REGRESSION

It is a tool of frequency, use of statistics that allows to investigate the relationships between different dependent quantitative variables, regression analysis is a process or model that analyzes the link between a dependent variable and one or more independent variables

OBJETIVE

Regression analysis is to construct a function to stimulate the future value of the study variable

Regression allows calculating a conditional average hope

TIPES OF VARIABLES

Independent or regressive variables or variables

They are used as predictors or are confounding variables that

Outcome or variable dependent or variable response

Attributes on which we want to measure changes or make predictions

CORRELATION

It refers to that there is a link between several events one of the tools that allow us to infer if such a link exists is precisely the correlation analysis

OBJETIVE

We indicate if the relation exists between two events, that is to say variables a little about the nature of said relation and force

The correlation can say something about the relationship between variables, it is used to understand: if the relationship is positive or negative

CORRELATION RATES

Direct correlation: Occurs when when doubling one of the variables the other increases the line corresponding to the distribution point cloud on a growing line

Inverse correlation: Given when increasing one of the variables the other decreases- The line corresponding to the nuve of distribution points is a dependent line

Zero correlation: It occurs when there is no dependence of any kind between the variables. in this case it is said that the variables are icoraded and the point cloud has a rounded shape

CORRELATION COEFFICIENT

Statistical correlation is measured by what is called a correlation coefficient. its numerical value varies from 1.0 to -1.0 indicates the strength of the ralacion. In general r>0 indicates the relation and r<1 indicates a negative relation, while r=0 indicates that there is no relation (or that the variables are independent and unrelated. Here r=1.0 describes a perfect positive corelacion and r=-1 describes a perfect negative

CORRELATION DEGREES

strong correlation: will be stronger when near the points of the line

Weak correlation: The correlation will be weak when more points are separated from the line

No correlation: there is no pattern or relationship between them

Subtopic

determination R2

The coefficient of determination is the proportion of the total variance of the variable explained by the regression. The coefficient of determination, also called R square, reflects the goodness of the adjustment of a model to the variable to be explained.

It is important to know that the result of the coefficient of determination ranges between 0 and 1. The closer to 1 its value, the greater the adjustment of the model to the variable we are trying to explain. Conversely, the closer to zero, the less tight the model will be and therefore less reliable.