In elementary statistics, the regression equation you are most likely to come across is the linear form. Some of the more common include exponential and simple linear Regression (to fit the data to an exponential equation or a linear equation). There are several types of regression equations. Or, you might want to predict how long it can take to recover from an illness. For example, you might want to know what your savings are going to be worth in the future. This is extremely useful if you want to make predictions from your data–either future predictions or indications of past behavior. Regression equations can help you figure out if your data can be fit to an equation. According to this particular regression line, it actually is predicted to happen sometime in 2018: Having a negative rainfall doesn’t make too much sense, but you can say that rainfall is going to drop to 0 inches sometime before 2020. If you wanted to predict what would happen in 2020, you could plug it into the equation: The first chart above goes from 1995 to 2015. Regression is useful as it allows you to make predictions about data. Polynomial regression results in a curved line. The curved shape of this line is as a result of polynomial regression, which fits the points to a polynomial equation.
In this next image, the dots fall on the line. In the above image, the dots are slightly scattered around the line.
It’s not very common to have all the data points actually fall on the regression line. That means that if you graphed the equation -2.2923x + 4624.4, the line would be a rough approximation for your data. The regression line is represented by an equation. In linear regression, the regression line is a perfectly straight line: It’s like an average of where all the points line up. You basically draw a line that best represents the data points. A regression line is the “best fit” line for your data.
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