Line of best fit
- Linear Regression
Explore different lines of linear fit and the error between the prediction and observation, using "sum of errors", "absolute sum of errors", and "sum of squared errors" as criteria for best fit. You can change the sliders 'a' (slope) and 'b' (y-intercept), to affect the linear fit line, until you think it's "the best fit", and then display the system generated line. The graphs and table should help you see both qualitative and quantitative differences between the various criteria.
There are other ways to define "best fit", and depending on the data points and the context, linear lines are not always suitable or "the best fit".