Assumptions of logistic regression.

- First, binary logistic regression requires the dependent variable to be binary, and ordinal logistic regression requires the dependent variable to be ordinal.
- Second, logistic regression requires observations to be independent of each other. In other words, the observations should not come from repeated measurements or matched data.
- Third, logistic regression requires there to be little or no multicollinearity among the independent variables. This means that the independent variables should not be too highly correlated with each other.
- Fourth, logistic regression assumes the linearity of independent variables and log odds. although this analysis does not require the dependent and independent variables to be related linearly, it requires that the independent variables are linearly related to the log odds.
- Finally, logistic regression typically requires a large sample size.

Some of the assumptions for logistic:

- The outcome is a binary or dichotomous variable like yes vs no, positive vs negative, 1 vs 0.
- There is a linear relationship between the logit of the outcome and each predictor variables. Recall that the logit function is
`logit(p) = log(p/(1-p))`

, where p is the probabilities of the outcome (see Chapter @ref(logistic-regression)). - There is no influential values (extreme values or outliers) in the continuous predictors
- There is no high intercorrelations (i.e. multicollinearity) among the predictors.