# Statistics - Correlation (Coefficient analysis)

### Table of Contents

## About

Correlation is a statistical analysis used to measure and
describe the relationship between **two** variables.

The Correlations coefficient is a statistic and it can range between +1 and -1

- +1 is a perfect positive correlation. If the scores goes up for one variable the score goes up on the other.
- > 0.8 is a strong correlation
- > 0.4 is a high correlation
- > 0.2 correlate
- < 0.2 is not a strong correlation
- < 0.1 doesn't correlate
- 0 is no correlation (independence)
- -1 is a perfect negative correlation. If the scores goes up for one variable the score goes down on the other.

Correlation is used:

- mainly to describe relationship
- and then it's used for prediction that leads to regression because when two variables are correlated, then one variable can be used to predict the other variable. <note info>In other words, if the variables X and Y are correlated, a regression can be used to predict Y from X.</note>

If two variables are correlated, X and Y then a regression can be done in order to predict scores on Y from the scores on X.

Correlation demonstrates the relationship between two variables whereas regression provides an **equation** (with two or more variables) which is used to predict scores on an outcome variable.

Positive correlation only means that the univariate regression has a positive correlation. In a multiple regression, the sign (positive, negative) is dependent of the other variables.

## Articles Related

## Assumptions

## Correlation does not imply causation

Correlation does not imply causation but correlations are useful because they can be used to assess:

## Type

There are several types of correlation coefficients, for different variable types

- Pearson product-moment correlation coefficient ® (When both variables are continuous)
- Point bi-serial correlation (When 1 variable is continuous and 1 is dichotomous)
- Phi coefficient (When both variables are dichotomous)
- Spearman rank correlation (When both variables are ordinal)

## Venn diagrams

Venn diagrams representation of a correlation between two variables X and Y.

Venn diagrams representing:

- All the variants in X,
- All the variants in Y
- And the overlap (the covariance). The overlap is can also be expressed as:
- the sum of the cross product between x and y (ie the covariance definition)
- the sum of the square for the model.

The degree to which x and y correlate is represented by the degree to which these two variance circles overlap. The correlation (degree|coefficient) is the systematic variance in Y that's explained by X.

The correlation is approaching:

- one for an high degree of overlap
- zero for no overlap

The residual is the unexplained variance in Y. Some of the variance in Y is explained by the model. Some if it is unexplained, that's the residual.