One of the biggest challenges that data analysts face is communicating statistical results to our clients, advisors, and colleagues who don’t have a statistics background.
Unfortunately, the way that we learn statistics is not usually the best way to communicate our work to others, and many of us are left on our own to navigate what is arguably the most important part of our work.
In this webinar, we will cover how to:
- Identify and gauge the communication needs of collaborators
- Simplify results without simplifying analyses
- Use effective communication methods, with several simple to more complicated examples for illustration
Date and Time
Wednesday, January 18, 2017
3 pm – 4:30 pm (US EST)
(In a different time zone?)
About the Instructor
Kim Love is the owner of and lead consultant at K. R. Love Quantitative Consulting and Collaboration.
Her expertise includes regression and linear models, categorical data, generalized linear models, mixed effects models, nonlinear models, repeated measures, and experimental design. She has worked as a statistical consultant and collaborator in multiple professional roles, most recently as the associate director of the University of Georgia Statistical Consulting Center.
While she enjoys working with clients with varied backgrounds, she particularly enjoys working with those who feel they have a less-than-perfect relationship with statistics. One of her goals is to spread an appreciation of statistics across many fields of study, starting by making it understandable to those who interact with it.
She has a B.A. in mathematics (2003) from the University of Virginia, and an M.S. (2004) and Ph.D. (2007) in statistics from Virginia Tech.
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- May 2016 Member Webinar: Communicating Statistical Results: When to use tables vs graphs to tell the data’s story
- November 2017 Member Webinar: A Data Analyst’s Guide to Methods and Tools for Reproducible Research
- March 2017 Webinar: Statistical Rules of Thumb: Essential Practices or Urban Myths?
- March 2018 Member Webinar: Using Transformations to Improve Your Linear Regression Model