regression and correlation

regression and correlation

1. Correlation measures the degree of linear relationship between two variables.

   • it is characterized by
     • Range
       • It can take values ​​from -1 to 1
         • -1 indicates that the negative correlation is missing
           • that is to say
             • As one variable increases, the other decreases proportionally.
         • 1, indicates a perfect positive correlation
           • that is to say
             • If one variable increases, so does the other.
     • Address
       • It can be positive or negative
         • It does not imply a causal relationship.

2. seeks to model the relationship between an independent variable (x) and a dependent variable (Y)

   • It is characterized by
     • Linearity
       • It can be represented by a straight line
     • Causality
       • look for a causal relationship between the variables
     • Data setting
       • seeks to find the best straight line that fits the data
     • parameters
       • indicates how the dependent variable changes to the independent variable
     • prediction
       • allows you to make projections based on the model