Matrix Algebra Review

In this video, @centerstat instructors Patrick Curran & Dan Bauer provide a review of matrix algebra concepts and operations that commonly appear in multivariate statistics. It is important to have some familiarity with matrix algebra when learning and applying multivariate models for several reasons: models are often expressed in matrix notation, software often references model matrices in output, and some things in multivariate statistics are really best understood with reference to matrix operations. In this video, Patrick and Dan provide a guided tour of this foreign land, imparting conceptual meaning to matrix operations, offering humorous and semi-pertinent anecdotes, and occasionally bickering good naturedly with one another. We hope you will find this material to be of some use as you learn and apply multivariate statistics of various kinds.

[dougcheung]

Patrick is an amazing teacher. I took his CenterStat's free course on intro to SEM and it was life changing (no affiliations declared).

[aun]

A much needed one!
Many want to 'do' multivariate statistics but few want to pick a book about matrices.

[Ivaniushina]

Thank you so much for clear explanation

[larissacury]

Hi, thanks very very much for this, it helped me a lot to get an overall understanding of matrix algebra. I started to study matrix algebra because I already know how to perform and interpret regression models (in R, mostly), but now I'd like to go a step further and really understand what is going on behind the scenes to improve my understanding of them and of other models. Do you have tips on how could I do this? I mean, from a beginner's perspective, what resources may I use in order to understand matrix algebra so that I can apply to my existing knowledge of stats and futher expand it? (I don't come com a math background).

[shidnng]

Thank you, you’re truly gifted teachers! Attended several of Centerstat workshops and highly recommend EVERY ONE of them 🙌
Also, you really should consider the „Quantitube“-idea 😆😂