Log odds interpretation of logistic regression
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- Опубліковано 29 жов 2013
- This video explains how the linear combination of the regression coefficients and the independent variables can be interpreted as representing the 'log odds' of success.
Check out oxbridge-tutor.co.uk/undergrad... for course materials, and information regarding updates on each of the courses. Check out ben-lambert.com/econometrics-... for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: ben-lambert.com/bayesian/ Accompanying this series, there will be a book: www.amazon.co.uk/gp/product/1... - Навчання та стиль
A 5 mins lecture >>>>>> An 1 hour and a half in class lecture. Mind blowing. Thank you.
Cheers Ben, this cleared up my understanding wonderfully!
Excellent explanation! Always come to your channel when I got confused in class! Thank you so much : )
Great explanation there. Can't believe this was posted way back in 2013. Still relevant nearly 10 years later!
Omg just the right explanation im looking for. My professor introduced everything out of thin air and it was hard to put pieces together even though I have some backgrounds on logistic regression.
Ben Lambert certainly has one of the best UA-cam channels ever
Thank you very much professor Lambert that was very helpful.
Can't stress enough how gratefoul I'm woth you kind Sir, my college degree in economics will be dedicated to you
this was very helpful; thanks!
Thanks simple videos to understand. Helps with many basic concepts....sometimes requiring review. hope to see more. Taking Categorical Data Analysis in the Spring....Doctoral level and I am not a Biostats major in Public Health....I am Epi. Looking forward to watching all to help review basic concepts I may have let go of.
Hi, many thanks for your message and kind words. If you are willing it would be great to see your syllabus; this would allow me to add these videos to the list. Best, Ben
Very nice. Please raise the audio volume, its very difficult to follow on my laptop.
Your video is 'To the point'
Thank you!
Thanks for showing but when doing 1-p. When you say the num and denim simplifying to one (you circled in purple) isn’t it just the number values of e^betas positive and negative values equaling zero then leaving you with 1/1+e^betas.
Thank you
So to make sure; one can say that log of the odds is equivalent to the dot product (wT dot X) which is where we get our linear combination?
Nice!
Thanks :)
Is the logistic regression part of GLM? Do you have videos explaining other GLM models in which the dependent variable follows other non-normal distributions, such as Gamma, poisson, negative binomial? Thank you!
ua-cam.com/video/J4zNmGqttVo/v-deo.html
How to explain about linear regression and linear regression in interviewer frequently asking this questions could you make the video
Also, are you ever available for questions? Answered by video of course. In the U.S.. we have language barriers unless one is of Asian or Indian descent.
Hi, yes I am. I am also looking into getting my videos transcribed to help people with non-native English to understand the videos better. Many thanks, Ben
Is this about log of odds, not log of odds ratio? I believe odds is different from odds ratio.
they are same i think
No. Logit is log odds. Odds ratio is a ratio of odds
this is not correct definition of odds ratio. p/1-p is odds, not odds ratio.
odds ratio is altogether a different thing than odds. When you have a categorical predictor variable X and target variable Y, odds ratio is the ratio of odds .
odds ratio=(odds of Y when X=1)/ (odds of Y when X=0)
Please research and correct me if I am wrong.
I am not sure I understand this correctly, specifically why your X is binary which is not necessary in Logit. Could you please review your comment and elaborate, if accurate?
how can we find beta0,beta1 values
Using maximum likelihood
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