Bivariate statistical measures regression and correlation.

Bivariate statistical measures regression and correlation.

1. Regression and Correlation.

   • Graphically the scatter diagram or cloud of points allows obtaining information about the type of relationship between X and Y.
     • Correlation.
       • The purpose of mapping is to examine the direction and the strength of the association between two variables quantitative. Thus we will know the intensity of the relationship between them and if, by increasing the value of a variable, increases or decreases the value of the other variable.
     • Regression
       • The regression is aimed at describing how is the relationship between two variables X and Y.
       • Using regression techniques we invent a variable Ŷ as a function of another variable X (or vice versa).
       • The residuals or errors are the difference between the observed values ​​(true value of Y) and the values ​​predicted by the model: ei =Y-Ŷ.

2. Bivariate Statistics

   • Bivariate statistical techniques allow the joint analysis of two characteristics of the individuals in a population in order to detect possible relationships between them.
     • Dispersion diagram
     • Both Quantitative Variables
       • Linear correlation coefficient of Pearson
         • The percentage of variability of Y Explained by the variability in X is measured by the coefficient of determination that corresponds to r2
           • X: Score in a learning system y: Cost associated with achieving the score
       • inferential
         • H0: The variables are independent (variables not related)
         • Ha: Variables are not independent ( variables related)
     • Both Quantitative variables
       • Data table
       • Condensed code table
         • Frequency table or contingency table
           • Graphic representation
       • Questionnaire

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