Multinomial logistic regression: Difference between revisions

Revision as of 23:59, 15 March 2024

Revision as of 14:37, 19 May 2024

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===As a set of independent binary regressions===

To arrive at the multinomial logit model, one can imagine, for ''K'' possible outcomes, running ''K''-1 independent binary logistic regression models, in which one outcome is chosen as a "pivot" and then the other ''K''-1 outcomes are separately regressed against the pivot outcome. If outcome ''K'' (the last outcome) is chosen as the pivot, the ''K''-1 regression equations are:

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