Bivariate statistical measures

Bivariate statistical measures

1. They are representative values ​​of a data collection, synthesizing the information of the total data in a few values.

   • Regression
     • Linear regression
       • Makes it possible to predict the behavior of a variable (dependent or predicted) from from another (independent or predictor).
         • Linear coefficient of determination
           • explains a fitted regression, with total variation
         • Simple Linear Regression
           • is derived from the equation
             • Regression line of Y on X
             • Regression line of X on Y
         • Regression analysis model
           • computes the estimated relationship between a dependent variable and one or more explanatory variables
             • deterministic
             • Statistical
             • standardized
         • Linear regression coefficient
           • Result of a multiple analysis, showing the average change of a variable
     • multiple regression
       • predicts the value of the dependent variable or to evaluate the influence that predictors have on it
         • Linear
           • it is restricted to the estimation of linear models
         • Curvilinear
           • Attempts to fit a curve instead of a straight line
         • Non-linear
           • not based on a simple proportional relationship between cause and effect

2. They allow the joint analysis of two characteristics of the individuals of a population with the purpose of detecting possible relationships between them.

   • Correlation
     • indicates the strength and direction of a linear relationship and proportionality between two statistical variables.
       • Linear correlation coefficient
         • Lies between the covariance and the product
           • Variables
             • predictive
             • criterion
       • Types
         • Direct correlation
           • occurs when increasing one of the variables increases the other.
         • Inverse Correlation
           • occurs when one of the variables increases and the other decreases
         • Null correlation
           • occurs when there is no dependency of any kind between the variables
       • Degrees
         • Strong correlation
           • when the points are closer to the line
         • Weak correlation
           • when the points are farther from the line
         • Null Correlation
           • There is no pattern or relationship between them
       • Correlation analysis
         • indicates if there is a relationship between variables
           • Measure closeness
           • interval scale

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