longer is better. this is perfect for something im actually working on at work right now(fun with manufacturing and statistics). I'm really looking forward to part 2! Love your videos! keep up the good work! oh and first i guess.
I’m watching these videos for my actuary exams, and I have to learn linear regression in my notes it spits out a formulae for mean and predicted response and I have no intuition for what the understanding is between them as it’s so ambiguous, hopefully once I’m done watching your videos I can understand the difference better
Thanks! nice and clear explanation. I used the ci option randomly in my thesis and right now, evening before the defense I realized I can't interpret it :D so thank you!
I understand that since there can be many samples, our expected tip value for a given bill value is not just one number you get by putting values in the equation of y = b0+b1x. It will be an estimate for all the different values you get for different samples. But you can have only one mean for those different expected values of different samples. How can you have a number of different means for one input value of bill amount. You can have a distribution for different expected values of y for a given x(bill amount) because of different samples, but how can you have different mean values and their distribution? it will be just one mean expected value for all those different expected values for different samples, right?
At 14.13 it looks like the video was interrupted. Like there were a few slides still to come but then it cuts to the 'Great Courses' promo instead and then the video abruptly ends? There's a fraction of a second where the 'Final Points' slide appears but it quickly disappears and isn't discussed...
Hi Brandon, thank you so much for these videos... so clear and helpful! @13:57 you discuss the tip amount and the CI lower and upper. When the bill is $34, the CI includes zero, does this mean that we must accept the null hypothesis at this bill amount?
Hi Grace! Assumption is normality, but it's pretty robust with respect to violations. Transformations are also OK and common if normality is violated in an extreme degree.
Brandon the notation is actually weird. I mean look at the slide on 09:35. Phrased like this is like saying constructing a ci for the estimate, although this is not what you are trying to say as explained earlier. The ^ symbol as far as I have seen is used for estimates, I prefer the notation of μ^Υ |X* for the estimate and μΥ |X* the parameter that you actually make the ci for.
Hi! As I say in the video, different authors, practitioners,instructors, etc. use different notation. I use the one I am familiar with. Sorry for the confusion. Hopefully someday everyone will settle on one way but I'm not holding my breath :)
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longer is better. this is perfect for something im actually working on at work right now(fun with manufacturing and statistics). I'm really looking forward to part 2!
Love your videos! keep up the good work!
oh and first i guess.
I’m watching these videos for my actuary exams, and I have to learn linear regression in my notes it spits out a formulae for mean and predicted response and I have no intuition for what the understanding is between them as it’s so ambiguous, hopefully once I’m done watching your videos I can understand the difference better
God bless you. I m saying that for a 100th time. U even arranged all vids into playlists.
Thanks! nice and clear explanation. I used the ci option randomly in my thesis and right now, evening before the defense I realized I can't interpret it :D so thank you!
I understand that since there can be many samples, our expected tip value for a given bill value is not just one number you get by putting values in the equation of y = b0+b1x. It will be an estimate for all the different values you get for different samples. But you can have only one mean for those different expected values of different samples. How can you have a number of different means for one input value of bill amount. You can have a distribution for different expected values of y for a given x(bill amount) because of different samples, but how can you have different mean values and their distribution? it will be just one mean expected value for all those different expected values for different samples, right?
At 14.13 it looks like the video was interrupted. Like there were a few slides still to come but then it cuts to the 'Great Courses' promo instead and then the video abruptly ends? There's a fraction of a second where the 'Final Points' slide appears but it quickly disappears and isn't discussed...
Thanks Sir....so great a teacher
Wonderfull man, your work is benefigial
Thank you for the great work and helping tons of people understand stats!
This is G-R-E-A-T! If I could, I would have tipped you on paypal. Keep the good work
Thank you! I do have a GoFundMe ❤️
thank you very much!
Great videos on this playlist. Thank you. Do you have any videos how to use regression for segmentation?
Thanks from Ross and Cassie
thank u sir !
Hi Brandon, thank you so much for these videos... so clear and helpful! @13:57 you discuss the tip amount and the CI lower and upper. When the bill is $34, the CI includes zero, does this mean that we must accept the null hypothesis at this bill amount?
Thanks. What if there is only one data point with $64, how to get the distribution?
thank you so much
Do you have any videos how to use regression for market segmentation?
Did you check out the whole channel ? Did you also search for it on his website ?
Thank you so much :-) !
what distribution are you assuming?
Hi Grace! Assumption is normality, but it's pretty robust with respect to violations. Transformations are also OK and common if normality is violated in an extreme degree.
I miss the positive reinforcement at the start. It was nice to hear 😅
Thank you for breaking everything down visually 🙏
Brandon the notation is actually weird. I mean look at the slide on 09:35. Phrased like this is like saying constructing a ci for the estimate, although this is not what you are trying to say as explained earlier. The ^ symbol as far as I have seen is used for estimates, I prefer the notation of μ^Υ |X* for the estimate and μΥ |X* the parameter that you actually make the ci for.
Hi! As I say in the video, different authors, practitioners,instructors, etc. use different notation. I use the one I am familiar with. Sorry for the confusion. Hopefully someday everyone will settle on one way but I'm not holding my breath :)
"thinking in your head"?