Assumptions of Logistic Regression

Firstly, it needn’t bother with a direct relationship between the predictor and explanatory variables. Logistic regression can deal with a wide range of connections, since it applies a non-straight log change to the anticipated chances proportion. The error terms (the residuals) don’t need to be multivariate normally distributed. Homoscedasticity is not required. Logistic regression does not require differences to be heteroscedastic for every level of the explanatory variables. In conclusion, it can deal with ordinal and nominal information as explanatory variables. The explanatory variables don’t need to be metric (interim or proportion scaled).
Binary logistic regression requires the dependent variable to be binary (0, 1) and ordinal logistic regression requires the predictor variable to be ordinal. Lessening an ordinal or even metric variable to dichotomous level loses a great deal of data, which makes this test second rate contrasted with ordinal logistic regression in these cases.
Furthermore, since logistic regression expect that P(Y=1) is the likelihood of the occasion happening, it is important that the predictor variable is coded likewise. That is, for a binary regression, the element level 1 of the predictor variable ought to speak to the sought result.