Bivariate statistical measuress

Bivariate statistical measuress

1. Definition

   • Allow the joint analysis of two characteristics of the individuals of a population with the purpose of detecting possible relationships between them
   • The nature (nominal, ordinal or numerical) of the characteristics under study will determine the most appropriate tools for their analysis

2. Regression analysis

   • Definition
     • It is used to derive an equation that relates the criterion variable to one or more predictor variables
       • In this, the frequency distribution of the criterion variable is considered when one or more of the predictor variables are held fixed at various values
   • Models
     • Simple regression
       • When only one predictor variable is used
     • Multiple regression
       • When two or more variables are used
     • Logistic regression
       • Predict the outcome of a categorical variable
   • Regression problems
     • Correlated errors
     • Nonlinear relationship
     • Inhomogeneous variance

3. Correlation analysis

   • Definition
     • It measures the closeness of the relationship between two or more variables, considering the joint variation of the two measurements, neither of which is subject to restriction by the experimenter
   • Coefficients
     • of determination
       • term used in regression analysis to denote the relative proportion of the total variation in the criterion variable that can be explained by the fitted regression equation
     • of correlation
       • term used in regression analysis to designate the strength of the linear relationship between criterion and predictor variables.
       • Correlation coefficients are numbers ranging between +1 and -1
         • Its magnitude indicates the degree of association between the variables
           • If it is 0, it indicates that there is no relationship
           • If it is +1 or -1 they indicate a perfect positive or negative correlation respectively
             • Positive if increasing or decreasing the values ​​of the independent variable increase or decrease those of the dependent variable
             • Negative If increasing the values ​​of the independent variable decrease the values ​​of the dependent variable or vice versa

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