Bivariate statistical measures

Bivariate statistical measures

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 the mapping is to examine the direction and the strength of association between two variables quantitative. This is how their intensity of relationship is known, and if increasing the value of a variable increases or decreases the value of the other variable
       • Regression
         • The regression takes care of describe the relationship between two variables X and Y.
           • Using regression techniques we invent a variable Y as function of another variable X (or vice versa)
             • Ý = f(X), Y-Ý = mistake
               • The residuals or errors ei are the difference between the observed values (true value of Y) and the values predicted by the model ei = Y-Ý.

2. Bivariate Statistics

   • Bivariate statistical techniques allow the joint analysis of two characteristics of the individuals in a population for the purpose of identify possible relationships between them
     • Both quantitative variables
       • Inferential
         • Ho: The variables are independent (unrelated variables)
         • Ha: The variables are not independent (related variables)
       • Dispersion diagram
       • Pearson's correlation coefficient
         • 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
     • Both Quantitative variables
       • Questionnaire
       • Condensed Code Table
         • Frequency table or contingency table
           • Graphic representation
       • Data table

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