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25:43, SST=SSR+SSE has something to do with Ordinary Least Squares. The goal of OLS is to find the line that minimizes the sum of squared residuals which is the SSE in this video. And yes, the detailed math proof of that equation is more detailed.

Thanks! Appreciate triggering the intuition!!

13:53 so your saying we arbitrailly use n-1 to inflate the variance because the population variance (and thus Std Dev) may, or likely to be, larger? Why not use n-2 then? I guess because you need two data points to get to a standard dev.

I had really high expectations from this video but the cultural bias didn't help me at all...I have no clue about NBA and what 3 pointer is...that added to my confusion a lot more :(

How will I understand the price conversion e9044 to real dollar terms $8468. Can you please help me understand!

this guy, bluebrown guy and stat quest guy is pioneer of stats/maths/ML and DL ngl!!!!

Clearest explanation I've encountered so far! Thank you!

@5:42, I searched up and kinda found an intuitive explanation about why we don't use absolute value: "Standard deviation is a statistical measurement of how spread out a data set is relative to its mean. When data points are further away from the mean, the data set has a higher deviation and a greater standard deviation. This is because the data points become more dissimilar and extreme values become more likely." And I assume this also has to do with the shape of the bell curve. If it were a piecewise linear curve, i.e., an angle shape, then absolute values would probably be enough. Let me know what you think.

For the first example , I could not find anyone telling the answer . I am very new to statistics. But is it : Numerical discrete ? Thanks

Thank you for your nice, informative and clearly explained video.

This video is amazing; it helped me so much!👍

Why can’t all professors teach it like this?

This is awesome!

Please upload the rest of the 3 videos as well, please?

I would like to understand the intuition behind calculation of standard error (formulae) next to slope coefficient. I haven't seen it in any of your videos or I have missed it

For the very first time in my life, I understood what the hell log is all about; while finishing my PhD. I am so happy I learned this today.

Grand

Marvellous

Heteroskedasticity of our city, of our ciiiiiity

Crystal clear explanation of what it means actually.... Best in the category... Kudos

Thank you very much. What are you doing is changing my perspective about the concept of learning. I will definitely recommend your channel. I wish you the best 🤍

i dont know what you are going through right now (since you are not active on this channel anymore) but i want to say this YOU ARE ONE OF A KIND in all youtube. its been 3 years since i got to know about this channel and i keep coming back everytime i need to brush up my concepts

absolute lifesaver 🙏

Excellent work! This is a proper lecture about this issue. But I can't download the spreadsheet, is it not available now? It would be priceless. Otherwise I have to make my own.

Hi Have you done anything about latent class analysis and cluster analysis Many thanks your intuitive teaching is amazing!

Wow, this was great explanation. Thanks Justin!

you explained it a lot easier and probably my course teacher did too but i need to watch it 3 more times.

Please do go into the excruciating derivations of second & third moments- both sample and population! In the SAMPLE third moment for example, it makes sense algebraically that (n-1)(n-2) factors out, but since when is n-2 make sense with regards to degrees of freedom? So on with the 4th moment?

its so nicely explained that i wanna explain it to someone else now

THANK YOU SO MUCH

at last I understand !!! thank you so much !!!

Medical student, studied the whole course, but feels like know nothing, exam in one and a half hour 😂😢

Thanks for the wonderful explanation. Just one confusion in the section "Visualisation (PDF and CDF)" - the Exponential distribution graph at @6:35 minutes is correct? because on the Y-axis you have put values greater than 1. but shouldn't these values be less than 1 representing the probability?

i love you and thank you

Excellent. Thank you.

This the only video I found that explains this concept clearly and correctly!

Exceptionally explanatory and illustrative! Thank you so much for your excellent video!

excellent

This class is very useful and deeply knowledge full for me . thanks a lot

Negative skew looks like a hockeystick: Income, CO2, average temperature over time (these are not the typical subject of statistics I suppose) Another example could be all things associated with old age on the Y axis and old age on the X axis. (E.g. hearing loss vs. age, number of close people died vs. age etc.)

thank you so much for this

Loved it. Been trying to undertsand this concept for sometime now...

I have not watched it yet. But the comments are overwhelmingly supportive to my desire to do better in maths despite my job and age.

What about using Delta method in obtaining variance of S(t)?

You are amazing. Thank you for the vedio!

super clear explanation. Thank you !

That did not feel like 40 mins, thank you!

My final is tomorrow

Not sure if you read comments on old videos. looking for advice please. I know nothing about maths or excel until a year or so ago (I am 70 now). I have been using Poisson to predict an event. Imagine a coin. Head, Tails and it can land on its edge. But each time the weight of the coin is heavier on the head side, or tail side or even the edge. Those variables are always known. What is the best type of probability distribution I should be using to predict head,tail or edge. I have watched your video series and now just think that Poisson is not the best fit. Any help would be appreciated. Thank you