Using Linear Models for t tests and ANOVA, Clearly Explained!!! 

bam! :)

Glad you like them!

Good luck!!

Thanks!

Thanks!

Thank you!

I'll keep those topics in mind.

You might want to start with the first video in this series (this is "part 2") because that will answer your questions about how the p-values are computed and what they mean: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-nk2CQITm_eo.html

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ANOVA is just a specific type of General Linear Model.

An ANOVA is just a generalized t-test. Technically, you can do an ANOVA with just 2 conditions, Drug A vs a control, and you'll get the exact same results as a t-test (which is why people call it a t-test).

:)

yeah lol, idk if it's a re-upload though

It's a re-upload. For some reason RU-vid put the originals behind a paywall...so I re-uploaded them so that they would still be free.

@@statquest oh ok, interesting 🤔, I thought you manually did that, lol. Thanks 👍 for the information and clarification. I'm curious whether if you access your own video or do you have to pay RU-vid to watch your own videos as well?

@@computerconcepts3352 I can watch them, so at first I had no idea what was going on. The content worked for me, but no one else. It was very confusing and stressful because I got a lot of negative comments. Ugh. A bad day in StatLand. :(

@@statquest oofty doof oof oof, I guess uploading videos to other platforms could help reduce damages against something like this but then I really hate how RU-vid does random things like this. I remember one of my videos got deleted for the wrong reason and I lost a bunch of views. Fixing RU-vid is actually one of my eventual goals and me watching your RU-vid videos is my first step towards that goal 👍

What time point, minutes and seconds, are you asking about?

It is spreadt between 5:00-7:00. ... where you start to show y= ewuations and you end up with simple equation in 7 amd then you move on, I am confused how this fit together

@@pavoldzama4641 First, when we use the equations to calculate the exact y-axis value for each of the known data point, we include the residuals because the mean value + the residual = the exact y-axis coordinate for the original data value. However, when we use the equations to make predictions - say like someone asks us to predict gene expression for a new control mouse - and we don't actually know what that value is - then we leave the residual off the equation, since we don't know what the difference between the mean and the "true" value is. Thus, when the equation is used with known values for the y-axis, we include the residual. When the equation is used to make predictions (and we don't know the y-axis value) we leave off the residual. Now, the reason we keep track of the equations in a matrix is that we can change the coefficients in one place and see how they effect the residuals and thus, how they minimize ss(fit).

​@@statquestthanks for explainig, I think maybe we misunderstand, I just could not connect the dost .... I think I get it now after watching again, you use this formula for fit and this should not include residual as we calculate residuals based on this... then I got co fused pretty much on those matrices but ypur follow up video saved the day! Great wotk, keep it up

It's actually the exact same test - there are just two ways to do it. The advantage of doing it this way (using regression) is that we have more flexibility - we can easily generalize it into an ANOVA test or even something more fancy.