Multinomial logistic regression, Part 2: Multiple multinomial regression

This video discusses multinomial logistic regression models with more than one explanatory variable. It shows how the results can be interpreted either as odds ratios or predicted probabilities. It also introduces principles of model selection, including the use of Wald-tests and likelihood ratio tests.
This video is part of NCRM Online Resource on Multinominal logistic regression by 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.
The National Centre for Research Methods (NCRM) delivers research methods training through short courses and free online resources.
- Visit the NCRM website: www.ncrm.ac.uk
- Browse our short courses: www.ncrm.ac.uk/training/ncrm-...
- Find online resources: www.ncrm.ac.uk/resources
Follow NCRM on social media:
- Twitter: / ncrmuk
- LinkedIn: / ncrmuk
- Facebook: / ncrmuk
- KZbin: / ncrmuk
Subscribe to the NCRM monthly newsletter: www.ncrm.ac.uk/news/subscribe

[@ricardoortega3266]

Excellent thank you, greetings from México.

[@shwetabijla]

how to find predictive prob for age? and interpretation

[@supradg]

What would you do if you have to calculate the predicted probability of employment for a female, married and aged 25-44 years for example? Your baseline category is male so I guess if its female you would add that coefficient to the numerator for the last three equations?

[@alessandrorosati969]

If i have teo categories for dependent variable i can use multinomial logistic regression?

[@adekunlerukayat7136]

I think you will have to use binary logistic regression

[@user-ko9dn2ve9e]

1) What's about overdispersion? 2) ... age - continious var?