# What is Regression: A Statistical Analysis Guide

## What is Regression?

Regression is a statistical method used in finance, investing, and other disciplines that attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables). The term regression was coined by Francis Galton in the 19th century to describe a biological phenomenon. The phenomenon was that the heights of descendants of tall ancestors tend to regress down towards a normal average (a phenomenon also known as regression toward the mean).

## Uses of Regression

Regression techniques have become one of the most commonly used statistical methods. They are used to model relationships between variables and are particularly useful when a variable (the dependent variable) is believed to be influenced by one or more variables (the independent variables). The use of regression is widespread in predicting and forecasting, where its use has substantial overlap with the field of machine learning.

### Regression in Finance

In finance, regression analysis is used to understand which factors influence the performance of stocks and bonds, and to forecast future performance. For example, a financial analyst may use regression to determine whether stock market performance can be predicted based on the performance of certain sectors or based on the performance of the economy as a whole.

## How Regression Works

Regression works by estimating the relationship between the dependent variable and the independent variables. The simplest form of regression, linear regression, estimates this relationship as a straight line. The equation for this line is Y = a + bX, where Y is the dependent variable, X is the independent variable, and a and b are constants. The constant a is the intercept of the line, and b is the slope. The regression equation is used to predict the value of Y based on X.

### Types of Regression

There are several types of regression, each with its own specific use cases and assumptions. Here are a few examples:

• Linear Regression: This is the simplest form of regression, where the relationship between the dependent and independent variables is assumed to be linear.
• Logistic Regression: This is used when the dependent variable is binary (i.e., it can take only two values, like 0 and 1).
• Polynomial Regression: This is used when the relationship between the dependent and independent variables is best modeled by an nth degree polynomial.

Each type of regression has its own assumptions about the data, and it’s important to understand these assumptions before using a particular type of regression.

## Conclusion

Regression is a powerful statistical tool that can provide valuable insights into the relationships between variables. It is widely used in finance, economics, and other fields to model and predict outcomes. Understanding the basics of regression and its various types can help you choose the right tool for your data analysis needs.