Jon R. Kettenring

The Practice of Cluster Analysis

AbstractCluster analysis is one of the main methodologies for analyzing multivariate data. Its use is widespread and growing rapidly. The goal of this article is to document this growth, characterize current usage, illustrate the breadth of applications via examples, highlight both good and risky practices, and suggest some research priorities.

The Future of Industrial Statistics: A Panel Discussion

Technometrics was founded in 1959 as a forum for publishing statistical methods and applications in engineering and the physical and chemical sciences. The expanding role of statistics in industry was a major stimulus, and, throughout the years many articles in the journal have been motivated by industrial problems. In this panel discussion we look ahead to the future of industrial statistics. Ten experts, encompassing a range of backgrounds, experience, and expertise, answered my request to share with us their thoughts on what lies ahead in industrial statistics. Short biographical sketches of the panelists are provided at the end of the discussion. The panelists wrote independent essays, which I have combined into an integrated discussion. Most of the essays were written as responses to a list of 10 questions that I provided to help the participants direct their thoughts. I have organized the discussion in that same fashion, stating the questions and then providing the related responses. Several discussants added remarks on the role of statistics journals, particularly of Technometrics, and I have added that as a final question. We see this article, not as the end of the story, but rather as the takeoff point for further discussion. To that end, we are initiating an open discussion forum; to participate, go to http://www.asq.org/pub/techno/ and click on Networking and Events. The American Society for Quality will host the forum and Bert Gunter has graciously agreed to serve as moderator.

What Industry Needs

Abstract Industry needs holistic statisticians who are nimble problem solvers. They need to be able to work smoothly on teams and to communicate effectively about their work. Many of industrys statistics problems are interdisciplinary and involve exciting challenges in unplowed areas. Industry needs people who thrive on such opportunities.

A conversation about implicit bias

Explicit bias reflects our perceptions at a conscious level. In contrast, implicit bias is unintentional and operates at a level below our conscious awareness. Implicit stereotypes shaping implicit biases are widely studied in criminal justice, medicine, CEO selection at Fortune 500 companies, etc. However, the problem of unconscious bias remains. E.g., while women constitute an increasing proportion of all STEM undergraduates, they still make up only a small proportion of faculty members at research universities, and they are substantially under-represented in organizational leadership and as recipients of professional awards and prizes. Can we afford to have unintentional perceptions continue to hinder the success and advancement of women and other underrepresented groups? Can we afford to continue to underuse human capital in science? This session at the 2015 Joint Statistical Meetings (JSM) aimed to illuminate what statisticians need to know and do to break the glass ceiling of implicit bias.

Challenges and Opportunities for Statistics in the Next 25 Years

Beginning with the 75th Anniversary of the American Statistical Association in 1914 and for subsequent 25-year celebrations, distinguished members of the association have addressed the future of statistics. A four-person panel engaged in the same exercise during the 2014 Joint Statistical Meetings for the ASAs dodransbicentennial. The panel identified a variety of strengths, weaknesses, opportunities, and threats for the profession in the next quarter of a century. This article highlights some of the discussion that took place.