Bivariate statistical measures

Bivariate statistical measures

1. Analyze the relationship of two or more continuous variables

   • When the two variables are analyzed, this is known as a bivariate variable
     • Regression
       • It is a statistical technique that allows change in one of the variables, called response, and that corresponds to another variable called explciative.
         • Simple linear regression
           • It tries to explain the relationship that exists between the response variable Y and a single explanatory variable X.
         • Multiple regression
           • Allows you to work with a variable at the interval or ratio level. In the same way it is possible to analyze the relationship between two or more variables through equations
         • Logistic regression
           • used to predict the outcome of a categorical variable (a variable that can take on a limited number of categories) as a function of the independent or predictor variables
       • Regression lines: they are the lines that best fit the cloud of points (also called scatter diagram) generated by a binomial distribution.
   • Correlation: Indicates the strength and direction of a linear relationship and proportionality between two statistical variables.
     • It is considered that two quantitative variables are correlated when the values ​​of one of them vary systematically with respect to the homonymous values ​​of the other: If we have two variables, A and B, there is a correlation between them if, when the values ​​of A are reduced, the values ​​of A are also reduced. from B, and viceversa.