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Viewing session type: Short Course (half day)
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Tuesday, February 1
Tue, Feb 1
10:00 AM - 1:30 PM
Virtual
SC04 - Skills for Statistical Writing: Tips and Tricks for Improving Written Communication
Short Course (half day)
Instructor(s): Emily Griffith, North Carolina State University; Julia Sharp, Colorado State University; Zachary Weller, Colorado State University
Effective writing is an essential skill for statistical practitioners, yet it is a skill that is often overlooked in coursework due to the need to stay up to date on the latest statistical methodology. This course will provide participants the opportunity to think critically about the writing process and learn about principles and best practices for statistical writing. Participants will improve their writing skills through participation in writing exercises and will be given the opportunity to receive feedback on their writing. The course will address topics such as organizing and streamlining, reducing clutter, best practices for peer review, and statistical aspects of writing such as alternatives to using the term “statistically significant”. The course will engage participants through discussion and short exercises of editing and reviewing writing samples.
Outline & Objectives
Outline: Introduction (20 min); Introductory Lecture (1 hr): instructors share how they work through the writing process, best practices, and principles of effective writing; Discussion, Questions, and Conversation (15 min); Break (10 min); Mini-lectures with exercises (1 hr 45 min, approximately 25 minutes each): [1] organization and streamlining with exercises in building outlines and telling the story, [2] peer review: what should a peer review look like with exercise in reviewing writing samples and peer review checklist, [3] reducing clutter with exercises in a checklist of steps for reducing clutter and paced, productive, and powerful writing, and [4] statistical aspects of writing such as avoiding “statistically significant” with exercise of rewriting passages; Closing Discussion (15 mins).
Objectives: [1] Improve participants’ confidence and skills in written communication through examples and discussion. [2] Give participants the opportunity to get feedback on their own writing and learn best practices for giving feedback on the writing of others. [3] Provide participants with tips, tricks, and resources for improving their writing and reviewing skills.
About the Instructor
The three instructors (Dr. Zach Weller, Dr. Julia Sharp, Dr. Emily Griffith) for this course have extensive statistical collaboration expertise and PhDs in Statistics. All three instructors have successfully published and peer-reviewed numerous papers in both statistics and applied science journals. The instructors have also been involved in grant writing as both principal investigators and collaborating statisticians.
Relevance to Conference Goals
This short course will increase participants’ confidence in written communication by providing them resources and feedback on the writing and review process. The course will improve participants' skills through short lectures on writing topics followed by exercises and discussion.
Tue, Feb 1
10:00 AM - 1:30 PM
Virtual
SC05 - Using Design of Experiments (DOE) in Industry
Short Course (half day)
Instructor(s): Theodoro Koulis, Genentech; Tony Pourmohamad, Genentech
Design of experiments (DOE) remains the gold standard for the design and development of industrial applications. DOEs can increase efficiencies and provide valuable experimental information that may be used to improve industrial processes. Despite its valuable contributions to various industries, there are a lot of misconceptions of DOE. This course is geared towards applied practitioners who may not be aware of the strengths and benefits of factorial designs. The course includes real datasets and examples from the biotechnology industry. Course participants will be able to use the lessons learned in order to design more efficient experiments in their own domains.
Outline & Objectives
Outline: The course covers fundamental design concepts and presents a simple approach to the design and analysis of multi-factor screening designs. Participants will learn how to design, conduct and analyze multi-factor experiments. No prior statistical training is assumed.
Objectives: The course will cover the following topics
- One-at-a-time vs multi-factor experiments
- Feasible space, design space and center points
- Factorial, fractional factorial, Plackett-Burman designs, and projectability
Participants will be able to design their own multifactor experiments, and will be able to analyze the data using simple techniques. The course will use the JMP Statistical software. Course participants will be able to use the 30-day free trial version of JMP.
