Statistics 101: Nonlinear Regression, The Quadratic Model
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- เผยแพร่เมื่อ 26 พ.ค. 2018
- In this Statistics 101 video, we learn about the nonlinear quadratic model. To support the channel and signup for your FREE trial to The Great Courses Plus visit here: ow.ly/xVD030fiZ8S
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#statistics #regression #machinelearning
I was that quite student in the class without any participation, but now! Oh man I even stand up in front of the class and explain what everything comes together. Thank you from the bottom of my heart
I am happy the channel is still active, and videos are being uploaded. I hope you keep it up... The tutorials are great, really love them. Tbh, I have tried a few out there but these were the awesomest from all I can see. :)
This is much better quality both in delivery the content and lay out. Thank you so much. Should have a course on udemy. Then i would have taken it 100%.
Bless you for these amazing videos! You're making my quarantine enjoyable and useful :)
amazing like always...your videos are really made to facilitate understanding...Great Job Sir!
Nice video, thanks...Excellent explanation.
Wow! Thank you for making it understandable!
Thank you PROF
really easy to understand. thank you, it helps me alot. :)
Hi! I am sorry but using a predictor^2 does not change your OLS problem to a non-linear one.
Great video. Indebted to you Brandon.
Hi Brandon, do you know how I can manually calculate sum of square of quadratic factors such as A^2, B^2 etc?
Great video! In reality, due to job turn over, there would not be as many salesmen still working after five years compared to the number of salesmen who have worked less than three years. So the density of observations should show more than half who had 3.5 years or less experience. Unless, there is almost no job turn over.
Hi Brandon, a request. I am interested in understanding the relationship of Design of Experiments, like a Central Composite Design, with Regression. Are they different subjects, or is Regression a component of DOE? Also, I think I understand interaction effects, but quadratics --- I cannot grasp them yet. I really like how you slow down and explain what is going on, answering all the questions that a novice would ask themselves as they went through the process. We like understanding what the calculations are, even though we will ultimately let the software do it. Any thoughts on this are appreciated, or if you want to ask me for any clarification, I hope I am explaining it right.
hey, I love your videos, do you plan to do something on bayesian statistics??
Hello Brandon I have one question.
Hi Brandon
Thank you Brandon it was really helpful in my professional transformation.
how did you decide that b1 is the x term and b2 is the xsquared term?
Super