Bivariate statistical measures

Bivariate statistical measures

1. Bivariate statistical measures of regression and correlation are tools used to analyze the relationship between two variables.

2. Regression focuses on predicting one variable (called the dependent variable) as a function of another variable (called the independent variable) via a line of best fit. Provides an equation that allows estimating values ​​of the dependent variable from the independent variable.

   • Both measures are useful for understanding and quantifying relationships between two variables, allowing informed decisions in various fields, such as scientific research, economics, and data analysis.

3. Regression

   • The term regression was originally used by Galton to indicate certain relationships in the theory of biological inheritance, although it has later come to mean the statistical method developed to investigate such relationships.
     • The regression analysis gives rise to a mathematical equation that allows us to describe the relationship between two variables. That is, to obtain an "ideal" line known as the regression line, which describes the relationship or dependency between two variables.

4. correlation

   • The correlation analysis describes the degree or strength with which this relationship occurs, for this we will use a measure known as correlation coefficients or Pearson correlation.
     • Just as the correlation analysis allows to measure the strength of the association between two variables, the regression analysis allows the prediction, that is, the estimation of a value or average of a so-called dependent variable, based on a value or average supposedly known for the other. variable, called independent.

5. It must be clear that the regression analysis, in addition to explaining the relationship between two variables, cause and effect, tells us if the mathematical relationship can be linear, parabolic, exponential, etc., also allows us to estimate the values ​​of a variable, assuming a known value of the other variable.

6. Referencias bibliográficas: Martínez, C. (2011). Distribuciones de frecuencia. En: Estadística y muestreo. 13a ed. [Online]. Ecoe Ediciones. (pp 590 – 677).https://elibro-net.bibliotecavirtual.unad.edu.co/es/ereader/unad/131880?page=31