Table of Contents

**What is Correlation?**

Correlation is a statistical measure that describes the relationship between two variables. It is used to measure the strength of the linear relationship between two variables.

The variables can be anything that can be measured, such as height and weight, IQ scores, etc. Correlation can be positive, negative, or zero.

It is a value between -1 and 1, where -1 indicates a perfect negative relationship, 0 indicates no relationship, and 1 indicates a perfect positive relationship.

As one variable increases, that means it is a positive correlation, so the other variable also increases. When the other variable decrease, it means that one variable increase causing negative correlation. When there is no relationship between the two variables, it is called zero correlation.

Correlations are used in a variety of fields, including science, statistics, finance, and psychology.

**What is Regression?**

Regression is a statistical method used to predict the value of a dependent variable (often called a ‘target’ or ‘outcome’) based on the values of one or more independent variables (often called ‘predictors’ or ‘covariates’).

The independent variables can be categorical (e.g., sex, race, treatment group) or continuous (e.g., age, income, hours of sleep). The dependent variable can be continuous (e.g., height, weight, IQ) or categorical (e.g., success/failure, pass/fail).

There are many different types of regression, but the most common is linear regression, which models the relationship between the dependent variable and the independent variables as a linear equation.

Other types of regression include logistic regression, Poisson regression, and Cox regression.

Regression is a powerful tool for understanding the relationships between variables

**Difference Between Correlation and Regression**

- Correlation is a statistical measure that evaluates the strength of a linear relationship between two variables. Regression is a statistical technique that is used to predict the value of a dependent variable based on the value of an independent variable.
- Correlation measures the strength of the relationship between two variables, while regression is used to predict the value of one variable based on the value of another.
- Correlation is a measure of how two variables are related to one another. Regression is a statistical technique that is used to predict the future behaviour of a variable based on its past behaviour.
- X and Y are random variables in correlation, on the other hand in regression, X is the random one and Y is the fixed variable.
- The range of relationship in a correlation lies between -1 and +1, whereas in regression, the regression value is an absolute figure.

**Comparison Table Between Correlation and Regression**

Parameters of Comparison | Correlation | Regression |

Purpose | Description, inferential, statistics | Prediction, designed, experiments |

Variable | Studies the linear relationship between the variables | Studies the linear and nonlinear relationship between the variables. |

Prediction | Correlation does not make predictions | Regression analysis enable us to make predictions |

Indication | The extent to which two variables move together | The impact of a unit change in the known variable on the estimated value |

Cause and effect | Does not address, assume cause and do not effect relationship | Attempts to show the cause and effect relationship |