Finding practice problems on linear models grad level

AI Thread Summary
A first-year statistics graduate student is seeking practice problems related to linear models, specifically focusing on topics like estimability, Gauss-Markov theorem, and least square estimators. They express difficulty finding resources that apply the theoretical definitions learned in class. Other participants suggest that there are numerous books available that contain relevant problems, and they inquire if the student received a list of references from their instructor. Recommended titles include "Fundamentals of Statistics" by Goon, Gupta, and Dasgupta, and "Linear Statistical Inference" by C.R. Rao. The discussion highlights the need for practical exercises to reinforce theoretical concepts in linear models.
libragirl79
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Hi,

I am a first year stats grad student and I am taking a class on linear models. The book and lectures focus on the format of definition, proof, lemma, definition, proof, lemma etc. However, I cannot seem to find anywhere some practice problems where I could use these definitions before I actually take the tests. Some of the topics include estimability, gauss markov, aitken model, projection matrices, least square estimators, multivariate normal, spectral decomposition, cochran's thm and so on.

If anyone knows of any books or websites or anything else that has some practice problems that use these definitions and formulas it'd be much appreciated!

Thank you in advance!
 
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Actually there are countably large number of books for all standards and all contain good problems.
Did not your teacher give a list of references!
 
Could you please give me a couple of titles? Thanks!
 
Fundamentals of Statistics, by Goon, Gupta and Dasgupta. Linear Statistical Inference by C.R.Rao.
 
Thank you!
 
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