Probability and Statistics > Statistics Definitions > What is a Regression Equation?

## Regression Equation: Overview

A regression equation is used in stats to find out what relationship, if any, exists between sets of data. For example, if you measure a child’s height every year you might find that they grow about 3 inches a year. That trend (growing three inches a year) can be modeled with a regression equation. In fact, most things in the real world (from gas prices to hurricanes) can be modeled with some kind of equation; it allows us to predict future events.

## What is a Regression Equation used For?

Regression equations can help you figure out if your data can be fit to an equation. This is extremely useful if you want to make predictions from your data–either future predictions or indications of past behavior. For example, you might want to know what your savings are going to be worth in the future. Or, you might want to predict how long it can take to recover from an illness.

There are several types of regression equations. Some of the more common include exponential and simple linear Regression (to fit the data to an exponential equation or an linear equation). In elementary statistics, the regression equation you are most likely to come across is the linear form.

## Calculating Linear Regression

You can find several articles on this site for help with calculating linear regression equations (click the link):

- Find a linear regression equation (by hand or Excel).
- How to Perform TI89 Regression.
- TI83 Linear Regression.

The linear regression equation is shown below.

## Downside of Regression Analysis

In order to make data fit an equation, you have to figure out what general pattern the data fits first. The general steps to performing regression include first making a scatter plot and then making a guess as to what kind of equation might be the best fit. Then you can select the best regression equation for the job.

However, as the following image shows, it isn’t always completely straightforward to select the appropriate regression equation, especially when you’re dealing with real life data. Sometimes you get “noisy” data that doesn’t seem to quite fit any equation. If the bulk of the data looks like it follows a pattern, you *could* omit the outliers. In fact, if you ignore outliers, the data looks like it could be modeled by an exponential equation.

To: Regression Analysis Index

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