BIVARIATE STATISCAL MEASURES

BIVARIATE STATISCAL MEASURES

1. REGRESSION

   • REGRESSION ANALYSIS
     • Mathematical equation that allows describing the relationship between two variables.
       • Integration of a random component in the relationship.
       • Relationship between the variants.
       • Condition of the dependent and independent variable.
       • It can be expressed as:
   • It explains the behavior of a variable and predicts the value of one variable based on the values given by the other.
   • REGRESSION MEASURES
     • Multiple Regression:
       • It works with two independent variables, related to each other.
         • The expression of the function is varied:
         • Regression model: Y=β0+β1X1+***+βpXp+∈
         • Analysis: F-Test, R Square, T-Test, Beta Coefficient.
         • Degree of variation between (x) and (y), indicating in the cloud of points if it is increasing or not.
     • Simple Linear Regression:
       • Existential relationship between the variable (y) and (x).
       • Example: The simple linear regression model has the expression; And X=+ + αβ∈,
       • Point cloud adjustment mode.

2. CORRELATION

   • CORRELATION ANALYSIS
     • Statistical technique used to measure the closeness of the linear relationship between two or more variables on an interval scale.
   • Establishes the relationship or dependency that exists between the two variables involved in a two-dimensional distribution. Determine if changes in one of the variables influence changes in the other.
     • Correlation types.
       • Inverse correlation.
       • Direct correlation.
       • Null correlation.
     • Degrees of correlation
       • Strong correlation.
       • Weak correlation.
       • Null correlation.
     • Positive correlation
       • The two variables are correlated in a direct (linear) sense.
     • Negative correlation
       • The two variables are correlated inversely (linear relationship with negative slope)

3. Bivariate statistical measures allow the analysis of sets of two characteristics of individuals in a population in order to detect possible relationships between them.

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