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Come to WSDS for Interaction, Knowledge, Community, Inspiration

Fri, 06/01/2018 - 7:00am

Not to Be Missed

Nancy Potok
Chief Statistician of the United States, US Office of Management and Budget
Aarti Shah
Senior Vice President, Information Technology, and Chief Information Officer, Eli Lilly & Co.
Claudia Perlich
Senior Data Scientist, Two Sigma
Alicia Carriquiry
Distinguished Professor of Statistics, Iowa State University
Mine Çetinkaya-Rundel
Associate Professor, Department of Statistics, Duke University, and Data Scientist and Professional Educator, RStudio
Shanthi Sethuraman
Senior Director Global Statistical Sciences, Eli Lilly & Co.

The Women in Statistics and Data Science conference has become one of the ASA’s most popular and positive conferences. Last year’s WSDS welcomed more than 450 attendees, sponsors, and exhibitors. This fall’s conference should be on your list of must-attend events.

Women in Statistics and Data Science will take place this October in Cincinnati, Ohio. When we convene, we will gather professionals and students from academia, industry, and the government who are working in statistics and data science. WSDS offers unique opportunities to grow your influence, your community, and your knowledge, but—more importantly—to interact with other leading women in the field.

With a wide range of content—including engaging plenaries, poster sessions, short courses, and concurrent sessions about managing family-work balance, cutting-edge advances, and growing in your career—each attendee will find enriching material to help them at any stage.

Leaders from academia, industry, and government will come together to present a world-class experience for attendees, from student and postgraduates to seasoned professionals. Aarti Shaah of Eli Lilly, Claudia Perlich of Dstillery/NYU, and Alicia Carriquiry from Iowa State will give plenary talks. The technical content will again be top notch, but what sets this conference apart is the hands-on, warm, and engaging environment that proves particularly conducive to learning and growing in both professional and personal ways. What do attendees say about WSDS? They call the meeting welcoming, inspiring, empowering, motivating, eye opening, and awesome!

Mark October 18–20 on your calendar and learn more by visiting the WSDS website.

Proposed Revisions to the ASA Bylaws

Fri, 06/01/2018 - 7:00am
Recommended by the Board of Directors April 13, 2018

The ASA Board of Directors proposes the following modifications to the ASA bylaws. The purpose of the changes is to ensure the ASA’s finance-related committee charges are consistent with current best practices and to update some provisions that are either no longer applicable or not reflective of current best practices.

Finance-related committee charges:
4.a. Audit Committee. The Audit Committee shall consist of the Treasurer, who acts as chair, the chair of the Budget Committee, and the Past President. It shall periodically recommend an audit firm to the Board of Directors; serve as the Board of Directors’ liaison to the Association’s auditors; represent the Board of Directors in discharging its responsibilities relating to the accounting, reporting, and financial practices of the ASA; have general responsibility for surveillance of internal controls, accounting, and audit activities of the ASA; ensure the audit is carried out in a fiscally sound manner; review with the audit firm their audit procedures, including the scope and timing of the audit, the results of the annual audit, and any accompanying management letters; assess the adequacy of internal controls and risk management systems; review the IRS Form 990, 990-T, and Virginia Form 500; review the document destruction and whistleblower policies; and review material about any pending legal proceedings involving the ASA. recommend an audit firm to the Board of Directors. It serves as the Board of Directors’ liaison to the Association auditors. It is responsible for seeing that the audit is carried out in a fiscally sound manner and that reports are prepared as needed by the Board of Directors.

4.b. Budget Committee. The Budget Committee shall consist of the three Vice Presidents and Treasurer, the latter ex officio without vote. The senior Vice President shall serve as chair of the committee. It is responsible The Committee shall annually recommend the operating budget for the coming fiscal year, including the Association staff compensation budget (salaries and fringe benefits), for action by the Board of Directors; periodically review the Association’s financial results in comparison to the budget; and periodically assess the facilities needs of the Association home office. for annually proposing the budget for the coming fiscal year. It is responsible for annually recommending a budget for action by the Board of Directors. It is also responsible for annually evaluating the capital budget, the salary classification structure, and the fringe benefits for the Association staff. It shall also periodically review the incomes, expenditures, and allocations during the year for consistency with the budget; the accounting system employed and the budgeting process; and the facilities need of the Association home office. If it so chooses, the Board of Directors as a group may serve as the Budget Committee.

5.d. Finance Investments Committee. The Finance Investments Committee shall recommend to the Board of Directors, and assess adherence to, investment guidelines that will improve the safety, return, reporting, or management of the investment accounts; periodically review the holdings in the investment accounts of the Association; assess appropriate benchmarks for investment performance; evaluate the performance of the investment managers and consultants; recommend to the Board of Directors, as appropriate, steps that will improve the safety, return, reporting, and/or management of the investment accounts; and such other matters related to the financial performance of the Association as the Board may assign from time to time.recommend long-term financial planning, supervise the investments of the Association, and carry out other duties as determined by the Board of Directors. The Finance Committee shall consist of the Treasurer as chair and six full members, each serving a three-year term, designated by the President-Elect.

Other revision recommendations:

4. Directory. At suitable intervals, the Association shall make available a directory of its members. At suitable intervals, the Constitution and By-Laws of the Association shall be published.

3. Authority. All funds of the Association shall be deposited with the Treasurer, who shall make disbursement therefrom under regulations of the Board of Directors. The Treasurer shall have authority to purchase securities with funds that the Board of Directors has designated for investment and to sell such securities, but such purchases and sales shall be made only in accordance with such guidelines as the Board of Directors shall prescribe.

The Board of Directors may appoint full members of the Association residing outside the United States to serve as depositories for funds.

With the approval of the Board of Directors, the Treasurer may delegate the powers listed in the first paragraph of this section, as well as the power to sign checks and to access safe-deposit boxes.

4. Surety Bonds. All persons who are responsible for the disbursement of funds shall be insured by a surety and performance bond in amounts and with companies approved by the Board of Directors. Fidelity: All persons who are responsible for the disbursement of funds shall be held as covered under a blanket Employee Dishonesty policy at limits approved by the Board of Directors.

10. Indemnity. The Association shall indemnify each person who was or is a party or is threatened to be made a party to any threatened, pending, or completed action, suit, or proceeding, whether civil, criminal, administrative, or investigative, by reason of serving at the request of the Association as a director, officer, employee, or agent of another organization, against all judgments, penalties, fines, and settlements, and against all reasonable expenses, including attorneys’ fees, actually incurred in connection with such action, suit, or proceeding, to the fullest extent permitted by Massachusetts law, except if the actual or potential liability is due to the person’s own negligence or gross negligence, or criminal misconduct, or action in violation of ASA rules or policies.

Note: In accordance with the bylaws, the membership shall have 75 days to review and respond to any proposed change. Please direct comments to the executive director and ASA secretary by September 15, 2018. Member comments will be shared with the ASA Board of Directors before further action regarding these changes is taken.

Stats4Good: The (Higher) Power of Data for Good

Fri, 06/01/2018 - 7:00am

This column is written for those interested in learning about the world of Data for Good, where statistical analysis is dedicated to good causes that benefit our lives, our communities, and our world. If you would like to know more or have ideas for articles, contact David Corliss.

With a PhD in statistical astrophysics, David Corliss works in analytics architecture at Ford Motor Company while continuing astrophysics research on the side. He serves on the steering committee for the Conference on Statistical Practice and is president-elect of the Detroit Chapter. He is the founder of Peace-Work, a volunteer cooperative of statisticians and data scientists providing analytic support for charitable groups and applying statistical methods to issue-driven advocacy in poverty, education, and social justice.

Data for Good volunteers can be found in many places and situations—at work, Data for Good organizations like Statistics without Borders, DataKind, and topic-driven organizations focused on a particular subject such as supporting a school. One area attracting volunteers for good causes are faith-based organizations. Obviously, Data for Good brings in people across the spectrum—from entirely secular to religiously motivated, from every faith and none. For those connected to a faith-based group in some way, Data for Good volunteers can be an invaluable resource.

Many faith-based groups have turned to statistics and data science as critical components of achieving their mission of serving people and the community. Identifying drivers of poverty and homelessness, survey design and analysis, models to improve the effectiveness of refugee programs, discrimination and injustice research, and data-driven guidance for reform initiatives such as prisons and sentencing are a few examples of how faith-based groups are using statistical volunteers today. The most common use of statistics, however, is in operations research for the organization itself—surveys to understand the needs and interests of members, increasing membership and fundraising, and optimizing the use of space and other resources.

A great example of what can be done at a local level can be found at a synagogue in Chicago, Congregation Rodfei Zedek. Located near The University of Chicago and with many people having analytic experience in the congregation, Rodfei Zedek has formed its own informatics committee. Led by congregation member and statistician Andrea Frazier, the team’s goals include building stronger relationships and fostering data-driven decision-making.

An important analytic use case for any membership organization is … membership! The informatics committee at Rodfei Zedek needs to track both individual and group memberships—classes and activities, households, and larger family associations. The informatics team digitized all the records, cleaned the data, established variables for various group memberships, and flagged special skills—for example, informatics! All members are matched to roles in which they possess the requisite skills to broaden the number of people participating. This database has resulted in more efficient program management, improved program participation, and better use of member resources.

