Understanding the General Linear Model

AI Thread Summary
The General Linear Model (GLM) can be confusing, particularly in distinguishing its relationship to ANOVA and regression analysis compared to t-tests. ANOVA is considered more similar to regression because both analyze variance and can handle multiple predictors. There is a request for accessible resources, such as simple explanations with visual aids, to better understand the GLM. The discussion highlights a sense of frustration with the complexity of the topic and a desire for clearer guidance. Overall, the need for simplified educational materials on the GLM is emphasized.
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I've been trying to get my head around the GLR on & off for months now, and its not getting much clearer. Why is ANOVA more like regression analysis than a t-test? Anybody know of where I can get a good, simple, explanation of the GLR, preferably with little cartoons and soothing music?
 
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Perhaps if I stop calling it the 'GLR' (Greater London Radio) :rolleyes: someone might be willing to help?
 
Can I take it that nobody knows diddly about the GLM?
 
I was reading documentation about the soundness and completeness of logic formal systems. Consider the following $$\vdash_S \phi$$ where ##S## is the proof-system making part the formal system and ##\phi## is a wff (well formed formula) of the formal language. Note the blank on left of the turnstile symbol ##\vdash_S##, as far as I can tell it actually represents the empty set. So what does it mean ? I guess it actually means ##\phi## is a theorem of the formal system, i.e. there is a...
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