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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.
● 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
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.
Regression analysis has its origins in the 19th century, when Francis Galton investigated the concept of regression to the mean to describe biological phenomena. Since then, regression has become a central tool in many scientific, economic and social fields.
Regression is used in almost every field that requires data-based predictions and decision making. These include economics, finance, medicine, biology, engineering and more. The methodology makes it possible to learn from historical data and predict future trends or outcomes.
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