Binary logistic regression: introduction (video 1 of 3)

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This video introduces the method and discusses how it differs from linear regression. It shows a simple example with one explanatory variable to illustrate how the method works and how the results can be interpreted using either log-odds, odds or predicted probabilities.

Please note: we may be unable to respond to individual questions on this video.

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00:00 Intro
01:11 Binary Response
03:55 Why we can't use linear regression for binary outcomes
06:21 Make a regression model work for a binary outcome
07:50 Visual explanation of logit link function
10:28 Characteristics of logits and probabilities
12:07 Example#1: what is the prob. of a normal birth weight
14:04 Interpretation of fitted logistic regression model
18:02 How to report results

mehmetaliozer

Thanks very much for this, you made it look so easy

jumashafara

Hi Dr Heini Väisänen, this is by far the best ever binary logistic regression explanation I've ever come across. Thank you so much.

henry

Thank you for this great video!! At the beginning you mentioned that the EV can be on an ordinal scale too. However, is that totally correct? Because is it correct to interpret the beta coefficient (e.g., a unit increase in X increases the log odds by beta units) when the one-unit steps at the EV are not the same for each EV category (as common for ordinal-scaled variables)? For me, this makes only sense if you treat it as a dummy-coded variable (with one reference category) as you did in the video, but not, if you just put it in as a "normal" covariate (because then it is treated as continous).

manuelleitner

well explained. please explain how to calculate probability for women

vidyadharshinikalaimonyrab

I was hanging around all my day until i came across to your videos. Very well explained with plain English. May I say thank you very much?

faridhusen

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