Hi Sasha,
To conceptually obtain the line of best to a set of data (xi,yi) using y=mx+b you assume a m and b and using the xi"s calculate the difference between the y of the line and the yi of the individual data points, You do this for all the remaining points. You then square the differences and add them up. This is called the sum of squares for obvious reasons and is a function of m and b. If you used a different m and b you would have a different sum of squares. The next step is to find the m and b that gives the smallest sum of squares. This can be done analytically using calculus or by using Solver in MS Excel. This m and b will position the line in the "center" of the data when plotted on a scatter plot. This line is called the line of best fit or the least squares line.
Hope this helps
Jim
Jim S.
tutor
Hi,
That's pretty easy all that needs to be done is to find the slope and intercept that minimizes the sum of squares. That's one sentence.
Jim
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04/18/15
Sasha B.
04/18/15