"Regression analysis" is a statistical method used to examine the relationship between a dependent variable and one or more independent variables. It is employed to understand data patterns, make predictions, and assess the strength of relationships between variables. Typically, regression analysis is used to develop mathematical models that best describe the data, identify influencing factors, and quantify their impacts.

Typical software functions in the area of "regression analysis":

- Model Building: Development of mathematical models to describe data relationships.
- Variable Selection: Identification of relevant independent variables for analysis.
- Parameter Estimation: Estimation of coefficients and parameters of the regression model.
- Model Validation: Evaluation of the goodness-of-fit and significance of the developed model.
- Predictions: Forecasting future values of the dependent variable based on the model.
- Visualization: Graphical representation of results and model diagnostics.
- Interpretation: Interpretation of regression coefficients and their significance for understanding the data.

Examples of "regression analysis":

- Linear Regression: Examination of the linear relationship between a dependent and an independent variable.
- Logistic Regression: Prediction of a binary dependent variable based on independent variables.
- Polynomial Regression: Modeling a non-linear relationship between variables.
- Multiple Regression: Analysis of the relationship between a dependent variable and multiple independent variables simultaneously.
- Time Series Analysis: Forecasting future values based on historical data and trend analysis.
- Elasticity Analysis: Assessment of the sensitivity of a dependent variable to changes in an independent variable.

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