![]() ![]() 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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