Member Training: Matrix Algebra for Data Analysts: A Primer

by Karen Grace-Martin

If you’ve been doing data analysis for very long, you’ve certainly come across terms, concepts, and processes of matrix algebra.  Not just matrices, but:

  • Matrix addition and multiplication
  • Traces and determinants
  • Eigenvalues and Eigenvectors
  • Inverting and transposing
  • Positive and negative definite

These mathematical ideas are at the heart of calculating everything from regression models to factor analysis; from multivariate statistics to multilevel models.

Data analysts can get away without ever understanding matrix algebra, certainly.  But there are times when having even a basic understanding of how matrix algebra works and what it has to do with data can really make your analyses make a little more sense.

In this webinar we’ll introduce some matrix algebra terms and methods that directly apply to the statistical analyses you’re already doing.


Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

About the Instructor

Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.

She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.

Not a Member Yet?

It’s never too early (or late) to set yourself up for successful analysis with support and training from expert statisticians.

Just head over and sign up for Statistically Speaking.

You’ll get exclusive access to this training webinar, plus live Q&A sessions, a private stats forum, 75+ other stats trainings, and more.

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