Multivariate logistic regression is a type of data analysis that predicts any number of[1] outcomes based on multiple[2][3] independent variables.[4][5][6] It is based on the assumption that the natural logarithm of the odds has a linear relationship with independent variables.[7]
- ^ "... while multivariate logistic regression deals with multiple outcomes simultaneously." - [1] (This vs. That)
- ^ "In contrast, multivariate logistic regression involves analyzing the relationship between multiple outcome variables and one or more predictors. This can lead to more complex interpretations, as researchers must consider the impact of predictors on each outcome variable." - [2] (This vs. That)
- ^ "Number of variables: One dependent (y), At least two independent (x)" - [3] (Ylva B. Almquist)
- ^ "Multivariate logistic regression is a type of analysis that can help predict results when you're working with multiple variables." - [4] (Indeed)
- ^ Sperandei, Sandro (2014). "Understanding logistic regression analysis". Biochemia Medica. 24 (1): 12–18. doi:10.11613/BM.2014.003. ISSN 1330-0962. PMC 3936971. PMID 24627710.
- ^ "Use multiple logistic regression when you have one nominal variable and two or more measurement variables, and you want to know how the measurement variables affect the nominal variable." - [5] (Handbook of Biological Statistics)
- ^ "The multiple logistic regression equation is based on the premise that the natural log of odds (logit) is linearly related to independent variables." - [6] (Springer)
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