Statistics 101: Linear Regression, Prediction Interval Bands
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- เผยแพร่เมื่อ 25 ส.ค. 2024
- In this Statistics 101 video, we calculate prediction interval bands in regression. To support the channel and signup for your FREE trial to The Great Courses Plus visit here: ow.ly/xVD030fiZ8S
So this video is the next in my series on simple linear regression. And this video is about prediction interval bands. So in the previous video, we talked about confidence interval bands. This one is about prediction interval bands. So remember confidence interval bands are about the mean value of the dependent variable at a given value of the independent variable. This is about individual predictions. So we're gonna have two different bands on our graph. One's confidence intervals, that was the previous video, this one is about prediction intervals.
So if you are new to Regression or are still trying to figure out exactly what it even IS...this video is for you. Sit back, relax, and let's go ahead and get to work.
My playlist table of contents, Video Companion Guide PDF documents, and file downloads can be found on my website: www.bcfoltz.com
#statistics #regression #machinelearning
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I miss his encouragement and reassurances from the initial few videos in the series
Thank you for making this video. In short, a 95% confidence interval catches the true mean for 95 times out of 100. 95% prediction interval catches the individual y generated by the true mean 95 times out of 100. Visual explanation around 14:40 is very helpful. Thank you again!
Yes for some reason i did not understand confidence vs prediction interval when it is shockingly simple. The visual is helpful but i think the explanation could be much improved starting from previous videos.
Hi, Brandon. Thank you so much for making these videos as they are a big help (reference source) in helping in the learning process of my University students. Keep up the good work!
Great video, especially with the illustration!
Dude your videos are awesome !
Thank you so much.
thanks
Those videos have helped me a lot.!!!!
I can't wait to watch more and more videos~~~
(( I have a Statistic midterm exam in the next week.......TAT
感恩版主，等不及看更多影片了(下禮拜是統計學期中考，這些影片拯救了我!!!
Excellent your explanation....thanks
Brandon your not a nerd, your actually amazingly awesome!
tnx
I think what doesn’t make it clear in many videos and lecture notes is that the prediction interval is the interval of the population regression line in which an observed y value is in , is this correct ?
Fantastic video!
Very clear explanation.. good job bro...
@14:46 If we are combining two variances of the star marks and the dots together to get the prediction interval, then won't the star variances and dot variances overlap each other causing double addition?
At 9:29 , how does the software predict a negative value for the lower bound of the tip if the bill is 34$ or 51$? That doesn't seem feasible in reality.
In population, the Y distribution is an actual value, not estimated. So if we use a population data, does PI band equal CI band?
Thank you so much for the VDO.
Thanks Brandon. How is the prediction interval different from just using the regression equation to predict a value of y (at a given level of confidence?). Which approach would you use if you were trying to do predictive analysis?
Hello! I would report both the CI and PI.
Nice video's! If there would be for example 2 restaurants at which the tips were analysed, how could we then calculate the 95% CI of the difference in tip amount at $64? Or with other words, how can we calculate at a certain point in two regression lines if the difference between them is significant? Should I use the 95% prediction interval or the 95% CI?
Great video, Thanks a lot! But wait, isn't Sy*-squre for the mean of E(y*) from your last video (Statistics 101: Linear Regression, Confidence Bands - Part 2 of 2,) in which you used Sy* square to calculate confidence interval, would you please give deep explain?
Brandon Foltz is a god.... nuff said
can someone explain how the mean value of the tip and the individual value of the tip is different?
The mean value of the actual population of tips on a $64 bill is the average of a lot of possible tips given, whenever the bill happens to be $64. The individual value is an attempt to estimate what the tip will be one particular time that the bill is $64. We can calculate averages a lot more confidently than we can one particular outcome. One particular outcome is more affected by random chance. That is why the '1' is added in, under the radical sign, to enlarge the whole 'give or take' quantity after the plus-or-minus sign.
please, can I known the name of Textbook that you are used ?
Hi Brandon,
At 19:06, shouldn't the (64-74)^2 values be values of the IDV instead of the dependent variable? or am I misunderstanding something? thank you always!
Yes even I thought so.
The tip amount is dependent on the meal amount.
Yeah, x should fundamentally be the independent variable ..right?
I think that's a typo since X is the independent variable
@@tomasnobrega8087 right
Im just getting on you video and I don't know what you used before. please explain. I need help with this specific topic........
Why are people actually proud of being "nerds" now?
Because culture and knowledge are things that you should always be proud of.