**Download link : Pearson Correlation Coefficient**

**Meaning :**

The Pearson's Correlation Coefficient is used to determine whether there is a significant linear relationship or association between two variables.

**Examples :**

- Are years of education completed related to income?
- Is motivational level related to achievement?
- Is employee satisfaction related to profit?
- Is employee attrition related to employees salary?

**Assumptions :**

- The variables must be either interval or ratio measurements
- There is a linear relationship between the two variables
- The variables must be approximately normally distributed

**Interpretation :**

It indicates whether or not a significant linear relationship exists between two variables. It is denoted by

**r**. It can take a range of values from +1 to -1.

Strength of Association | Positive | Negative |

Low | 0.1 to 0.3 | -0.1 to –0.3 |

Moderate | 0.3 to 0.5 | -0.3 to –0.5 |

High | 0.5 to 1 | -0.5 to -1 |

- A value of 0 indicates that there is no association between the two variables.
- A value greater than 0 indicates a positive association; that is, as the value of one variable increases, so does the value of the other variable.
- A value less than 0 indicates a negative association; that is, as the value of one variable increases, the value of the other variable decreases.

**Important Points :**

- The two variables can be measured in entirely different units.
- It does not mean that for every unit increase in one variable there is a unit increase in another.
- It does not indicate what kind of relationship it is.

The relationship can be of any of the following types :

**1. Causal relationship :**Two variables do indeed affect each other.

**2. Spurious relationship :**Statistical relation is caused by a third variable.

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