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• Asking Good Questions to Facilitate Better Collaboration and Enhance Relationships

  • When
    11 Dec 2025
  •  
    11:30 AM

  Topic: Asking Good Questions to Facilitate Better Collaboration and Enhance Relationships 

  Speaker: Christine M. Anderson-Cook, Los Alamos, NM 

  Date: December 11, 11:30am Eastern Time / 5:30pm Central European Time / 8:30 am Pacific Time

  Abstract

  I think most of us have an inherent sense that asking the right question in the right way at the right time can be critical to smooth-running teams and good collaborations. In this talk, I explore many different goals of question asking beyond the exchange of factual information or assessment. These include soliciting feedback, establishing rapport and inclusiveness, changing the flow of a discussion, and confirming the exchange of information. We benefit, and so do our colleagues. Understanding the diverse potential for how good questions can impact our collaborations makes us a better team member and a more sought-after colleague. Once the “why” has been considered, I will also present some strategies of “how” to pose questions to build an open and non-threatening environment. Details of the material are available in the JMP White Paper at www.jmp.com/asking-good-questions. 

  Speaker Bio

  Christine M. Anderson-Cook retired from Los Alamos National Laboratory in 2021 after 17 years in the Statistical Sciences Group. Before her time as a Research Scientist, she was a faculty member in the Department of Statistics at Virginia Tech. She is passionate about statistical engineering and its potential for raising the impact that statisticians can have in their organization. In 2012, she co-edited (with Lu Lu, at the University of South Florida) the first Special Issue on “Statistical Engineering” in Quality Engineering. She is a regular columnist for the Statistics Spotlight in Quality Progress, and has written numerous papers and case studies on applied statistics and how to make it practical and easily implemented for problem solving and decision making. She is a Fellow of ASA and ASQ. She has been honored with the George Box Medal, the Gerald Hahn Achievement Award, the Shewhart Medal and the William G. Hunter Award. 

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• A Statistical Engineering Example: Forecasting Stability at P&G

  • When
    27 Jan 2026
  •  
    11:30 AM

  Topic: A Statistical Engineering Example: Forecasting Stability at P&G

  Speaker: Fangyi Luo, Director/Principal Statistician and Data Scientist, P&G

  Date: Tuesday Jan 27, 2026 at 8:30 am PST /11:30 am EST / 5:30pm Central European Time

  Abstract

  In this webinar, I will share a statistical engineering example from Procter & Gamble, focusing on the forecasting of product stability and predicting shelf life or long-term stability failure risk with uncertainty estimates at the early stages of product development. This was an unstructured, large, and complex problem and we used a statistical engineering approach to tackle this problem effectively.
  
  I will also provide an overview of various statistical methods used in forecasting and predictive stability, including degradation and reliability modeling, chemometric Multivariate Curve Resolution, Bayesian Analysis of Differential Equations, AI deep learning, Bayesian Network modeling, and Functional Data Analysis.  I will demonstrate applications of these methods across multiple products and stability failures.  Additionally, I will share key learnings gained from solving this complex problem, as well as some of the newer challenges we are currently working on.
  
  

  Speaker Bio

  Fangyi Luo, Director/Principal Statistician and Data Scientist, has been with P&G for 28 years. She holds a Ph.D. in biostatistics from the University of Cincinnati and has statistical design and modeling experience across various areas, including clinical and consumer research, formulation, process, stability, and packaging. She has extensive expertise in developing advanced predictive and forecasting stability models and is currently focused on packaging modeling and 3D computer vision.

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