Bivariate statistical measures of regression and correlation

Bivariate statistical measures of regression and correlation

1. Analyze the relationship of two or more variables continue

2. A REGRESSION analysis results in a mathematical equation describing the relationship between two or more variables.

   • SIMPLE LINEAR REGRESSION: It tries to explain the relationship that exists between an explanatory variable "x" and a response variable "y". Its expression is: y= mx+b+e, where m is the slope, b the intercept and e the error.
   • MULTIPLE REGRESSION: It allows working with a variable at the interval level, it also allows regression with more than two variables by means of equations
   • LOGISTIC REGRESSION: It is used to predict the outcome of a categorical variable based on the independent variables or predictors.

3. A CORRELATION analysis produces a number that summarizes the degree of correlation between two variables.

   • LINEAR COEFFICIENT OF CORRELATION: it is the specific measure that quantifies the intensity of the linear relationship between two variables in a correlation analysis. In correlation reports, this coefficient is symbolized by r.
   • GRADE OF CORRELATION
     • It can be a strong correlation when the points on the line are closer together.
     • The correlation will be weak the more the points on the line are separated.
     • It will be null when there is no relationship between the variables.
   • KIND OF CORRELATION
     • It can be direct, reverse or null.