The informatics team also evaluates programs. Surveys are conducted using one of the common online survey tools and the data analyzed and visualizations created to better understand how people feel about programs. Analysis produces data-driven insights to guide improvements. Predictive modeling is used to understand the key factors driving member engagement and estimate the attendance to be expected for a given event. Events can be selected based of the level of interest within the group and planned with clear expectations of the amount of participation. An event that will attract dozens or more can be placed in a larger room and more volunteers recruited to support it.

As people involved with charity management will be familiar, some important activities will attract just a handful of people. Predictive analytics can direct these toward smaller meeting rooms, or even other locations such as people’s homes.

Many important religious celebrations occur on different days in the civil calendar each year. Easter, for example, falls on the Sunday after the first full moon in spring, while Diwali falls on the new moon in the period from late October to early November.

Predictive analytics can describe the interaction of these “moveable feasts” with the civil calendar based on day of the week and other events. Analytics predicting attendance—and therefore required resources—can also address over-crowded holiday periods. Predictive analytics can support an answer to those who want to push one more event into an already over-crowded holiday period by giving solid estimates of the number of volunteers required and how many people will be able to participate.

Statistical science can analyze and identify the challenges facing the wider community, enabling closer partnerships and helping to address the sadly common issue of congregations that have grown away from their surrounding community. Frazier emphasizes the diverse purposes Data for Good can serve, which can be used “to save the world, but it’s also valuable for enhancing your own community. … It’s a great tool for the greater good!”

Once an informatics team is developed, it can take on challenges well beyond the walls of the congregation. Assessing the needs of the community, fighting poverty and homelessness, supporting local schools—almost any objective of the community groups you are active in can be helped by a Data for Good team.

While the Rodfei Zedek informatics team was developed to use the analytic resources available within a particular community of faith, the model can be applied to many kinds of organizations. School support groups, service organizations (e.g., Rotary, Kiwanis, etc.), alumni organizations, and many more can benefit. As long as there is a large group of people, especially where there are many professions, there is likely to be a subset with the analytic and data skills needed to form an informatics team.

Does your community, civic, faith-based, or other organization use statistics and data science for projects in your community? Let us know! We are always looking for inspiring examples of Data for Good to feature in this column.

For new Data for Good opportunities this month, consider having a look at Statistics Without Borders. It’s a great organization with many wonderful opportunities to work in Data for Good. Also, Peace-Work is looking for people interested in homelessness solutions to study the Utah program that has reduced homelessness there by 91% in recent years and perform economic analysis of the feasibility of doing the same in the investigator’s home state. You can contact them via their website.

Academic Twitter – Statistics Education

Fri, 06/01/2018 - 7:00am

Many academics and fields use Twitter as a professional resource. As we all know, statistics education is a field filled with great ideas and wonderful people from all over the world. However, searches for posts relating to statistics education return few results, indicating a lack of presence of our field on Twitter. The information below should help academics and professionals who work at the intersections of statistics, education, and teaching to create and use Twitter accounts to help develop an active, informative social media network.

  • Microblogging: Activity or practice of making short, frequent posts to a microblog (e.g., Twitter).
  • Hashtag: A word or phrase preceded by a hash or pound sign (#) and used to identify messages about a specific topic.
  • List: A curated group of Twitter accounts. You can create your own lists or subscribe to lists created by others. Viewing a list timeline will show you a stream of Tweets from only the accounts on that list.
  • Follow: Following another user means that all their tweets will appear in your feed.
Uses of Twitter for Academics
  • Build/maintain professional networks: during conferences; information sharing; literature recommendations; learn about academic/professional opportunities; career advice; microblogging
  • Advertise: research; events; publications; other updates
  • Increase visibility: individual; field
Academic Twitter Resources Twitter Accounts to Follow
  • @AmstatNews American Statistical Association
  • @RoyalStatSoc Royal Statistical Society
  • @CAUSEweb Consortium for the Advancement of Undergraduate Statistics Education
  • @NCTM National Council of Teachers of Mathematics
  • @IntCSE International Centre for Statistical Education
  • @ThisisStats ASA project to raise awareness of careers in statistics
  • @signmagazine Statistics magazine and website by the Royal Statistics Society and ASA
  • @DrSteveFoti Me
Common Abbreviations

Since a tweet is limited to 140 characters, abbreviations are used to replace commonly used phrases. This is a list of frequently used abbreviations, but you will likely encounter many more. Use your favorite search engine if you need help decoding one.

  • RT: retweet
  • MT: modified tweet
  • FWIW: for what it’s worth
  • BTW: by the way
  • IMO: in my opinion
Relevant Hashtags
  • #statistics
  • #statistician
  • #StatEd
  • #StatisticsEducation
  • #statliteracy
  • #biostatistics
  • #BiostatEd
  • #data
  • #dataliteracy
  • #JSM2018
  • #DataScience
  • #rstats
  • #NoticeWonder


Steven Foti is a clinical assistant professor in the department of biostatistics and the director of the online MS program at the University of Florida. He earned his PhD in statistics education and his MS in statistics from the University of Florida, while earning his BS in applied mathematics and statistics and physics from Clarkson University. He teaches biostatistics courses to both undergraduate and graduate students in public health and medicine. Follow Foti on Twitter by searching @DrSteveFoti.

Statistics Association Presidents Establish Elizabeth L. Scott, F.N. David Lectureships

Fri, 06/01/2018 - 7:00am
Amanda L. Golbeck

    Elizabeth Scott

    F.N. David

    The Committee of Presidents of Statistical Societies (COPSS) announced in April the establishment of two lectureships named after women: The Elizabeth L. Scott Lecture and the F.N. David Lecture. The lectures will be given in alternate years at the annual Joint Statistical Meetings beginning in 2019.

    This will be the first time JSM, which has been held annually since 1840, will have lectures named after women. JSM is the largest gathering of statisticians in North American and one of the largest in the world. Each year, there are more than 6,000 participants from more than 50 countries.

    The Elizabeth L. Scott Lecture and F.N. David Lecture will be included in the COPSS portfolio, which already includes the Fisher Lecture. According to Nick Horton, chair of COPSS, “One of the main tasks for COPSS involves granting awards that highlight the work of notable statisticians. I’m proud that starting in 2019, at least one of the lectures at the JSM will be named after a woman. This is long overdue.”

    The Caucus for Women in Statistics (CWS) spearheaded the effort to establish the lectureships. Horton reported the COPSS Executive Committee voted unanimously to approve the CWS proposal. CWS partnered with the ASA LGBT Concerns Committee, ASA Committee on Women in Statistics, Statistical Society of Canada Committee on Women, International Statistical Institute Committee on Women, and International Biometric Society ENAR/WNAR.

    The idea that too few women receive national recognitions for their research and scholarship is not new. The National Science Foundation in 2010 established an AWARDS project “to investigate and improve the process of granting awards and prizes for scholarly achievement” in disciplines like statistics. This project led to many association reforms.

    Establishing a new named lecture slot at JSM for the Scott and David lectures is another significant step forward in advancing the statistics profession. It adds a face to the profession’s ongoing and growing commitment to diversity and inclusion. 2018 CWS President Shili Lin remarked, “I’m so excited and grateful that the long overdue recognitions for women in statistics in the form of two named lectures are finally here, and here to stay!”

    The first lecture will be the F.N. David Lecture. It will be given at JSM 2019 in Denver, Colorado, from July 27 to August 1. ASA Committee on Women in Statistics Chair Kimberly Sellers said, “Already looking forward to JSM 2019!”

    For more information about the lectureships, contact Lin.

    A Commitment to Community Reaches 10-Year-Old at StatFest

    Tue, 05/01/2018 - 7:00am
    Adrian Coles and Reneé Moore

      From left: Nagambal Shah, Erica Dawson, Dawson Batemon, Brian Millen, and Reneé Moore. Millen was the keynote speaker for this year’s event.
      Photo by Jesse Chittams

      Building supportive communities within our broad field helps create pipelines through which talented individuals from all backgrounds can enter into our discipline. One such pipeline is StatFest, which is a one-day conference aimed at encouraging undergraduate students from historically under-represented groups to consider careers and graduate studies in statistics.

      Organizers of StatFest typically endeavor to reach undergraduate and high-school students. However, they discovered their efforts to build community this year extended the pipeline to at least one person who is a bit younger.

      This year’s youngest attendee was 10-year-old Dawson Batemon, who accompanied his mother, Erica Dawson, as she balanced professional and family service.

      Erica is an epidemic intelligence officer at the US Centers for Disease Control and Prevention. She was an invited panel speaker who has learned to use the often-unavoidable overlap between professional and personal life to her advantage.

      She says, “I felt extremely comfortable bringing my son to the workshop. Everyone welcomed and embraced him. StatFest has a sense of community that enables participation from parents, like myself.” Dawson has accompanied her to several events such as this year’s StatFest and been exposed to the same guidance and wisdom used to motivate high-school and undergraduate students.

      Dawson’s favorite subject in school is mathematics, and he uses his mathematical skills as a member of the LEGO Robotics Team at his school. Erica believes her love for mathematics and the support she has received from this community has contributed to his enjoyment of mathematics.

      She says, “He gets a lot of exposure to opportunities beyond high school and undergraduate studies. This normalizes the notion that people from under-represented groups can successfully earn advanced degrees in mathematical sciences.”

      Program Summary

      This year’s StatFest brought 150 students and professionals to Emory University to connect to and learn from graduate students; early career professionals; and established leaders in academia, industry, and government.

