Lineare Regression: Einfach erklärt

Linear regression analysis is used to create a model that describes the relationship between a dependent variable and one or more independent variables. Depending on whether there are one or more independent variables, a distinction is made between simple and multiple linear regression analysis. In the first case, simple linear regression, the aim is to examine the influence of an independent variable on a dependent variable. In the second case, multiple linear regression, the influence of several independent variables on a dependent variable is analyzed. In both cases, the prerequisites are that the variable is interval-scaled and that the distribution is normal. Simple Linear Regression The goal of simple linear regression is to predict the value of a dependent variable based on an independent variable. The greater the linear relationship between the independent and dependent variables, the more accurate the prediction. This also means that a greater proportion of the variance of the dependent variable can be explained by the independent variable. The relationship between variables can be visually represented in a scattergram. The greater the linear relationship between the dependent and independent variables, the more closely the data points lie on a straight line. Multiple Linear Regression In contrast to simple linear regression, multiple linear regression allows for the consideration of more than two independent variables. The goal is to estimate a variable based on several other variables. The variable to be estimated is called the dependent variable (criterion). The variables used for prediction are called independent variables (predictors). More information: https://numiqo.com/tutorial/lineare-r... Online Statistics Calculator: https://numiqo.com/statistik-rechner/...