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What are the assumption of logistic regression?
Basic assumptions that must be met for logistic regression include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers.
Which is not an assumption of logistic regression?
Logistic regression does not make many of the key assumptions of linear regression and general linear models that are based on ordinary least squares algorithms – particularly regarding linearity, normality, homoscedasticity, and measurement level. Third, homoscedasticity is not required.
What are the main assumptions of logistic regression?
To begin, one of the main assumptions of logistic regression is the appropriate structure of the outcome variable. Binary logistic regression requires the dependent variable to be binary and ordinal logistic regression requires the dependent variable to be ordinal. ASSUMPTION OF OBSERVATION INDEPENDENCE
When to use specification error in logistic regression?
3.1 Specification Error. When we build a logistic regression model, we assume that the logit of the outcome variable is a linear combination of the independent variables. This involves two aspects, as we are dealing with the two sides of our logistic regression equation.
Do you need a large sample size for logistic regression?
Finally, logistic regression typically requires a large sample size. A general guideline is that you need at A general guideline is that you need at minimum of 10 cases with the least frequent outcome for each independent variable in your model.
What are true conditional probabilities in logistic regression?
The true conditional probabilities are a logistic function of the independent variables. No important variables are omitted. No extraneous variables are included. The independent variables are measured without error. The observations are independent.