Binary Logistic Regression Analysis Assumptions. The outcome is a binary or dichotomous variable like yes vs no positive vs negative 1 vs 0. If any of these six assumptions are not met you might not be able to analyse your data using a binomial logistic regression because you might not get a valid result.
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Your dependent variable should be measured on a dichotomous scale. There should be no outliers in the data which can be assessed by converting the continuous predictors to. Assumptions for a Logistic regression.
The logistic regression equation expresses the multiple linear regression equation in logarithmic terms and thereby overcomes the problem of violating the linearity assumption.
Assumption Linear regression assumes linear relationships between variables. In a normal logistic regression there is always a dependent variable Y and a set of independent variables Xs that can be dichotomous quantitative or a combination of both. We are going to use the 10 binary data to estimate the effects of a number of covariates of interest on the probability that an individual fish used the Stillwater Branch for migration in each year of this study using logistic regression. However some other assumptions still apply.