      In addition, participants benefited from panel discussions that addressed topics such as careers in statistics and the graduate student experience. Participants also took advantage of structured activities that helped enhance their networking skills.

      Former ASA President Sastry Pantula provided a special presentation that highlighted student opportunities within the ASA, while former ENAR president F. Dubois Bowman provided insight into how to prepare for graduate school admission.

      A special presentation was held in honor of Nagambal Shah, founder of StatFest. She was presented with flowers and a plaque to honor her initiation of this annual event and her continued contributions to the ASA’s Committee on Minorities in Statistics (CMS).

      The chair of this year’s StatFest Planning Committee was Reneé Moore, chair of the ASA’s CMS.

      StatFest 2018 will be held at Amherst College on September 23. Check the Committee on Minorities in Statistics website for more information if you are interested in participating in the next StatFest or the CMS’s other key initiative, the Diversity Mentoring Program.

      Journal of Privacy and Confidentiality Moves to Cornell

      Tue, 05/01/2018 - 7:00am
      Editorial Board Accepting Papers from Multiple Disciplines

        The editorial office of the Journal of Privacy and Confidentiality—a multidisciplinary journal focused on the interface of social, computer, and statistical sciences—has migrated to Cornell University, where it is now managed by Lars Vilhuber at the Labor Dynamics Institute.

        In 2008, Cynthia Dwork of Harvard, Stephen Fienberg of Carnegie Mellon, and Alan Karr of RTI issued a call for papers on privacy and confidentiality to be published in a new journal—the Journal of Privacy and Confidentiality. The novelty of their call was that it was addressed to multiple, usually separate, constituencies. Statisticians, computer scientists, lawyers and social scientists, health researchers, and survey designers have all responded to the call over the years and been published in the journal.

        In the editorial of the first issue, US Census Bureau Chief Scientist John Abowd, Kobbi Nissim of Georgetown University, and Chris Skinner of the London School of Economics noted that “Gargantuan online services gather petabytes of data on search queries, online purchases, email exchanges, […] many data users from all of the fields listed above perform analyses that are conditioned on the privacy and confidentiality protections imposed on their work without all the means to assess the consequences of those measures on the inferences they have made.” Those concerns continue to resonate today.

        For nearly seven years, Fienberg was the editor-in-chief of the journal. With his passing in 2016, the journal needed a new home. Vilhuber has assumed the role of managing editor and migrated the journal infrastructure to a new system (Open Journal System). Dwork, Karr, Nissim, and Abowd continue to serve on the editorial board. The Edmund Ezra Day Chair at Cornell University contributes funding to the journal’s operating budget.

        The journal is open access, and there is no submission fee. Academics and practitioners from all domains are invited to submit their papers.

        Obituaries for May 2018

        Tue, 05/01/2018 - 7:00am
        Mary E. Reuder

        ASA member Mary E. Reuder, born in 1923 in Minneapolis, Minnesota, passed away peacefully November 15, 2017, of natural causes at age 94 near her beloved lake home in Shohola, Pennsylvania. She was preceded in death by her husband, Marvin A. Iverson, a social psychologist.

        Mary completed her undergraduate work at College of St. Catherine (1944), her MA from Brown University (1945), and her PhD from the University of Pennsylvania (1951). In any era, she was remarkably well trained as a statistician, experimental psychologist, and licensed clinical psychologist, but particularly so at a time when women were not as active in academia as they are today.

        She worked for the US Navy as a management specialist and a research psychologist for the Adjunct General’s Office of the US Army before accepting a faculty instructor position at Queens College of the City University of New York in 1954. She rose through the ranks. During her tenure, she held positions as chair of the undergraduate and graduate programs in psychology at Queens College, chaired the Queens College Academic Senate, and was highly active in faculty governance. She retired in 1986 but remained active in the American Psychological Association (APA).

        Mary was the recipient of the William James award for outstanding contributions to psychology from the NY State Psychological Association, received funding from the National Science Foundation, and was involved with Sigma Xi. The APA Florence Denmark and Mary E. Reuder award from Division 52 was created in recognition of her scholarly contributions, international outlook, and outstanding mentoring, particularly of junior faculty and undergraduates. Mary was a member of the APA for more than 60 years as a fellow, president of Divisions 1 and 36, and member of the Council of Representatives. She remained active in the APA until she was in her late 80s.

        Mary seemed to be just around the corner, waiting for everyone to catch up and with an ever-deepening vision of what it takes to become a well-trained, clear-thinking scientist. No one has done so much for so long to assist students, junior faculty, or the profession. Everyone who met her enjoyed her quick wit, to-the-point challenging questions, uncanny ability to navigate the seas of academia, piercing blue eyes, and—of course—those sandals, which she wore all year long. She is remembered for her generosity in both professional and personal relationships. It will be difficult to fill her sandals, but she left a trail of footprints that many of us will follow.

        Early-Career Statistician Offers Language, Tools for Establishing Influence

        Tue, 05/01/2018 - 7:00am
        This column is written for statisticians with master’s degrees and highlights areas of employment that will benefit statisticians at the master’s level. Comments and suggestions should be sent to Megan Murphy, Amstat News managing editor.

        Jessica Lavery is a biostatistician in the Health Outcomes Research Group at Memorial Sloan Kettering Cancer Center in New York. She has an undergraduate degree in statistics from Loyola University Maryland and a master’s degree in biostatistics from The University of North Carolina at Chapel Hill.

        While statisticians are formally taught methodological skills, tactful project management and establishing credibility early on are less often taught, but equally important to successfully contributing to a team. In some settings, such as the medical field where traditional hierarchies drive authority, it may be particularly difficult as an early-career statistician to establish credibility and manage projects with confidence. To build expertise and command respect from the get-go, or to re-work the current dynamic on a project team, several subtle tactics may be used.

        At the Women in Statistics and Data Science (WSDS) conference in October 2017, I shared tools picked up during my first few years as a statistician that have proven useful in being taken seriously by senior collaborators. I share them again with you here.

        Managing Expectations

        At the beginning of collaboration, it is important to establish that the scope of a statistician’s responsibilities often spans beyond producing analyses. The level of involvement by the statistician certainly varies across institutions and may vary across projects and investigators, as well. For this reason, it is most beneficial to establish what your skills are and how you expect to use them at the beginning of each project and on an ongoing basis so your collaborator is aware of how you expect to be involved. Do you expect to be consulted at the beginning of a project, or on an ad hoc basis as the project progresses? Do you anticipate writing part of the manuscript, or to only review the methods and results sections? Do you want to be involved in all project meetings, or only meetings that are statistics oriented? These are examples of details the collaborator should be informed of from the beginning to set the stage for a successful collaborative relationship.

        After you have explained your role, it is important to maintain respect for your expertise throughout the project. Often, intending to be seen as having mastered my role, I’m inclined to say, “Sure, no problem, this task will be easy.” This was something I struggled with in my first job out of graduate school. Of course I can tackle any task you present me with; I just got my master’s degree! Not only does this approach minimize the amount of effort required and results produced, resulting in unrealistic expectations in terms of how long it takes to complete a task, it also conveys the idea that anyone can do my job, devaluing my role on the team. Additionally, if a task comes easily, often that’s only because I went through six years of school to learn how to master that skill.

        Instead of saying something is easy, it is more beneficial to say something along the lines of, “This project will involve x, y and z, and I’m looking forward to getting started on it.” This conveys competence, confidence, and enthusiasm without minimizing your contributions.

        In addition to wanting to seem so skilled at my job that everything was easy, another challenge I encountered was figuring out the right time to send results to an investigator. Naively, my initial approach was to drop what I was doing if it was a quick request and to always send results the moment they were produced. Of course, this sense of urgency would be necessary in the presence of a deadline, but it can lead to setting up unreasonable expectations in the absence of one.

        Like saying something is easy, constantly having a short turnaround time implies your job is quick and effortless and you are constantly on call, ready and waiting to immediately handle all requests. Even investigators with good intentions will learn they can wait until the last minute to ask for something, since it takes such a short time to produce. Instead of immediately replying with results, acknowledging you have received the request and intentionally delaying sending the results for a short time helps manage your collaborators’ expectations and provides buffer time for you to digest the results before disseminating.

        Collaborating and Communicating

        In the day to day, back and forth of collaborating, several subtle language modifications can be helpful in enforcing the idea that you are an integral part of the team.

        As statisticians, we are often taught to speak the same language as our collaborators, and this cannot be emphasized enough. Giving an example or explaining an analysis in the same content area as the project prevents your clinical collaborator from having to translate from an abstract content area to statistics, and from statistics back to their content area. As an example, working in oncology, I would want to avoid explaining a methodology based on agriculture plots, even though this is how I was taught a lot of statistics. My oncologist collaborator is then translating ideas from agriculture to statistics to oncology, opening a lot of opportunity for things to get lost in translation. While this may sound obvious, it is something that serves us well to remember.

        Using the term “we” instead of “you” ingrains you into the research team. Asking, “What question are we trying to answer?” conveys you are equally invested in the research.

        I used to frequently insert “just,” as in, “I’m just emailing to …” or “I just wanted to …” The intention is to be polite and deferential, but what this does is indicate we are in a position of inferiority when we deserve to be an equal collaborator. If I am emailing to follow up with a collaborator who ignored my last email for a week, inserting “just” makes me feel like I am conveying understanding that they are assiduously working and may not have had time to respond. As equal members in the professional field, it is my collaborator’s responsibility to respond to an email, and I should not feel shy about reaching out. I now scan all emails after drafting them and remove unnecessary instances of “just” (which is most of them) and re-read, noting how much clearer the email sounds.

