a primer in statistical discovery

Copyright © 2013, SAS Insti tute Inc. Al l r ights reserved.

A PRIMER IN STATISTICAL DISCOVERY

ECSITE ANNUAL CONFERENCE 2015

Dr. Ian Cox, [email protected]

mailto:[email protected]


ABSTRACT

• This presentation introduces ’Statistical Discovery’, a process that allows you

to work with data to discover new, useful, insights that drive cycles of

learning.

• After a brief overview to introduce the concept, an example involving property

prices in the US will be used to demonstrate the how the process works in

practice.

• Through this example we also exemplify the skills and aptitudes required to

exercise the process successfully.


AGENDA

• Why?

• What?

• How?

• Who?


ECSITE VALUES

• We believe that science is part of culture and that curious minds make for

better societies.

• We are convinced that science needs fair, open-minded, democratic societies

and active citizenship to thrive.

• We know that citizens and scientists working together with other

stakeholders can make research more relevant.

• We find science is fascinating and we are dedicated to making it a source of

inspiration for everyone.


PREMISE OF THIS

PRESENTATION

So, in the areas of:

• Science…

• Evidence-based public policy…

• The more utilitarian world of providing products and services…

it’s good to have an appreciation of how new knowledge is acquired.

Better yet is to be able to communicate how this process works, and perhaps

even to encourage it’s more widespread use.

Especially as ‘big data’ takes hold . . .


SCIENTIFIC

METHOD TIMELINE


A COLLOQUIAL

VIEW

http://idea.ucr.edu/documents/flash/scientific_method/story.htm

http://idea.ucr.edu/documents/flash/scientific_method/story.htm


‘MAKE

OBSERVATIONS’

PRODUCES DATA,

AND …

• All data is contextual.

• Any analysis that does not understand the data context is pointless, or worse.

• Making new discoveries is not ‘algorithmic’, and never could be.

• Only people understand ‘context’, ‘relevance’ and ‘utility’.


AGENDA

• Why?

• What?

• How?

• Who?


A ‘QUALITY’

PERSPECTIVE

All value-adding activity occurs in a system of interconnected processes,

each of which has suppliers, owner(s) and customers.

Variation exists in all processes.

Causes of process variation can be segregated into ‘common’ and ‘special’

causes.

Understanding the unique nature of common and special causes is the key to

reducing process variation,

and

Reducing process variation is the key to improving product and service

quality, productivity and profitability.


UNIVERSAL MODEL

OF A PROCESS

System of InterestCauses We Understand

Causes We Don’t Understand, Know About, or Care About

Measured Effects or Outcomes of

Interest

X1

X2

X3

X4 X5 X6

Y1

Y2

Y1 = Signal Function1(X1, X2, X3) + Nuisance Function1(X4, X5, X6)

Y2 = Signal Function2(X1, X2, X3) + Nuisance Function2(X4, X5, X6)

The ‘Nuisance Functions’ are what give rise to the Variation

in the process outputs.

‘Statistics’ can be seen as the study of variation.


‘STATISTICS’ IS . . . LEARNING IN THE PRESENCE OF VARIATION

(Adapted from Box,

Hunter and Hunter).

What we think

is happening

What is really

happening

Situation

Appraisal

Measurement and

Data Collection

Situation

Appraisal

Situation

AppraisalMeasurement and

Data Collection

Sta

tisti

cs

Able to Consistently Meet Customer Requirements

Real

WorldModel

Unable to Consistently Meet Customer Requirements

There is no ‘free lunch’ – The cost of gaining new knowledge must be

offset by its perceived or expected value


STATISTICAL DISCOVERY WITH JMP


CDA EDA


STATISTICAL

DISCOVERY

Frame Problem

or Opportunity Collect DataUncover

Relationships

Model

Relationships

Utilize

Knowledge

Revise

Knowledge

“Statistics as

Detective” or EDA

“Statistics as

Lawyer” or CDA


STATISTICAL

DISCOVERY

1. Data visualization, done properly, is very powerful and effective.

2. Statistical analysis, done properly, and defined broadly to include things

like experimental design and predictive modeling, is also very powerful

and effective, but in a different way.

3. Tightly integrating the two creates a synergy that is much more

powerful and effective than either one alone.


AGENDA

• Why?

• What?

• How?

• Who?


JMP …

• Aims to provide ‘Statistical Discovery’ on

the desktop of Researchers, Engineers

and Scientists.

• Is characterized by interactivity and agility

in usage.

• Is a SAS product which has been in

development since 1989.

• Aspires to support users who:

• Have specific research questions, or are

simply curious.

• Have access to, or can generate, somewhat

relevant data.

• Do not necessarily have specialist statistical

training.

a primer in statistical discovery

Software

sas institute

process variation

data context

special causes

big data

science evidence

science needs fair

better societies