A Primer in Statistical Discovery

2. Copyright © 2013, SAS Institute Inc. All rights reserved. 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.

5. Copyright © 2013, SAS Institute Inc. All rights reserved. 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.

6. Copyright © 2013, SAS Institute Inc. All rights reserved. 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 . . .

9. Copyright © 2013, SAS Institute Inc. All rights reserved. ‘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’.

11. Copyright © 2013, SAS Institute Inc. All rights reserved. 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.

12. Copyright © 2013, SAS Institute Inc. All rights reserved. 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.

13. Copyright © 2013, SAS Institute Inc. All rights reserved. ‘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 Appraisal Measurement and Data Collection Statistics Able to Consistently Meet Customer Requirements Real World Model 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

16. Copyright © 2013, SAS Institute Inc. All rights reserved. STATISTICAL DISCOVERY Frame Problem or Opportunity Collect Data Uncover Relationships Model Relationships Utilize Knowledge Revise Knowledge “Statistics as Detective” or EDA “Statistics as Lawyer” or CDA

17. Copyright © 2013, SAS Institute Inc. All rights reserved. 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.

21. Copyright © 2013, SAS Institute Inc. All rights reserved. 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.