Exploring Data | Inspiring Innovation
Prague | 21-23 March 2017
John Sall, Co-Founder and Executive Vice President, SAS
Triskaidekaphilia. This word means “love of the number 13.” With the release of JMP® 13, we plan to make this word meaningful. This session is a tour of some feature highlights of the new release.
To Explain or to Predict?
Galit Shmueli, Tsing Hua Distinguished Professor, Institute of Service Science, National Tsing Hua University, Taiwan
Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction and description. In many disciplines, there is near-exclusive use of statistical modeling for causal explanation with the assumption that models with high explanatory power are inherently of high predictive power. Conflation between explanation and prediction is common, yet the distinction must be understood for progressing scientific knowledge and for proper use in practice.
Understanding the differences between explanatory and predictive modeling and assessment is crucial for being able to assess a data set’s information quality – its potential to achieve a scientific/practical goal using data analysis. While the explain-predict distinction has been recognized in the philosophy of science, the statistical and data mining literature lack a thorough discussion of the many differences that arise in the process of modeling for an explanatory versus a predictive goal. In this talk I will clarify the distinction between explanatory and predictive modeling and reveal the practical implications in terms of data analysis.
From Quality by Design to Information Quality: A Journey Through Science and Business Analytics
Ron S. Kenett, Research Professor, Mathematics Department, University of Turin, Italy
This talk is a journey meandering between science and business analytics. To provide context I will first list, very briefly, my role models. Specifically, I will mention Sir David Cox, who taught me the introduction to statistics class as an undergraduate at Imperial College; Sam Karlin, who was my PhD adviser at Stanford and the Weizmann Institute; George Box and Bill Hunter, who opened the door for me to applied statistics in business and industry; as well as Stu Hunter, Ed Deming and Joe Juran. They all had a significant impact on my career. The next stop on the journey will provide a brief introduction to Quality by Design (QbD), as applied in the pharmaceutical industry. The third stop will discuss a topic of growing concern in science – reproducibility of research findings. To address this issue, I will sketch a new proposal based on generalizability of findings. Generalization is one of the eight dimensions of information quality (InfoQ), and this stop represents joint work with Galit Shmueli carried over the last eight years and summarized in our recent book. A final stop will discuss challenges ahead for analytics and statistics. The motivation behind this journey is to demonstrate the key role of statistical thinking in modern analytics and its impact both on science and business applications. Eventually, these thoughts and examples are driven by the ambition to put statistics back in the driver’s seat of data-driven work. Throughout, I will show some examples using JMP to make the case.
Statistical Thinking and Politics: Perspectives From a Parliamentary Experience
Pedro Manuel Saraiva, Full Professor, Chemical Engineering Department, University of Coimbra, Portugal
After some decades in an academic career, during which I used a strong statistical background to conduct research activities, I was given the opportunity to run twice for election as a Member of the National Parliament of Portugal. It is mostly about this challenging experience and period of my life (ranging from 2009 to 2015) as a Member of Parliament (MP) that I will share examples, thoughts and conclusions. This talk will show evidence of how statistical thinking and tools, as well as fact-based approaches, can provide a better understanding about how Parliaments work and some of the strongest features of their organizational cultures, and help achieve better results, increased efficiency and efficacy. For that purpose, specific illustrations will be provided, through which I will try to show namely how statistical tests, variation analysis, clustering or Bayesian interpretations were applied to several situations related to the Portuguese Parliament, politics and politicians. I hope that this presentation will provide enough support to show that indeed statistical thinking and tools can help to better understand and improve Parliaments and help politicians make better fact-based decisions. Parliaments and societies are likely to improve if more people with a good statistical background accept the challenge of becoming a MP, at least for a while.
- Beginner: 1
- Intermediate: 2
- Advanced: 3
- Power user: 4