        Upspeak is something I only recently learned about, and I could not stop noticing how frequently I did it once I did. The term upspeak refers to ending what should be a statement as a question. As a simple example, if someone asks when a meeting is and you reply, “The meeting is Monday at noon?” Ending this in a question leaves the group with no more information than before you answered. It is especially important to avoid doing this when sharing results with a group, and to instead speak a sentence as a declaration, portraying more confidence in the answer. It is much easier for a collaborator to be dismissive of an idea or response if it is presented as a question.

        Authorship can be a touchy subject, especially when the project team is large. Additionally, clinical collaborators are sometimes unaware that authorship is important to a statistician’s career. It is your responsibility to establish early on what your expectations are regarding any abstracts, as well as the final manuscript. Instead of presenting this as a question—“Can I be listed as second author?”—it is more effective to present a statement such as, “Generally, when a statistician cleans the data, runs the analyses, and writes the methods and results, they are awarded second author.” This politely establishes a norm and requires justification by your collaborator if there is going to be a shift.

        Collaborating with medical doctors and doctoral-level health services researchers, I often felt conflicted about how to refer to them. Dr. Jones? Ms. or Mrs. Jones? Jane? The trick that comes in handy here is to start formally and then mirror the way someone signs emails. If a collaborator signs off using his or her first name, I take that as an invitation to refer to them as such, connecting on a more personal level and further building a collaborative relationship. Always defaulting back to the more formal name reinforces the idea that you are a subordinate. With that said, if a collaborator indicates a preference for a more formal communication, then it is certainly appropriate to respect that preference.

        Last, but certainly not least, it is important to remember that silence is okay. Often, when I present a result or explain a method and a collaborator is processing what I just told them, I continue nervous-talking—speaking rapidly and quickly trailing off into gibberish as I worry something I explained did not make sense. I often fall back on prematurely asking, “Did that make sense?”, pre-emptively suggesting I was incoherent. Instead, it is more constructive to give the collaborator a few moments to process, and then invite feedback by having the collaborator reiterate their understanding of what you communicated.

        Thoroughly explaining your role and consistently communicating the value of your contributions both explicitly and implicitly increases your influence on the project team. Of course, not all tips will be applicable in all scenarios, but I hope you will find some helpful. This is an area in which I am constantly learning, and I look forward to hearing from others about tactics that have proven useful.

        What Does Wayne Nelson Like to Do When He Is Not Being a Statistician?

        Tue, 05/01/2018 - 7:00am

        Wayne Nelson dances with his tango partner, Cheryl Monti, who he calls an angel.

        Who are you, and what is your statistics position?

        My name is Wayne Nelson. I am a semi-retired private statistical consultant and leading expert on reliability data analysis, recurrent events data analysis, and statistical methods for accelerated testing. I also give training courses for clients and professional societies. An employee of General Electric Corporation Research and Development for 24 years, I consulted across the company. As an adjunct professor at Union College and Rensselaer Polytechnic Institute, I taught graduate courses on the theory and application of statistics.

        Tell us about what you like to do for fun when you are not being a statistician.

        When I was 12, my grade school gave me ballroom dance lessons with girls. Now 81, I’m still dancing with them—but, today, it’s Argentine tango, which is a three-minute romance. Seriously, dancing social ballroom at age 60, I discovered Argentine tango, became addicted, and now need a “tango fix” two or three times a week.

        What drew you to this hobby, and what keeps you interested?

        Argentine tango has various charms. Few in number, tangueros are friendly and welcoming to all dancers. I’ve been warmly welcomed in dances all over the US and abroad, including Buenos Aires, Cairo, Mexico City, Bordeaux, and embargoed Havana (I went there as a wetback).

        Used to dancing chest-to-chest (heart to heart) and cheek-to-cheek, tangueros warmly hug friends on greeting. No other dance has such intimate contact—chest, head, feet, calves, and, yes, thighs.

        The women dance only on the balls of their feet and have gorgeous legs. It is the world’s most difficult social dance, an enticing challenge that requires years to master. I’ve been working on tango for 20 years. Still humbly learning.

        Tango music is romantic, beautiful, and expressive of feelings. Good social dancers express the feeling of the music using suitable “figuras” (dance patterns) and rhythms; that is, they spontaneously choreograph. Such musicality is rare in social ballroom dancing, which uses a simple repeating rhythm for each dance style. Hear the beautiful tango “Invierno” [Winter] and see charming professional choreography on YouTube.

        The best dancers have outstanding technique that feels wonderful to partners. Ballroom partners are performer wannabes and try to look good. Tangueros try to feel good to partners. My partners have ranged from clumsy sumo wrestlers to butterfly angels who are lighter and follow me better than my shadow. I always fall in love with the angels. A tango with an angel is three minutes in heaven. Such a tango dance is described in Buenos Aires as “one heart with four legs.

        Now 81 and an advanced dancer, I am flattered when asked to dance by gorgeous young 60-year-olds I don’t know. At a tango dance in the Catskills, Marilyn—a most attractive and skilled tanguera—invited me to dance with her in New York City. We’ve danced in Central Park, in the pavilion at the end of Pier 45 as the sun sets in New Jersey, in the UN Building, and in many tango clubs and dance halls. Tango brought me this much-treasured friend.

        Some special tango moments for me include:

      • Anne, my beloved dance partner and sweetheart of 27 years, took me on a tango cruise. It departed from Venice and stopped at various Greek ports (including Rhodes and the ancient Olympics site) and beautiful medieval Dubrovnik. Our group of 25 tangueros had classes every morning, an adventure ashore in a new place each afternoon, and a private tango dance at night. Our eight teachers put on a first-rate tango show in the ship’s theater; it attracted 300 passengers just by word of mouth. The trip included memorable stays in Venice and Florence.

      • As a raw beginner, I went to tango boot camp in Buenos Aires for a week in 1997. In a class of 25 beginners, I was taught a figura by a maestro (master teacher) each afternoon, followed by a practice with five or six attentive teachers who drilled me on technique involving balance and delicate connection with partner that does not disturb the partner’s balance and movement. Each evening, we went to a different dance club, struggled on a crowded floor, and also saw a tango show. Still needing boot camp, I repeated it the following year. Boot camp showed me what technique I needed to learn to dance well with a partner. I am still working on improving technique.

      • Buenos Aires is the Mecca for tangueros. Self-employed and having a good boss, I have spent five weeks there in March and April each year since 2000. A high point was my Fulbright Award to teach reliability statistics in Spanish to engineers there for three months. Of course, you know why I chose Buenos Aires and what I did in my free time.

      • Recently, for the first time, I attended the Stowe Tango Music Festival, where scores (no pun) of musicians improved their skills, instructed by maestros. For four days, 100+ tangueros participated in dance classes and danced to a live orchestra of 23 musicians who raised the hair on the back of my neck.

      • There are customs at tango dances. You do not approach a tanguera and ask her to dance. In ballroom dancing, just asking is customary. To invite a tanguera, you must catch her eye (sometimes across the dance floor) and smile and nod. If she smiles back, you go to her and dance. If she ignores you, you’re out of luck. The tangos are played in a tanda, a set of three or four tunes of the same style and by the same orchestra. On hearing the first notes of the music, you can invite a partner suited to the music and dance that tanda with her. Some tandas have other styles of music such as swing, Latin, polka, paso doble, etc. In Argentina, they do the chacarera folk dance. In Mexico, they dance traditional danzón, which is like rumba but more complicated. Most music at dances is by the famous orchestras of the Golden Age of Tango—the 30s, 40s, and 50s—and some is by more recent orchestras. We dance to three styles of Argentine music:
        • Traditional tango with a 2/4 or 4/4 (march) tempo
        • Waltz tango with 3/4 time (a three-beat measure), which is like a Viennese waltz but with a faster tempo
        • Milonga with a 2/4 or 4/4 (march) tempo, which is faster than traditional tango

        These dance styles have a common base, and each has some unique steps and customs. There are other styles of tango. In the US, ballroom dancers dance “American tango,” which is much like fox trot danced to music with a heavy drum beat, for example, “Hernando’s Hideaway.” International tango is a studio-invented competition style with exaggerated stylized movement, such as head snapping, and the men wear tails and the women wear long ball gowns. In addition to the social Argentine tango, there is professional stage tango, called fantasy tango. It is athletic and complicated with high speed and lifts.

        JSM for Newbies

        Tue, 05/01/2018 - 7:00am

        Christopher Bilder is a professor in the department of statistics at the University of Nebraska-Lincoln and a fellow of the ASA.

        The largest congregation of statisticians in the world happens every August during the Joint Statistical Meetings (JSM). More than 6,000 people attend these meetings, which are sponsored by 11 statistical societies, including the American Statistical Association. The meetings offer a variety of activities such as attending research presentations, interviewing for jobs, taking professional development courses and workshops, and browsing the exhibit hall. With so many opportunities, new attendees can be overwhelmed easily by their first JSM experience.

        Based on my familiarity with attending meetings over the last 18 years and the experiences of student groups I have led, I’m going to provide some tips on how to get the most out of JSM. If you would like to share your own recommendations, I encourage you to submit a comment below.

