Bivariate Statistical Measurements

Bivariate Statistical Measurements

1. Regression

   • It is a technique that forecasts or predicts the value of one variable based on the given values of the other.
     • Simple linear regression model
       • It is one of the most widely used techniques that allows modeling a reaction between two sets of variables.
     • When two variables are involved, one (X) will be the independent variable, while the other (Y) will be the dependent variable.
       • Thus, we speak of a regression of Y on (or as a function of) X.
     • It allows you to determine with confidence which factors are most important, which can be ignored and how they influence each other.

2. Correlation

   • Statistical technique that measures the closeness of the linear relationship between two or more variables.
     • It is calculated with the correlation coefficients.
       • Numbers varying between +1 and -1
     • Types of correlation
       • direct
         • The line corresponding to the point cloud of the distribution is an "increasing line".
         • When one of the variables increases, the other increases.
       • inverse
         • The line corresponding to the cloud of points of the "crescent line" distribution.
         • When one of the variables increases, the other decreases.
       • null
         • The line corresponding to the point cloud of the distribution is the "crescent line".
         • when there is no dependence of any kind between variables
     • Dependent variable: variable that is predicted or estimated: it is shown on the Y-axis.

3. Allow the joint analysis and observation of two variables in a sample of individuals in a population.

   • Its purpose is to detect possible relationships between them.
   • The regression (and correlation) will be simple if there is only one explanatory variable; on the contrary, it will be multiple if there are several explanatory variables.

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