Multinominal logistic regression, Part 1: Introduction

This video introduces the method and when it should be used. It shows a simple example with one explanatory variable to illustrate how the method works and how the results can be interpreted using either odds ratios or predicted probabilities.
This video is part of NCRM Online Resource on Multinominal logistic regression by Dr Dr Heini Väisänen. To view the resource (which includes, slides, worksheet, data and reading list) visit www.ncrm.ac.uk/resources/online/
Please note: we may be unable to respond to individual questions on this video.
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[@tatianabarcenasbarreto6967]

Thanks for sharing this valuable knowledge with your clear and fantastic explanations.

[@Sanjeevpudasaini]

Thank you so much for such a good explanation!

[@KayYesYouTuber]

Good explanation of multinomial logistic regression.

[@Robin-zc2iw]

I'm doing a master's program in the US and my professor just explained this concept and I was so confused. Today's my test and this video makes my understanding of MN logistic regression so much better than it was. Thank you!

[@kaynkayn9870]

"Today's my test" - certified uni student moment

[@Willi_Zhang]

Thank you for this very helpful video!

[@f.181]

Thank you very much for the excellent presentation. Very good video!
I have a question. At 13:37: shouldn't it be "The odds of being *unemployed* rather than in employment are 42% lower for women than for men"?

[@asadyare3268]

You are right.

[@drisselghoufi6728]

Thank you🙏

[@samnangum2750]

Thank for sharing Dr

[@jakobforslin6301]

Thank you very much!

[@bismarkanloadey289]

hello, what if, instead of the dependent variable being more than 2, you have the explanatory variable rather to be more than 2. example; how sitting technique (upright, bent and curled) impacts the shape of the spinal cord. can you help with the impact model that'll be ideal for this analysis?

[@prempant6428]

What's the explanation for that equation on slide 13:26 ? The logit scale which is used first is ln(x/(1-x)) = y, if I am not wrong so x = e^y / (1 + e^y), you say that you've used the odd scale values but you used the logit scale values, during the calculation of the percentages ?

[@KyambaddeFrancis-ih8uk]

Thanks for the presentation, which values of x did you use

[@kaushikjayaram8556]

Thanks for sharing

[@dentoda]

Mlogit depvar indepvar, rrr gives OR output instead of coefficients

[@JiyongKim-et9sw]

Thanks !

[@clausvind8010]

The sliste at arounnd 13:22 have the same text for both bullets: I believe the second bullet should read "The odds of being unemployed rather than in employment are 42% lower for women than for men"

[@clausvind8010]

slide not sliste

[@shashidharchavan3895]

@time line 13.23 the second interpretation should be unemployment rather than in employment.

[@babaabba9348]

Mycket bra!

[@littlenuts779]

Why the numerator of pi3 is 1 as the reference category?