        Important Links
        JSM 2018

        Online Program

        Job Seekers

        Professional Development

        Student Opportunities Before JSM

        Most new attendees who choose to present their research do so in a contributed session via an oral or poster presentation. The deadline to submit an abstract for acceptance into the program was in early February. For those who did this, additional proof of progress (e.g., drafts of a paper) for the presentation must be submitted by mid-May.

        A preliminary program listing the presentation schedule is now available. Because there may be more than 40 concurrent presentations at any time, it is best to arrive at JSM with an idea of which to attend. This can be done by examining the session titles and performing keyword searches in the online program prior to JSM.

        Oral presentations are separated into invited, topic-contributed, and contributed sessions, with each session lasting 1 hour and 50 minutes. Invited and topic-contributed sessions include groups of related presentations that were submitted together and selected by JSM Program Committee members. These presentations each last for 25 or more minutes for invited and 20 minutes for topic-contributed. Contributed sessions include groups of 15-minute oral presentations. Unlike invited and topic-contributed sessions, contributed presentations are submitted individually and then grouped by JSM Program Committee members.

        Poster presentations are also separated into invited, topic-contributed, and contributed sessions, with the vast majority in contributed sessions. These types of presentations involve speakers being available for questions next to their displayed poster during the entire session. Most posters are of the traditional paper format. An increasing number now are in an electronic format paired with a short four-minute oral presentation. For this combination of presentation types, the oral portion is given first in what is known as a “speed” session. A few hours later, the corresponding electronic poster presentation takes place.

        Online registration for JSM begins around May 1. For members of a sponsoring statistical society, the cost is $455 during the early registration period. The cost increases to $555 if you register at JSM.

        Registration for student members is only $120, and this rate is available at any time. Also starting around May 1, you can reserve a hotel room through the JSM website. A number of hotels near the convention center are designated as official conference hotels, and they discount their normal rates. However, even with a discount, you can expect to pay $200 or more per night for a room.

        Attending JSM can be expensive. Students have several options to reduce the cost burden. First, ask your adviser or department for funding. Many departments offer financial support for students who present their research at JSM. Students also may qualify for funding from the student activities office on their campus. For example, when I was a student, my department’s statistics club received funding this way, which paid for most of my first JSM expenses.

        In addition to school-based resources, many ASA sections sponsor student paper competitions that provide travel support to award winners. For example, the Biometrics Section of the ASA sponsors the David P. Byar Young Investigators Award, with $2,000 awarded to the winner and separate $1,000 awards given to authors of other outstanding papers. Most competitions require a completed paper to be submitted many months prior to JSM.

        At JSM

        JSM begins on a Sunday afternoon in late July. Business casual clothing is the most prevalent attire, but some attendees wear suits and others wear T-shirts and shorts. When you arrive at JSM, go to the registration counter at the convention center to obtain your name badge (if not already mailed to you) and additional conference materials.

        There is a significant online presence during JSM. A main resource is the JSM app and online program. Both contain all the information you will need, including a convention center map. Also, the ASA posts the most up-to-date news about JSM through its Twitter (@AmstatNews) and Facebook accounts. Attendees at JSM can use #JSM2018 to tag their JSM-related posts.

        To welcome and orient new attendees, the JSM First-Time Attendee Orientation and Reception is scheduled for early Sunday afternoon. At this reception, docents will be present (identified with a special orange button by their name badge) to answer any questions you may have about the meetings. These docents will be available throughout the conference as well.

        Later on Sunday evening, the Opening Mixer will be held in the exhibit hall. This event is open to all attendees, and drinks and hors d’oeuvres will be served.

        In between the orientation and the mixer, the ASA Awards Celebration and Editor Appreciation session is held. Many first-time attendees are honored during it due to being awarded a scholarship or winning a student paper competition.

        The main sessions start Sunday at 2:00 p.m. Many of the research presentations are difficult to understand completely. My goal for a session is to have 1–2 presentations in which I learn something relevant to my teaching or research interests. This may seem rather low, but these items add up after attending many sessions.

        For attendees who teach introductory courses, the sessions sponsored by the ASA Section on Statistical Education are often the easiest to understand. Many share innovative ideas about how to teach particular topics.

        Introductory overview lectures are another type of session that has easier-to-understand topics. Recent lectures have included introductions to variable selection, statistical learning, and quantile regression. There are also many Professional Development courses and workshops available for an additional fee. However, you can attend a course for free by volunteering prior to JSM to be a monitor. Monitors perform duties such as distributing and picking up materials during the course. As an added benefit, monitors can attend one additional course for free without any duties. Those who are interested should contact Rick Peterson.

        Featured talks at JSM are usually scheduled for late afternoon on Monday through Wednesday. On Tuesday evening, the ASA president’s address is given, along with an introduction to the new ASA fellows and winners of the Founders Award. The fellow’s introduction is especially interesting because approximately 60 ASA members (<0.33% of all members) are recognized for their contributions to the statistics profession.

        In addition to presentations, the JSM exhibit hall features more than 90 companies and organizations exhibiting their products and services. Many exhibitors give away free items (e.g., candy, pens, etc.). All the major statistics textbook publishers and software companies are there. Textbook publishers usually offer a discount on their books during JSM and often for a short time after. The exhibit hall also includes electronic charging stations and tables that can be used for meetings. Additionally, it’s the location for the poster presentations.

        The JSM Career Service provides a way for job seekers and employers to meet. Pre-registration is required, and the fee is discounted if you register before mid-July. The service works by providing an online message center for job seekers and employers to indicate their interest in each other. Once a common interest is established, an interview can be arranged for during the meetings.

        Other activities at JSM include the following:

        • Shopping at the ASA Store to purchase a statistics-themed T-shirt or mug
        • Attending an organized roundtable discussion during breakfast or lunch about a topic of interest (pre-registration is required)
        • Taking a little time off from JSM for sightseeing or attending a sporting event
        After JSM

        JSM ends in the early afternoon on Thursday. Don’t let what happens at JSM stay at JSM! The first thing I do after the meetings is to prepare a short review of my activities. Using notes I took during sessions, I summarize items from presentations I want to examine further. I also summarize meetings I had with individuals about research or other important topics. Much of this review process starts at the airport while waiting for my return flight.

        If you give a presentation at JSM, you may submit a corresponding paper to be published in the conference proceedings. Papers are not peer-reviewed in the same manner as for journals, but authors are encouraged to have others examine their paper before submission. The proceedings are published online around December. Authors retain the right to publish their research later in a peer-reviewed journal.

        Special Issue Looks at Statistics Behind Defense and National Security

        Tue, 05/01/2018 - 7:00am
        Scott Evans, CHANCE Magazine Executive Editor

          Threats to national security come in many forms. In 2016, Russians hacked the United States election. On September 11, 2001, 19 militants associated with the extremist group al-Qaeda hijacked four airplanes, killing nearly 3,000 people. In the 1990s, President Bill Clinton was convinced the global spread of AIDS was reaching catastrophic dimensions and formally designated HIV a threat to United States national security since it could threaten the stability of foreign governments, touch off ethnic wars, and undo recent advances in building free-market democracies abroad.

          Defense and national security is the theme of CHANCE 31(2), a special issue. Six articles discuss aspects of national security and how statistics is playing a key role in addressing various issues. David Banks and Alyson Wilson served as guest editors for this issue.

          In the first article, Laura Freeman and Catherine Warner discuss implementing statistical design and analysis in the evaluation of the Department of Defense (DoD) operational systems in “Informing the Warfighter—Why Statistical Testing Methods Matter in Defense Testing.” Ron Fricker and Steven Rigdon then discuss surveillance methods applied to detecting and tracking deadly diseases such as influenza (swine flu or bird flu), Ebola, Zika, or SARS. Banks discusses how adversarial risk analysis, a modeling strategy that incorporates an opponent’s reasoning, can be applied to a range of problems in counterterrorism. Douglas Ray and Paul Roediger then discuss adaptive testing of DoD systems with a binary response. The evolution of statistical modeling of military recruiting is the topic of an article by Samuel Buttrey, Lyn Whitaker, and Jonathan Alt. Susan Sanchez discusses the use of data farming, using tools and techniques for the design and analysis of large simulation experiments, as applied to defense problems.

          In an independent article, Beverly Wood, Megan Mocko, Michelle Everson, Nick Horton, and Paul Velleman evaluate clarifications and updates to the six recommendations for teaching from the original, foundational GAISE College Report. They consider evolutions affecting the teaching and practice of statistics, including the rise of data science, an increase in the number of students studying statistics, increasing availability of data, and advances in science and technology. They discuss how the original recommendations can be clarified by acknowledging these developments.

          In the Odds of Justice column, Mary Gray evaluates the death penalty and the role statistics is playing and can play in evaluating its appropriateness. In Visual Revelations, Howard Wainer and Michael Friendly take a historical look at visualization and the profound impact visual communication has had, going back to ancient civilizations.

          All ASA members have access to CHANCE online by logging in to members only and clicking on ASA publications.

          Applications Being Accepted for Diversity Mentoring Program at JSM

          Tue, 05/01/2018 - 7:00am

          2017 Diversity Mentoring Program participants and mentors

            Applications are being accepted for the JSM Diversity Mentoring Program, one of the initiatives of the American Statistical Association’s Committee on Minorities in Statistics.

