advisory experience working for Tableau, and 

previously Vertica and Informatica and management consulting 

experience previously working for Accenture and 

PricewaterhouseCoopers.
Jerry Valerio

§ Foodie since girth and it shows!

 Side hustles as adjunct 

professor and data science 

bootcamp instructor.

 Sky-dived (tandem) and also 

zip-lined once because YOLO!
Audience

 Basic knowledge of statistics

 Interested in Tableau's statistical capabilities

 Distribution

 Summary 

 Modeling
Agenda
Why Visual Analysis?
Anscombe's Quartet

I II III IV

x y x y x y x y

10 8.04 10 9.14 10 7.46 8 6.58

8 6.95 8 8.14 8 6.77 8 5.76

13 7.58 13 8.74 13 12.74 8 7.71

9 8.81 9 8.77 9 7.11 8 8.84

11 8.33 11 9.26 11 7.81 8 8.47

14 9.96 14 8.1 14 8.84 8 7.04

6 7.24 6 6.13 6 6.08 8 5.25

4 4.26 4 3.1 4 5.39 19 12.5

12 10.84 12 9.13 12 8.15 8 5.56

7 4.82 7 7.26 7 6.42 8 7.91

5 5.68 5 4.74 5 5.73 8 6.89

Let's analyze some data ...
Anscombe's Quartet

Let's summarize the data ...

Property Value

Mean of x in each case 9 (exact)

Variance of x in each case 11 (exact)

Mean of y in each case 7.50 (to 2 decimal places)

Variance of y in each case 4.122 or 4.127 (to 3 decimal places)

Correlation between x and y in each 

case

0.816 (to 3 decimal places)

Linear regression line in each case y = 3.00 + 0.500x (to 2 and 3 decimal 

places, respectively)
Anscombe's Quartet

Let's visualize the data ...
Distribution
Histograms
Histograms show us the 

distribution of numerical data

Histograms
Histograms

Basic Histogram Cumulative Histogram
Percentiles
Percentiles indicate the value below which a 

given percentage of the observed data falls.

Ex: If a school is in the 66.7th percentile, 

their teacher score is better or stronger than 

2/3 of compared schools. 

Percentiles

Percentile of Teachers Score
Box Plots
Traditional Box Plot Tableau Box Plot

Box Plots
Anatomy of the Tableau Box Plot
Anatomy of the Tableau Box Plot

Interquartile 

Range
Anatomy of the Tableau Box Plot

1.5 times the 

IQR
Anatomy of the Tableau Box Plot

Outliers!
Summary Statistics?
Summary Card
Summary Card - Skewness
Summary Card - Kurtosis
Modeling
Applying mathematical 

functions to data in an attempt 

to surface hidden insights.

What do we mean by Modeling?
Unsupervised Classification

Similar with respect to several 

attributes

• Examples:

• Trend / Regression Lines

• Forecasts

• K-Means Clustering

Classifying Data

Supervised Classification

Similar with respect to a target

• Examples:

• Logistic Regression

• Decision Trees

• Neural Networks

• Random Forest
Trend Lines / Regression Lines
Trend Lines

Options

• Linear

• Exponential

• Logarithmic

• Polynomial

• Power

y = mX + b

Logarithmic

Options

• Linear

• Exponential

• Logarithmic

• Polynomial

• Power

Y = b0 + b1 * ln(X)

Trend Lines (Overview)
Trend Models: Describing the Formula
Trend Models: Evaluating Model Fit
Trend Lines

The R-Squared value shows the ratio of variance in the data, as explained by the model, to the total variance in the data. The 

P-value reports the probability that the equation of the line was a result of random chance. The smaller the p-value, the 

more significant the model is. A p-value of 0.05 or less is often considered sufficient.
Forecasting
• At least:

• One Dimension

• One Measure

• Dimension Requirements

• Date Field

or

• Integer Field

Forecast Requirements

NOTE: Tableau requires at least five data points in the time series to estimate a trend, and enough data points 

for at least two seasons or one season plus five periods to estimate seasonality.
Exponential Smoothing:

more recent values are given 

greater weight

Trend

Tendency in the data to increase 

or decrease over time

Forecasting Terms

Seasonality

Repeating, predictable variation in 

value, such as an annual 

fluctuation in temperature relative 

to the season.

Granularity

The unit you choose for the date 

value is known as the granularity of 

the date
Forecasting Models

Multiplicative models can significantly improve forecast quality for data where the trend or 

seasonality is affected by the level (magnitude) of the data
Forecast Description

Analysis > Forecast > 

Describe Forecast

• OK: less error than a naïve forecast

• GOOD: less than half as much error as 

a naïve forecast

• POOR: means that the forecast has more 

error.
Clustering
Clusters

K-means is a simple algorithm 

that tries to minimize the 

distance from a center point to all 

points in the same cluster.

But first, we need to make a 

reasonable estimate of the 

number of clusters in our data.

How many clusters should there 

be with this visualization?
Clusters

3 – simple enough. We use 

Calinski’s algorithm to determine 

“k”.

Then we use Lloyd’s algorithm to 

compute the distances from each 

center point in our three clusters 

to every point in our data.

Assign each point to the closest 

center.

Repeat until points don’t change 

center assignments.
Clusters – Describing the results

This shows the inputs to the 

clusters. We see our two 

variables, we were not 

aggregated and scaling was not 

adjusted.
Clustering

Grouping a set of objects such 

that marks within each cluster 

are more similar to one another 

than they are to marks in other 

clusters
Clustering
Clusters – Saving the results

The clustering is done. Three 

clusters with default names and 

colors. Plus, if new data comes 

in the data gets re-clustered and 

results may change.

Drag/drop the clusters pill onto 

the Data pane.

I’m going to rename it to Vehicle 

Type Clusters. Notice the icon 

will change.
Clusters – Fine tuning the saved group

Rename the groups to 

something more meaningful

e.g.:

• Fuel Efficient

• Middle of the Road 

• Gas Guzzlers
Clusters – Fine tuning the saved group

Now I can remove the adhoc “Clusters” group and 

replace it with my saved

group.

Update the colors and I

am done!
Correlation (is not Causation)
Correlation Coefficient

Pearson’s correlation 

coefficient is a measure of 

the strength and direction 

of the linear relationship 

between two variables
Correlation Function:

• Built into Tableau

• Uses Calculated Fields

Correlation computation
Recap & Last Notes
Distribution

Recap

• Histograms

• Percentiles

• Box Plots

• Control Charts

• Trend Lines

• Forecasting

• Clustering

• Correlation Coefficients

Distribution Modeling
Thank You

Jerry Valerio

gvalerio@tableau.com