Bivariate measures

Bivariate measures

1. Bivariate statistical techniques allow the joint analysis of two characteristics of individuals in a population in order to detect possible relationships between them.

   • understands:
     • Dispersion diagram
       • It uses Cartesian coordinates to show the values of two variables for a data set.
         • Tomado de: http://el-estudihambre.over-blog.com/2018/01/6.diagrama-de-dispersion.html
       • it is characterized by using one or more variables as well as showing whether the correlations between them can be positive or negative.
       • The scatter plot allows you to study the relationships between two associated sets of data that appear in pairs (for example, (x,y), one from each set).
     • Correlation
       • It infers about the proportionality and the linear relationship that exists between the variables, it also indicates the strength and direction of a linear relationship.
         • Degree of correlation
           • Positive correlation
             • it is when the two variables are correlated in a forward direction (linear).
           • negative correlation
             • means that the two variables are correlated in an inverse direction (linear relationship with a negative slope).
           • null correlation
             • There is no kind of relationship or pattern between them.
               • Tomado de: https://www.maximaformacion.es/blog-dat/que-es-la-correlacion-estadistica-y-como-interpretarla/
     • Regression
       • Statistical technique used to derive an equation that relates a criterion variable to one or more predictor variables.
         • Simple Regression
           • This type occurs when an independent variable influences another dependent variable. Example: Y = f(x)
             • Shapes of the regression model
               • The simple linear regression equation indicates that the mean value or expected value of y is a linear function of x: E(y/x) = β0 + β1 x.
       • It studies the strength of the association through a measure of association called the correlation coefficient.
         • Multiple Regression
           • This type occurs when two or more independent variables influence a dependent variable. Example: Y = f(x, w, z).
             • Shapes of the regression model
       • https://economipedia.com/definiciones/modelo-de-regresion.html

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