            The program is designed to promote the statistics profession among under-represented minority populations in the US (African-American, Hispanic/Latino, Native American). Graduate students, postdoctoral scholars, and early-career professionals are brought together with senior-level statisticians/biostatisticians and faculty in academia, government, and the private sector in a structured program during the 2018 Joint Statistical Meetings.

            This multi-day program (Sunday, July 29, through Wednesday, August 1) provides career information, mentoring, and networking activities. Program activities include small-group discussions and one-on-one meetings between mentor-mentee pairs.

            Visit the committee website for the mentee application. Preference will be given to applications received by May 31, 2018. For more information, contact Dionne Swift.

            Arizona Chapter Members Participate in DataFest

            Tue, 05/01/2018 - 7:00am

            From left: Arizona Chapter officers Jie (Jane) Pu, Yongzhao Peng, Shuo Jiang, and Rodney Jee; district vice chair Ji-Hyun Lee; and Jennifer Broatch of ASU-West Campus

            The Arizona Chapter concluded its first ASA DataFest competition on March 25 with excellent participation from students of Arizona State University’s Tempe and West campuses. A total of 15 teams comprised of 49 students finished the weekend-long competition.

            Referred to as a data hackathon, ASA DataFest challenges undergraduates to analyze a large data set from industry over a weekend and present their results before judges.

            Many of the students began learning R for the competition, but at least one of the teams relied heavily on their training in SAS for their data preparation and analyses. Visualization was one of the judging criteria, yet teams were seen learning to use a commercial package like Tableau on the spot for their analyses and presentations. A few teams ventured into building quick models, ranging from logistic regressions, to time series, to machine learning. Clearly, many of the students took up the challenges and used the competition as a way to strengthen their technical skills while conducting data analyses.

            More than 30 mentors signed up to help the students overcome DataFest’s challenges. Most of the mentors were graduate students and faculty at ASU, but members of the Arizona Chapter saw many data professionals from local business and industry participating, too.

            An attempt was made to find mentors who could help with specific languages or software tools and such effort was not wasted. Python and Tableau experts were announced upon arrival and immediately found teams seeking assistance in applying those tools to their analyses.

            The competition finished Sunday afternoon with student presentations of their key findings. The ASU site was fortunate to have a representative of the data donor among the panel of five judges. A senior analyst from the health care sector provided another perspective on judging. ASA members Steve Robertson of Southern Methodist University, John Stufken of ASU, and Ji-Hyun Lee of The University of New Mexico rounded out the panel. Lee, who is also the current Council of Chapters district vice chair, not only judged, but also was present for the entire event. Besides working with the students, she took time to talk with members about the needs and opportunities for leadership roles in the chapters and ASA.

            North Carolina Chapter News for May 2018

            Tue, 05/01/2018 - 7:00am

            The North Carolina Chapter offered the ASA’s statistical leadership course to its members in March. Gary Sullivan from the ASA’s ad-hoc leadership committee came to the Research Triangle Park area to lead a course that included personal reflection, group discussions, and targeted exercises to develop a greater awareness of leadership.

            Local leaders involved in the courses included David Banks of Duke University and Abie Ekangaki of biopharmaceutical company UCB. Banks spoke of his personal leadership development, from working in his role in government to his current position as director of the Statistical and Applied Mathematical Sciences Institute. Ekangaki explained the differences between leadership and leaders and the implications of those differences inside an organization’s structure.

            What Does Claire Kelling Like to Do When She Is Not Being a Statistician?

            Sun, 04/01/2018 - 7:00am
            This column focuses on what statisticians do when they are not being statisticians. If you would like to share your pastime with readers, please email Megan Murphy, Amstat News managing editor.

            Claire Kelling began beading when she was 10 years old.

            Who are you, and what is your statistics position?

            I am a dual PhD candidate in statistics and social data analytics at Penn State.

            Tell us about what you like to do for fun when you are not being a statistician.

            When I am not being a statistician, I like to weave! I mostly create beaded patterns for family members as Christmas and birthday gifts. My most recent patterns have used more than 10,000 beads per design!

            Claire Kelling’s largest project thus far, a gift for her mother, contains 10,712 beads.

            What drew you to this hobby, and what keeps you interested?

            I have been beading since I was quite young, starting when I was about 10 years old, I think. I never enjoyed or was particularly good at the typical artistic activities, like drawing or painting. I am a triplet, and my brother and sister were both quite talented artistically. Having hobbies was actively encouraged in my family, as we lived on 56 acres with little to no TV, internet, or games. Therefore, I took up this hobby as a way to express myself artistically. It started as weaving potholders and turned into weaving pretty elaborate designs on a bead loom!

            Claire Kelling completed her first major beading project when she was in about the fourth grade and entered it into the 4-H fair. It won fourth place.

            My next big step for weaving will likely involve a large fabric loom (about 5 x 5 x 5 ft). I enjoy this hobby because, as a statistician perhaps, I am drawn to patterns, and beading involves patterns quite obviously in the designs, but also in the execution of the craft. It is very methodical and careful, much like statistics.

            I also enjoy creating something tangible through my hobby. My beadworks are almost always gifts, and I think a handmade gift is an excellent way to show your appreciation for someone. I think the fact that I have put 10,000+ beads individually onto a needle to weave into a design shows a lot about how much I care about someone!

            BASS XXV Scheduled for Fall

            Sun, 04/01/2018 - 7:00am

            The 25th Biopharmaceutical Applied Statistics Symposium (BASS XXV) will be held October 15–19 at the Hotel Indigo Savannah Historic District in Savannah, Georgia. One-hour tutorials on diverse topics pertinent to the research, clinical development, and regulation of pharmaceuticals will be presented by speakers from academia, the pharmaceutical industry, and the US Food and Drug Administration. Two parallel, two-day short courses will be presented October 17–19. BASS will also offer a poster session.

            BASS is a nonprofit entity established to support graduate studies in biostatistics. For further information, contact the BASS registrar or BASS chair Tony Segreti.

            Master’s and Doctoral Programs in Data Science and Analytics

            Sun, 04/01/2018 - 7:00am
            Steve Pierson, ASA Director of Science Policy
              More and more universities are starting master’s and doctoral programs in data science and analytics—of which statistics is foundational—due to the increasing interest from students and employers. Amstat News reached out to those in the statistical community who are involved in such programs to find out more about them. Given their interdisciplinary nature, we identified programs involving faculty with expertise in different disciplines to jointly reply to our questions. We have profiled many universities in our April, June, and December 2017 issues and January 2018 issue; here are several more. University of Central Florida Liqiang Ni is associate professor of statistics in the department of statistics at the University of Central Florida. His research interests include dimension reduction, multivariate analysis, actuarial science, and business intelligence. He has served as the graduate coordinator since August 2017.

              Shunpu Zhang is a professor of statistics and chair of the department of statistics at the University of Central Florida. His research interests include bioinformatics, functional estimation, health informatics, large-scale hypothesis testing, and big data analytics. MS in Statistical Computing—Data Mining Track

              Year in which first students graduated/are expected to graduate: 2002
              Number of students currently enrolled: 60
              Program format: In-person, 36 credits, comprehensive exam required, either thesis or project-based, full time and part time, graduate assistantship offer on competitive basis

              There are largely two components for required courses in the curriculum. The first tilts to traditional statistics, including a two-semester course for theoretical statistics and one-semester course for regression analysis and logistic regression/GLM, respectively. The second component tilts to applications: two-semester course for data processing and preparation, including coding, and two-semester course for data mining. There is a variety of elective courses students can choose from.

              What was your primary motivation(s) for developing a master’s (or doctoral) data science/analytics program? What’s been the reaction from students so far?
              In the late 1990s, the statistics community began to realize the great potential in data mining and data science. UCF created one of the earliest data mining programs, in part inspired by SAS Company and with support from Disney, Florida Hospital, Blue Cross Blue Shield of Florida, Universal Studios, and many local business partners.

              The students responded with a good deal of enthusiasm. We have seen a growing need for an educated and talented workforce at the MS level and beyond that can contribute to industry, government, and academia through innovative applications of data analysis methodologies.

              How do you view the relationship between statistics and data science/analytics?
              We believe statistical science is an integral part of data science/analytics. A good data scientist/data analyst must have adequate training in statistics.

              What types of jobs are you preparing your graduates for?
              We are preparing MS graduates largely for industries. Every year, a few graduates continue their studies in PhD programs.

              What advice do you have for students considering a data science/analytics degree?
              We suggest students have a solid foundation in computer programming, mathematics, and statistics to be a good data analyst. They also should have a keen interest in the new developments in data science.

              Describe the employer demand for your graduates/students.
              Demand for our graduates has always exceeded the supply, especially in recent years.

              Do you have any advice for institutions considering the establishment of such a degree?
              We believe a data analytics/data science graduate program resides best in a statistics department with concentrations in computer programming and software development. Open-mindedness is the key to a successful interdisciplinary program.

              University of Michigan Michael Elliott is a professor of biostatistics and research professor of survey methodology. His research interests include survey methods, causal inference, missing data, and longitudinal data analysis with applications to social epidemiology, cancer trials, women’s health, pediatrics, and injury.

              H. V. Jagadish is Bernard A. Galler Collegiate Professor of Electrical Engineering and Computer Science. His research has spanned many aspects of big data, including data usability when they come from multiple heterogeneous sources, and has undergone many manipulations.

              XuanLong Nguyen is associate professor and director of master’s programs in statistics. His research interests include Bayesian nonparametrics, hierarchical models, and machine learning.

