bivariate statistical measures

bivariate statistical measures

1. Analyze the relationship of two or more variables continue

   • When you analyze the two variables, this is known as a bivariate variable
     • regression
       • It is a statistical technique that allows us to change in one of the variables called response and which corresponds to another known as explanatory variables
         • simple linear regression
           • We try to apply the relationship that exists between the response variable Y and a single variable explanatory X
             • expression Y=a + bx +
         • multiple regression
           • Allows you to work with a variable at the interval or ratio level. of the 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) based on the independent variables or predictive
   • correlation
     • Indicates the strength and direction of a linear relationship and proportionality between two variables statistics. Two quantitative variables are considered to be correlated when the values ​​of one of them vary systematically with respect to the homonymous values ​​of the others
       • If we have two variables (A and B) there is a correlation between them if at decrease the values ​​of A so do those of B and vice versa

2. Regression lines are the lines that best fit the point cloud (also called scatter diagram) generated by a binomial tribution

3. The coefficient of determination is the proportion of the total variance of the variable explained by the regression. It is also called R squared and serves to reflect the goodness of fit of a model to the variable that is intended explain

4. The correlation coefficient r is the specific measure that quantifies the strength of the linear relationship between two variables in an analysis of correlation

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