What is Logistic Regression?

Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary).  Like all regression analyses, the logistic regression is a predictive analysis.  Logistic regression explains the relationship between a binary dependent variable and one or more independent variables.

Logistic regressions can be hard to interpret, but Intellectus Statistics simplifies the analysis and provides clear explanations.

Types of Questions Binary Logistic Regression Can Answer

How does lung cancer probability change with each pound of weight and pack of cigarettes smoked?

Do body weight, calorie intake, fat intake, and age affect the probability of having a heart attack?

Binary Logistic Regression Major Assumptions

1. The dependent variable should be dichotomous in nature (e.g., presence vs. absent).
2. You should assess the data for outliers by converting the continuous predictors to standardized scores and removing values below -3.29 or greater than 3.29.
3. There should be no high correlations (multicollinearity) among the predictors.  You can assess this by creating a correlation matrix among the predictors. Tabachnick and Fidell (2013) suggest that the assumption holds as long as the correlation coefficients among independent variables are less than 0.90.

At the center of the logistic regression analysis is the task estimating the log odds of an event.  Mathematically, logistic regression estimates a multiple linear regression function defined as:

logit(p)

for i = 1…n .