              Elizabeth Yakel is associate dean for academic affairs and professor in the school of information. Her research focuses on data reuse, teaching with primary sources, and the development of standardized metrics to enhance repository processes and the user experience.

              Ji Zhu is a professor and director of the data science master’s program in statistics. His research interests include statistical learning; network analysis; and statistical modeling in finance, marketing, and biosciences. Data Science Master’s Program

              Year in which first students graduated/are expected to graduate: 2019–2020
              Partnering departments: Biostatistics, Electrical Engineering and Computer Science, School of Information, Statistics (administrative unit)
              Program format: Full time, on campus; requires at least 25 credit hours in core areas including databases, data and web applications, regression, and statistical learning

              The program requires the students to have demonstrated competence in a basic computing sequence and a basic statistics sequence. By taking graduate-level courses, the students need to demonstrate expertise in data management and manipulations, as well as statistical techniques relevant to data science. The students need to take at least one advanced elective from each of the following buckets: principle of data science; data analysis; and data science computation. The students will also have an integrative capstone experience through an approved project.

              Students with an undergraduate degree in data science would already have obtained a reasonable level of training toward the core skills and may finish the master’s degree in one year. Students with an undergraduate degree in mathematics or physics, statistics or biostatistics, computer science, and other quantitative disciplines should be able to complete all requirements within two years.

              What was your primary motivation(s) for developing a master’s (or doctoral) data science/analytics program? What’s been the reaction from students so far?
              The data science explosion is fueled organically by new data generated from diverse sources, devices, web services, mobile communication, scientific studies, and social media. Data scientists require a versatile and unique set of skills to manage, process, and extract data from these complex information streams, and then interrogate, analyze, visualize, and interpret the information. Nationally, there is a pressing need for data scientists, and, in fact, for people with every level of data science training. The successful launch of our data science major, which has attracted almost 200 students across campus in its first two years, made it clear that our students want to be part of data science. The collaborative approach we take across departments and colleges enables us to pool resources and offer the best our university has for a truly cross-cutting program.

              How do you view the relationship between statistics and data science/analytics?
              Statistics is undoubtedly a major part of data science. The advancement of statistics has always been driven by new data that arise in science or society, whether they are from agriculture measurements or the industrial revolution or the internet. While data science requires tools from multiple disciplines (e.g., mathematics, computer science) and must work with specific domains of applications (e.g., business or health care analytics), statistics and data science are inseparable. From design of experiments to probabilistic modeling, from data exploration to confirmatory testing, and from estimation to prediction, statistics has been the core to data analysis. Statistics without data science will not thrive, and data science without statistics is certainly unsound.

              What types of jobs are you preparing your graduates for?
              This is a new program, but we provide the training the students need to work as data scientists in a wide range of industries, from financial services to health care, from marketing to social networking. We invite companies to our career fair for the students, and we encourage students to take internships to help them understand what they need to prepare for in school.

              What advice do you have for students considering a data science/analytics degree?
              We offer two master’s degrees, one in applied statistics and the other in data science. At the present time, the applied statistics degree focuses more on modeling and inference, and the data science degree focuses more on data handling and data mining. Some of the students from the applied statistics degree pursue a doctoral degree in statistics, biostatistics, economics, and other quantitative fields. We expect the data science students to be versed in data management and programming. However, there is an increasing overlap between the two programs, as we offer more computing courses to applied statistics students and more statistics courses to data science students.

              Describe the employer demand for your graduates/students.
              We do not have data on our graduates from the data science program, but the vast majority of our graduates from the applied statistics program was employed or went to PhD programs within six months of their graduation.

              Do you have any advice for institutions considering the establishment of such a degree?
              Data science programs by nature cross traditional boundaries, but the department of statistics is a natural and ideal home for such programs. To make such programs successful, the statistics departments must be willing to modernize their existing curriculum to embrace data science and reach out to work with the faculty from other programs. At Michigan, different programs offer complementary courses in data science and, together, we believe we can attract and accommodate students from diverse backgrounds.

              The Johns Hopkins University James Spall has four appointments at The Johns Hopkins University: principal professional staff at JHU/APL; chair of the applied and computational math program; co-chair of the data science program; and research professor in the department of applied math and statistics. Spall has published extensively in the fields of control systems and statistics. Master of Science in Data Science and Post-Master’s Certificate (PMC) in Data Science

              Year in which first students graduated/are expected to graduate: Late 2018
              Number of students currently enrolled: More than 120 fully matriculated students in the MS degree and 0 students in the PMC program. There are additional students who have been given a provisional admission status (additional evaluation and/or coursework required) for both the MS and PMC.
              Partnering departments: Applied and computational mathematics and computer science
              Student type: Nontraditional/part time/continuing education, although there are a few students pursuing the degree full time
              Program format: Online/in-person/combination; 30 credit hours required in five years for the MS; 18 credit hours required in three years for the PMC

              The program is a combination of selected offerings in two existing rigorous graduate degree programs in applied and computational mathematics (ACM) and computer science (CS). On the ACM side, students will take a foundational course in statistical methods and data analysis, followed by required courses in optimization, statistical models and regression, and computational statistics. On the CS side, students will take a foundational course in algorithms, followed by required courses in databases, visualization, and data science. All students are also required to take one upper-level ACM elective (e.g., data mining, queuing theory, or stochastic optimization) and one upper-level CS elective (e.g., machine learning or big data processing using Hadoop). Qualified students will need to have taken three semesters of calculus (through multivariate), discrete mathematics, Java, and data structures.

              What was your primary motivation(s) for developing a master’s (or doctoral) data science/analytics program? What’s been the reaction from students so far?
              The motivation for starting the program is clear to anybody even slightly paying attention to broad trends in society toward greater quantitative analysis in decision-making and the need for processing and interpreting massive data sets in many diverse fields. JHU had a well-received non-credit sequence in data science through Coursera and the school of public health for several years, and the need for a graduate credit program was fairly clear. In response, the JHU Whiting School of Engineering, through its engineering for professionals division, took on the challenge of creating a rigorous, credit data science program based in both applied math and computer science. Relative to the number of applicants, the data science program has had an overwhelming response since the program was rolled out in fall 2016. The cumulative number of applications grew from 0 to more than 2,000 in less than two years.

              How do you view the relationship between statistics and data science/analytics?
              While there is a wide variety of data science programs, all seem to have a substantial basis in statistics. That connection is not surprising when you consider statistics is defined as the field devoted to “the practice or science of collecting and analyzing data”!

              While we will not proclaim to know “the” relationship between statistics and data science, the JHU program in data science is deeply connected to advanced methods in mathematical statistics, modeling, and computational statistics. As such, the prerequisites for the data science program involve mathematics through multivariate calculus (Calculus III), as well as a course in discrete mathematics and exposure to linear algebra and matrix theory.

              What advice do you have for students considering a data science/analytics degree?
              A prospective student needs to be strong in math and adept at programming. Someone considering the program who has not taken mathematics or programming courses in several years prior to starting the program might consider taking a refresher to “hit the ground running.” Also, for the key demographic of students who are working full time or near full time, it is recommended that students initially take only one course at a time. This allows a person to re-acclimate to academic life.

              What types of jobs are you preparing your graduates for? Describe the employer demand for your graduates/students.
              The range of jobs associated with data science, broadly defined, is almost limitless. It seems many large and small employers have people doing data science in some capacity, but without having that label in the job title. Given that most of our students are part time and are partially or fully employer funded, the students are expected to continue with their current employer. For the minority of students not employer funded, we currently have little data regarding employer demand because the program is a new offering. That being said, given the strong demand for the program, there is little doubt that those students in the job market will be able to find relevant positions.

              University of Vermont James P. Bagrow is an assistant professor in mathematics and statistics at the University of Vermont and a member of the Vermont Complex Systems Center. He has degrees in liberal arts (AS) and physics (BS, MS, and PhD).

              Jeffrey S. Buzas is professor and chair of mathematics and statistics and director of the statistics program. He has degrees in mathematics (BS) and statistics (MS and PhD).

              Margaret J. Eppstein is professor and chair of computer science at the University of Vermont and the founding director of the Vermont Complex Systems Center. She has a BS in zoology, MS in computer science, and PhD in environmental engineering.

              Peter Sheridan Dodds is a professor in mathematics and statistics at the University of Vermont, where he is also the director of the Vermont Complex Systems Center and co-director of the Computational Story Lab. PhD in Complex Systems and Data Science

              Year in which first students graduated/are expected to graduate: 2021
              Partnering departments: Vermont Complex Systems Center (lead), Mathematics and Statistics, Computer Science
              Program format: In-person (online being developed), thesis/project or coursework, 30 credit hours, traditional/non-traditional/full-time/part-time/continuing education

              We provide students with broad training in computational and theoretical techniques for describing and understanding complex natural and sociotechnical systems, enabling them to then—as possible—predict, control, manage, and create such systems.

              Our PhD is a natural addition to our educational platform, which already consists of an MS in complex systems and data science and a five-course graduate certificate in complex systems. UVM also now has an undergraduate major in data science.

