Bivariate statistical measures

Bivariate statistical measures

1. The bivariate description tries to study the relationship between pairs of measured attributes simultaneously on the same sample and comprises a set of tools that focuses on the analysis of two variables in order to determine the relationships empirical between them.

   • Correlation
     • It refers to a set of statistical relationships that involve a dependency between them. The study of Correlation is useful because it can indicate predictive relationships that can be used in practice
       • The sample correlation coefficient, r, quantifies the strength of the relationship. Correlations are also tested for statistical significance
         • Positive r values ​​indicate a positive correlation, in which the values ​​of both variables tend to increase together
         • Negative r values ​​indicate a negative correlation, in which the values ​​of one variable tend to increase while the values ​​of the other variable decrease.
   • Regression
     • allows to investigate the relationships between different quantitative variables. This, by formulating mathematical equations.
       • when only one independent variable is taken into account, we speak of simple linear regression. On the other hand, if more factors are included, it would be a multiple linear regression
         • simple regression
           • The simple linear regression model is characterized by predicting the dependent variable through the following equation: E(Y/x) = 0 + β1x
         • multiple regression
           • This method makes it possible to identify which independent variables are the ones that can explain an independent variable, check the causes and predict the values ​​approximately.