About the Instructor
Theo Koulis obtained his PhD in Statistics from the University of Waterloo in Canada. His professional interests include: computational statistics, design of experiments, and statistical consulting. Theo is a Senior Statistician in Nonclinical Biostatistics at Genentech, Inc. supporting CMC (chemistry manufacturing and control) statistics activities. For over 7 years, Theo has supported manufacturing development at Genentech and has gained practical experience designing and implementing experiments in the biotechnology industry. Once a quarter, Theo teaches a Design of Experiments course that is geared towards specific needs of scientists and engineers working in the biotechnology industry.
Relevance to Conference Goals
The course is designed with the applied statistical practitioner in mind. The course will use real world data and examples in order to showcase the benefits of using DOEs in industry. Although the data generated from DOEs can be analyzed using simple techniques, the designed experiments can be used to generate rich and informative datasets. In addition, the course will showcase the JMP DOE toolset, which facilitates the design and analysis of DOEs.
Tue, Feb 1
2:00 PM - 5:30 PM
Virtual
SC06 - Equity and Bias in Algorithms: A Discussion of the Landscape and Techniques for Practitioners
Short Course (half day)
Instructor(s): Emily Hadley, RTI International Center for Data Science
With the growing use of algorithms in many domains, considerations of algorithmic bias and equity have far-reaching implications for society. A developing body of literature highlights the negative impact that biased algorithms can have on individual lives, while new resources provide opportunities for practicing statisticians and data scientists to better incorporate equity into our own work.
In this course, we review the landscape of equity and bias in algorithms. We take a deep dive into specific decision points related to bias and equity throughout the algorithm process, including problem framing, collecting data, completing analyses, and detecting and mitigating bias, and we discuss specific techniques that statisticians and data scientists can use to address these challenges. Attendees will evaluate tools and approaches relevant to their own work. Group discussion is a key component of this course.
Outline & Objectives
About the Instructor
Emily Hadley is a Research Data Scientist with the RTI International Center for Data Science. Her work spans several practice areas including health, education, social policy, and criminal justice. She has experience with machine learning, natural language processing, agent-based modeling, and predictive analytics, with a strong interest in antiracism, bias, and equity in data science. Emily holds a Bachelor of Science in Statistics with a second major in Public Policy Studies from Duke and a Master of Science in Analytics from NC State.
Relevance to Conference Goals
Tue, Feb 1
2:00 PM - 5:30 PM
Virtual
SC07 - Regression-Style Modeling with Variable Selection and Reduction
Short Course (half day)
Instructor(s): Clay Barker, SAS Institute / JMP Division; Ruth Hummel, SAS Institute / JMP Division
Variable Selection is a crucial step in the model building process, whether we are building a predictive model or trying to understand the results of a designed experiment. Generalized Regression modeling provides a single framework for doing interactive variable selection and fitting generalized linear models. This workshop will start with a brief overview of the generalized linear model for modeling responses that are not necessarily normally distributed. We will also introduce variable selection techniques, including stepwise methods like Forward Selection and penalized regression methods like the Lasso. We close the workshop with examples featuring both observational and experimental data and a variety of response types.
Outline & Objectives
About the Instructor
Dr. Clay Barker is a Senior Research Statistician Developer with JMP (a division of SAS) on a variety of statistical platforms in JMP, including Generalized Regression, Fit Curve and Clustering. He earned his doctorate in statistics from North Carolina State University. He holds several patents, including one for his work on implementing new visualizations for interactive model building in generalized regression.
Dr. Ruth Hummel is an Academic Ambassador with JMP (a division of SAS), supporting the technical needs of professors and instructors who use JMP for teaching and research. Dr. Hummel is a coauthor of Business Statistics and Analytics in Practice, 9th edition (2018), and has been teaching and consulting about statistics and analytics for over a decade, at the University of Florida, at the US Environmental Protection Agency, and now at SAS/JMP. She has a PhD in Statistics from The Pennsylvania State University.
Relevance to Conference Goals