Before You Model the Relationship between a Binary Dependent Variable and Independent Variable: 6 Logistic Regression Assumptions that You Must Know.

Logistic regression as per our last article, is a statistical process used to model the relationship between the probability of binary outcome of a dependent variable and one or more independent variables. Meaning that the dependent variable must be either 0 or 1 or 'life' or 'death' etc.

What is Logistics Regression

Logistic regression is a statistical process that models the relationship between a binary dependent variable and one or more independent variables. In predicting the likelihood of an outcome that is categorical, certain unique assumptions have to be satisfied to ensure that the outcome is both valid and reliable. In this article, we will discuss six logistic regression assumptions that are necessary to fitting the model to a dataset.

6 Logistic Regression Assumptions that You Must Known.

Binary Outcome: Logistic regression assumes that the outcome variable is binary. In essence, there is only one possible outcome out of two values. So a possible result could be either “yes” or “no”, “male” or “female” or “spam” and “not spam”. Cases where the outcome variable consists of more than two possible values require different models. In effect, logistics regression is only appropriate in cases where the dependent variable is a label.

Independence of Observations: The assumption is that the observed data are independent of each other. Examples include time series or longitudinal data, where observations are recorded over time for the same individuals. The outcome variable for one observation may impact the outcome of the previous observation, violating the assumption of independence since the independent variables appear to be too highly correlated with each other.

Linearity of Independent Variables and Log Odds: Logistic regression assumes a linear relationship between the independent variables and the log odds of the dependent variable. In effect, the extent to which independent variables impact dependent variables persists across all independent variables.

No Multicollinearity: Logistic regression assumes little or no multicollinearity among the independent variables. When two or more independent variables are exceedingly correlated with each other, multicollinearity occurs, which can result in risky estimates of the regression coefficients.

Large Sample Size: Logistic regression assumes the sample size is sufficiently large. A general rule of thumb is that there should be at least ten observations for each independent variable in the model. It ensures that the estimates of the regression coefficients are stable and reliable.

No Outliers: Logistic regression assumes that there are no highly influential outlier data points, as they can distort the outcome and accuracy of the model.

Conclusion

To summarize, logistic regression is a powerful statistical approach that models the relationship between a binary dependent variable and one or more independent variables. It is essential to ensure that the assumptions of logistic regression are in order to guarantee the validity and reliability of the results.

These assumptions include binary outcome, independence of observations, linearity of independent variables and log odds, no multicollinearity, large sample size, and no outliers. By adhering to these assumptions, data professionals can use logistic regression to make informed decisions and draw meaningful conclusions from their data.