Assumptions of logistics regression The main assumptions of logistic… — Research-ሪሰርች(ጁፒተር-Jupiter)

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Assumptions of logistics regression

The main assumptions of logistic regressions are the following:

👉 For Binary dependent variable, the outcome variable must be binary (e.g., 0/1, Yes/No, Success/Failure).

👉 Independence of observations: Each observation should be independent of the others.
👉 Linearity of independent variables with the logit: The relationship between continuous predictors and the log-odds (logit) of the outcome must be linear. This does not mean the predictors must be linearly related to the outcome itself.
👉 multicollinearity among independent variables: Independent variables should not be highly correlated with each other. High multicollinearity inflates standard errors and weakens inference.
👉 Large sample size: Logistic regression requires a sufficiently large sample for stable estimates.
👉 Absence of strongly influential outliers: Extreme values can unduly influence model estimates.

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