Bivariate statistical measures

Bivariate statistical measures

1. Bivariate statistical techniques allow the joint analysis of two characteristics of individuals in a population with the purpose of detecting possible relationships between them.

   • Correlation analysis
     • Statistical technique used to measure the closeness of the linear relationship between two or more variables on an interval scale.
       • example
         • techniques could show some correlation of high income with the tendency to take winter vacations in Caribbean islands, although it would be a mistake to assume that high income is a cause of a person traveling south when temperatures plummet environmental.
     • The correlation coefficient r is a unitless value between -1 and 1. Statistical significance is indicated by a p-value. Therefore, correlations are usually written with two key numbers: r = and p = .
   • regression analysis
     • Statistical technique used to derive an equation that relates a criterion variable with one or more predictor variables; when uses only one predictor variable, it is simple regression analysis, and if two or more are used, it is multiple regression analysis.
       • Term used in regression analysis to denote the proportion of the total variation in the criterion variable that can be explained by the fitted regression equation.
     • Simple linear regression model
       • y = β0 + β1 x + ε
     • Simple Linear Regression Equation
       • E(y/x) = β0 + β1 x  1 >0  1 < 0 ( µY/x=E(Y/x) )

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