Do independent variables in logistic regression need to be binary?
Logistic regression is an extension of simple linear regression. There must be two or more independent variables, or predictors, for a logistic regression. The IVs, or predictors, can be continuous (interval/ratio) or categorical (ordinal/nominal).
Why is linear regression not good for a binary dependent variable?
With binary data the variance is a function of the mean, and in particular is not constant as the mean changes. This violates one of the standard linear regression assumptions that the variance of the residual errors is constant.
When do you use binary logistic regression in statistics?
Logistic regression is the statistical technique used to predict the relationship between predictors (our independent variables) and a predicted variable (the dependent variable) where the dependent variable is binary (e.g., sex, response, score, etc…). There must be two or more independent variables, or predictors, for a logistic regression.
Can you use binary regression with simple linear regression?
Binary Logistic Regression Logistic regression is an extension of simple linear regression. Where the dependent variable is dichotomous or binary in nature, we cannot use simple linear regression.
What should you do if a dependent variable is binary?
If the dependent variable is binary, you should performe a logistic regression. The assumption is absence of collinearity between the indipendent ones. You should test if the indipendent variables have low correlation each other.
How many predictors are needed for a logistic regression?
There must be two or more independent variables, or predictors, for a logistic regression. The IVs, or predictors, can be continuous (interval/ratio) or categorical (ordinal/nominal). All predictor variables are tested in one block to assess their predictive ability while controlling for the effects of other predictors in the model.