              The major skill sets we aim to train include the following:

              • Data wrangling: Methods of data acquisition, storage, manipulation, and curation
              • Visualization techniques, with potential for building high-quality web-based applications
              • Uncovering complex patterns and correlations in systems through data-fueled machine learning and genetic programming
              • Powerful ways of identifying and extracting explanatory, mechanistic stories underlying complex systems—not just how to use black box techniques

              Students must have prior coursework or be able to establish competency in the following:

              • Calculus
              • Coding (Python/R ideal, but not necessary)
              • Data structures
              • Linear algebra
              • Probability and statistics

              What was your primary motivation(s) for developing a master’s (or doctoral) data science/analytics program? What’s been the reaction from students so far?
              The basic motivation was that we live in a renaissance time with so many fields moving from data-scarce to data-rich. Students need a suite of skills to be able to contend with the kinds of broad problem solving they will face in the real world, very likely as parts of teams. These students should not be cogs with narrow training. Student response has been extremely positive.

              How do you view the relationship between statistics and data science/analytics?
              Our PhD and master’s incorporate training in computer science, statistics, mathematics, physics (mechanisms), and complex systems.

              What types of jobs are you preparing your grads for? (If you have had graduates, please summarize the types of jobs they took and in what sector.)
              Data science positions at corporations and in governments positions. Students with training that will be formally framed by our PhD have gone on to work for companies, as well as into careers in education.

              What advice do you have for students considering a data science/analytics degree?
              Students should look for data science programs that are truly interdisciplinary. They should be able to develop skills that enable them to explain patterns, and not just reproduce them or generate novel ones. While explanation is fundamental to science, it is also crucial in real-world venues to be able to understand and defend, for example, decisions proffered by algorithms for maintenance of ethical, legal, and assurance standards.

              Describe the employer demand for your grads/students.
              Very strong. We have increasingly received interest in PhD students with a deeper training.

              Do you have any advice for institutions considering the establishment of such a degree?
              Just do it. The world has changed, and it is our responsibility to adapt. We have to frame education so students will have a clear path to becoming data scientists. Many essential courses will already exist, but the development of hybrid core courses on data science will likely also be necessary.

              Section on Physical and Engineering Sciences News for April 2018

              Sun, 04/01/2018 - 7:00am
              Joanne Wendelberger, Joint Research Conference Chair

                Make your plans now to head to Santa Fe, New Mexico, for the 2018 Joint Research Conference (JRC) on Statistics in Industry and Technology, which will be hosted by the Los Alamos National Laboratory at the Drury Plaza Hotel June 11–14. JRC2018 is a joint meeting of the SPES/Institute of Mathematical Statistics Spring Research Conference on Statistics in Industry and Technology and the Quality and Productivity Section’s Quality and Productivity Research Conference.

                A short course titled “Bridging Statistics and Data Science” will be taught by Ming Li from Amazon and Hui Lin from DowDuPont. Conference activities include a tour and reception at the Los Alamos National Laboratory Bradbury Science Museum. There will also be an opportunity to experience the Meow Wolf interactive exploration of art and technology.

                The conference program committee, co-chaired by Xinwei Deng and Brian Weaver, has arranged a stellar lineup of invited sessions. Plenary speakers include this year’s conference honoree, Max Morris from Iowa State University; Scott Vander Wiel from Los Alamos National Laboratory; and Derek Bingham from Simon Fraser University. Special invited luncheon speakers include ASA President-elect Karen Kafadar of the University of Virginia, who will give a talk titled “The Critical Role of Statistics in Development and Validation of Forensic Methods,” and Francesca Samse of The University of Texas at Austin, who will discuss work color perception and scientific visualization.

                Invited Sessions

                The Technometrics invited session will feature Mickael Binois with “Replication or Exploration? Sequential Design for Stochastic Simulation Experiments,” Joseph Guinness with “Permutation and Grouping Methods for Sharpening Gaussian Process Approximations,” and Matthias Tan with “Gaussian Process Modeling of a Functional Output with Information from Boundary and Initial Conditions and Analytical Approximations.”

                The Journal of Quality Technology (JQT) invited session will include Doug Montgomery, who will speak about 50 years of JQT; Michael Hamada, who will discuss estimation of a service-life distribution based on production counts and a failure database; and John R. Lewis, who will talk about selecting an informative/discriminating multivariate response for inverse prediction.

                Lessons Learned from Data Challenges and Challenging Data

                • Anne Hansen, “Overcoming Data Obstacles and Driving a Data Culture”
                • David Osthus, “When Flu Forecasting Isn’t About the Flu: What I’ve Learned Participating in the CDC’s Influenza Forecasting Challenge”
                • Christine Anderson-Cook, “Data Competition Hosting: Getting More Than Just a Winner Through Strategic Design and Analysis”

                Data Science in New Mexico

                • Lauren Hund, “Strategies for Calibrating Inexact Computer Models to Estimate Physical Parameters”
                • Oleg Makhnin, “gibbSeq: A Bayesian Multiple Testing Method for Genetics Applications”
                • James Degnan, “Using Approximate Bayesian Computation to Infer Evolutionary Trees”

                Test Planning for Reliability

                • Laura Freeman, “Challenges and New Methods for Designing Reliability Experiments”
                • Lu Lu, “New Developments on Demonstration Test Plans”
                • Isaac Michaud, “Using Mutual Information for Designing Sensitivity Tests”

                Astrostatistics Interest Group

                • Luis Campos, “Disentangling Astronomical Sources with Spatial, Spectral, and Temporal X-Ray Data”
                • Gwendolyn Eadie, “Estimating the Mass to Light Ratio of the Milky Way’s Nuclear Star Cluster and Its Central Black Hole”

                Design for Computer Experiments

                • Robert Gramacy, “Replication or Exploration? Sequential Design for Stochastic Simulation Experiments”
                • Matthew Plumlee, “Calibration with Frequentists Coverage and Consistency”
                • Daniel W. Apley, “Input Mapping for Calibration of High/Low Fidelity Simulation Models with Mismatched Inputs”

                Design for Physical Experiments

                • Jeff Wu, “Analysis of Marginal Tail Means: A Robust Method for Parameter Design Optimization”
                • Xun Huan, “Value of Feedback and Forward-Looking in Bayesian Sequential Optimal Experimental Design”
                • Ryan Lekivetz, “Restricted Screening Designs”

                Statistical Machine Learning

                • Tom Loughin, “Adaptively Pruned Random Forests for Modeling Means and Variances Simultaneously”
                • Nicholas Henderson
                • Rob McCulloch

                Uncertainty Quantification

                • Michael Grosskopf
                • Peter Marcy, “Bayesian Gaussian Process Models for Dimension Reduction Uncertainties”
                • Jared Huling, “Neural Networks for Flexible and Fast Emulation of Computer Experiments”

                Invited sessions on statistical process control, physics applications, and functional data are also being planned.

                Biometrics Section News for April 2018

                Sun, 04/01/2018 - 7:00am

                The Biometrics Section will sponsor seven continuing education (CE) courses at the 2018 Joint Statistical Meetings in Vancouver. Here, we highlight four of them:

                Prediction in Event-Based Clinical Trials

                Instructors: Daniel Heitjan and Gui-Shuang Ying

                Did you ever wish you could use the accumulating data from your event-based clinical trial to reliably predict its future course? Well, now you can! Give these instructors a half day at JSM 2018 and they will teach you how using their Bayesian simulation methods coded in straightforward R.

                Participants will learn about flexible parametric and nonparametric prediction models for simulating future enrollment and event histories. The instructors will describe applications to real trials, showing how you can predict the timing of future interim analyses, identify efficient enrollment strategies informed by current data, and give DSMBs the best possible information on the likelihood of trial success.

                Bring your own computer and data and give their methods a try!

                Health Care Analytics in the Presence of Big Data

                Instructor: Evan Carey

                The phrase “big data” has become widespread, but what does it mean for the practicing health care analyst? Come to this course to learn more!

                In this course, participants will gain hands-on experience using cutting-edge software tools for the analysis of large administrative health care data sets, with a focus on Python and Apache Spark. Serial and parallel optimizations techniques using frequentist statistical frameworks and machine learning frameworks will be demonstrated.

                This course will focus on methods and software, rather than the clinical context, but numerous real-world examples will be discussed that will offer a broad perspective. Students will be provided with a copy of a functioning “virtual machine” with all software and course materials pre-installed.

                Regression Modeling Strategies

                Instructor: Frank Harrell

                When was the last time you had a “statistical modeling tune-up”? How do you keep up to date with methods for developing and validating predictive models, dealing with common analytical challenges, and graphically interpreting regression models? This course is the answer!

                Here is an enlightening and extremely popular course (that’s why we offer it nearly every year) that covers multivariable regression modeling strategies, relaxing linearity assumptions, interaction surfaces, differences with machine learning, classification vs. prediction, quantifying predictive accuracy, detailed case studies using R, and more.

                Introduction to Bayesian Nonparametric Methods for Causal Inference

                Instructors: Jason Roy and Michael Daniels

                Have you ever thought about trying more innovative approaches to causal inference, but you didn’t know how to begin? Bayesian nonparametric methods (BNP) could be exactly what you are looking for!

                In this short course, expert instructors will review BNP methods and illustrate their use for causal inference in the setting of point treatments, dynamic (longitudinal) treatments, and mediation.

                The BNP approach to causal inference has several possible advantages over popular semiparametric methods, including efficiency gains, the ease of causal inference on any functionals of the distribution of potential outcomes, the use of prior information, and capturing uncertainty about causal assumption via informative prior distributions. You’ll learn even more from their wealth of examples, supported by detailed instructions for software implementation using R.