There is a bit of art and experience to model building. You need to build a model to answer your research question but how do you build a statistical model when there are no instructions in the box?
There are two basic strategies: Top Down and Bottom Up.
The best strategy and the appropriate framework to implement that strategy depend on many considerations.
This webinar will also cover:
- How your hypothesis influences your choices
- Practical issues that affect your options, like missing data, multicollinearity, and sample size
- Important model-building concepts like over-fitting and specification error
- Criteria for deciding whether to keep or drop a predictor
- When to include interactions, both significant and non-significant
- Transformations, centering, and high order variables
The role of model fit in decision making
This webinar will review model building strategies and teach you how to choose an approach that is best suited to your research.
Note: This webinar is an exclusive benefit to members of the Statistically Speaking Membership Program.
Date and Time
Wednesday, January 23, 2019
1pm – 2:30pm (US EST) (In a different time zone?)
About the Instructor
Audrey Schnell is a professional statistical consultant with a Master’s Degree in Clinical Psychology and a PhD in Epidemiology and Biostatistics.
She moved into the emerging field of genetic epidemiology and statistical genetics, and worked on a wide variety of common diseases believed to have a strong genetic component including hypertension, diabetes and psychiatric disorders. She helped develop software to analyze genetic data and taught classes in the US and Europe. Audrey has also worked for a number of institutions, including Case Western Reserve University, Cedars-Sinai, University of California at San Francisco and Johns Hopkins.
Not a Member Yet?
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Just head over and sign up for Statistically Speaking.
You’ll get exclusive access to this month’s webinar, plus live Q&A sessions, a private stats forum, 70+ video recordings of member webinars, and more.
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