Multivariate logistic regression
Multivariate logistic regression is a type of data analysis that predicts outcomes based on multiple independent variables.[1]
Types
The two main types of multivariate logistic regression are linear regression and logistic regression.
Linear regression
Linear regression produces results that show a linear relationship with a single independent variable (IV) and can be plotted on a graph as a straight line.[2]
Logistic regression
In contrast, logistic regression produces results that show a nonlinear relationship. As a result, plotting the data on a graph produces a curved line called a sigmoid. Unlike linear regression, logistic regression produces results based on two or more independent variables.[3]
There are three main types of logistic regression dependent variables (DVs): Binary, multi-class, and ordinal.[4]
Binary
A binary dependent variable is a variable with only two outcomes, and the possible values must be opposites of each other.[5]
Multi-class
A multi-class dependent variable is a variable with at least three qualitative (non-numerical) outcomes, usually with a constant numerical stand-in.[6]
Ordinal
An ordinal dependent variable is a variable with at least three possible outcomes, which are numerically different.[7]
Artificial intelligence
Multivariate logistic regressions are also used in machine learning.[8]
References
- ^ "Multivariate logistic regression is a type of analysis that can help predict results when you're working with multiple variables." - [1] (Indeed)
- ^ "Linear regression has a continuous set of results that can easily be mapped on a graph as a straight line." - [2] (Indeed)
- ^ "Logistic regressions are non-linear and are portrayed on a graph with a curved shape called a sigmoid. Instead of a continuous set of results, a logistical regression has two or more categories for data." - [3] (Indeed)
- ^ "Logistic regression includes three basic types: ..." - [4] (Indeed)
- ^ "A binary output is a variable where there are only two possible outcomes. These outcomes must be opposite of each other and mutually exclusive." - [5] (Indeed)
- ^ "A multi-class has three or more categories without any numerical value, though they usually have a numerical stand-in for datasets." - [6] (Indeed)
- ^ "An ordinal output also has three or more categories, though they're in a ranked output." - [7] (Indeed)
- ^ "This is a common classification algorithm used in data science and machine learning." - [8] (Indeed)