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Logistic Regression
This is a method in statistics applied when the dependent variable is binary or dichotomous. This type of regression is a predictive type of analysis just like other regression analyses. A logistic regression describes data and aims to explain the link between a dependent dichotomous variable and one or multiple independent variables that are ordinal, ratio-level, or interval. Intellectus Statistics Tool allows researchers to easily carry out a logistic regression and interpret the output in plain English.
Probit Regression
Probit regression is performed for variables with binary outcomes. So what are binary outcome variables? Well, these types of variables have two possibilities. For example, an answer can be yes or no, a test result can be positive or negative. The words probability and unit were coined to form the term probit. This model predicts the likelihood that a value will land into either of the two possible outcomes.
Non-linear Regression
In non-linear regression, the data fitted is modeled and expressed as a math function. From your elementary statistics class, you probably learned that linear regression associates the x and y variables with a straight line. Non-linear regression, on the other hand, relates the two variables, x, and y in a curved relationship. A non-linear regression model aims to make the sum of squares as minimum as possible.
Robust Regression
Robust regression is a technique that can be used in the place of least squares regression. It is often used for data that is full of outliers and to detect influential observations. Robust regression can be used as a substitute for least squared regression. Meaning, you can use it in any place you would apply a least-squares analysis.