Regression

Regression is a statistical method for analyzing the relationship between one or more independent variables and a dependent variable. In this module, you will learn to make predictions about the dependent variable by finding a function that best explains the measured data.
It is used in various fields such as economics, medicine and engineering and is based on assumptions such as the linearity of the relationship and the homoscedasticity of the residuals.

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Contents

  • Natural scattering
  • Non-natural scattering
  • Stability tests
  • Correlation vs. regression
  • Forms of correlation
  • Correlation coefficient
  • Regression analysis
  • Coefficient of determination (RQD)
  • Regression models
  • Linear regression
  • Quadratic regression
  • Cubic regression
  • Residual diagrams
  • Forecasts
  • Target value optimization
  • Overfitting
  • Residual vs. adjustment
  • Application in practice
  • Duration:

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Key information

Regression is a statistical tool used to model the relationship between a dependent variable and one or more independent variables. Its main goal is to understand this relationship and make predictions about future observations based on it. Through regression, we can recognize patterns, understand trends and even investigate causalities between variables. It is a flexible and versatile analytical tool used in various fields such as economics, social sciences, medicine and engineering.

Beneftits

  • Predicting the relationships between variables
  • Flexible application
  • Identify the strength of influence of the variables

Risks

  • Risk of overfitting
  • Very good statistical knowledge required
  • Ignoring non-linear relationships
  • Misleading results

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