Response
The sum of the squared distances from each point to the line are as small as possible.
Predictor
Use to illustrate the relationship between a predictor and response variable and to see if your model fits the data. This line is a graphical representation of the mathematical regression equation. It is plotted using the least squares method which minimizes the sum of the squared distances between the points and the fitted line.
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Response |
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The sum of the squared distances from each point to the line are as small as possible. |
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Predictor |
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For example, you are studying the relationship between a particular machine setting and the amount of energy consumed. You suspect that this relationship has considerable curvature but you need to confirm and document this for your company. You produce the plots below.
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Energy |
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Machine Setting |
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A visual inspection of the linear model on the left reveals that the data do not fit the line. The log transformed quadratic model on the right appears to provide a good fit to the data.
