The line of best fit is also known as the least square line. It uses a statistical technique to determine the line that fits best through a series of scattered data (plots). Using regression analysis, it finds the line that minimizes the amount of errors (deviations - the sum of vertical distance of data points from the line. The result is a unique line that minimizes the total squared deviations, statistically termed the sum of squared errors.
Yes but phrased differently
The line of best fit does not have to start from 0.
The line that minimized the sum of the squares of the diffences of each point from the line is the line of best fit.
A line of best-fit.
Because the "best fit" line is usually required to be a straight line, but the data points are not all on one straight line. (If they were, then the best-fit line would be a real no-brainer.)
The line of best fit is the best possible answer you can get from raw data. They also can be used to make predictions.
Finding the line of best fit is called linear regression.
The line of best fit does not have to pass through the 0 (origin) and rarely does
A straight line equation
A best-fit line is the straight line which most accurately represents a set of data/points. It is defined as the line that is the smallest average distance from the data/points. Refer to the related links for an illustration of a best fit line.
Check out the related links section below to see an example of a line of best fit.