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 linear Regression (to find out if the data fits a linear equation). In elementary statistics, the regression equation you are most likely to come across is the linear form.
You can find several articles on this site for help with calculating linear regression equations (click the link):
- Find a linear regression (by hand or Excel).
- How to Perform TI89 Regression.
- TI83 Linear Regression.
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 patter, you could omit the outliers (What are 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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