Regression Analysis: An introduction to Linear and Logistic Regression
Regression analysis in statistics makes it possible to estimate relationships between variables. Regressions are calculated if, starting from one or more variables, a conclusion is to be drawn about another variable. The variable to which the conclusion is to be drawn is called a dependent variable (criterion). The variables used for prediction are called independent variables (predictors). This results in two areas of application for regression:
Measuring the influence of one or more variables on another variable
Prediction of a variable by one or more other variables.
Types of regression analysis
Regression analyses are divided into simple linear regression, multiple linear regression and logistic regression. Which regression analysis is used depends on the number of independent variables and the scale of measurement of the dependent variable.
If you only want to use one variable for prediction, a simple regression is used. If you use more than one variable, this is multiple regression. If the dependent variable is nominally scaled, a logistic regression must be used. If it is metrically scaled, a linear regression is used. Whether linear or non-linear regression is used depends on whether or not there is a linear relationship between the independent variables and the dependent variable.
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Regression Analysis: An introduction to Linear and Logistic Regression
• Regression Analysis: An introduction ...
Simple and Multiple Linear Regression
• Simple and Multiple Linear Regression
Assumptions of Linear Regression
• Assumptions of Linear Regression
Logistic Regression: An Introduction
• Logistic Regression: An Introduction
Dummy Variables in Multiple Regression
• Dummy Variables in Multiple Regression
Regression with categorical independent variables
• Regression with categorical independe...
Multicollinearity
• Multicollinearity (in Regression Anal...
Causality, Correlation and Regression
• Causality, Correlation and Regression
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