Bivariate statical measures

Bivariate statical measures

1. correlation

   • The correlation refers to the fact that there is a link between several factors, one of the methods that allows us to verify this link is the correlation analysis.
     • degrees of correlation
       • strong correlation: The correlation between two variables is considered strong if the absolute value of r is greater than 0.75.
       • Weak correlation: The correlation between two variables is considered weak if the absolute value of r is between 0.25 and 0.5.
       • Null correlation: there is no relationship between the two variables. The correlation coefficient is equal to 0.
     • types of correlation
       • Direct correlation: this correlation occurs when the increase in one variable increases the other variable in the same way. the distribution line is increasing
       • Inverse correlation: it occurs when one of the variables increases and the other one decreases, therefore the distribution line in the point cloud is decreasing.
       • Non-existent correlation: it occurs when there is no dependent variable between variables and the other variable decreases, therefore the point cloud is rounded and the variables are determined uncorrelated.
     • Objectives
       • Indicate if there is a relationship between two variables
       • the correlation can give us information about the variables, you can see the degree of intensity in the relationship and whether it is positive or negative

2. Regression

   • analysis is a process or model that analyzes the link between a dependent variable and one or more independent variables. Thus, from this study, a mathematical relationship is found.
     • Objectives
       • allows examining the relationship between two or more variables and identifying which ones have the greatest impact on the object of study.
       • allows you to determine with confidence which factors are most important, which can be ignored and how they influence each other. These factors are called variables
     • Classification of variables
       • Dependent variables: The most important factor, which you are trying to understand or predict.
       • Independent variables: The factor that is thought to impact or modify the dependent variable.

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