At present, a line that fits the data well will be one for which then prediction errors (one for each of the n data points — n = 10, in this case) are as small as likely in some in the general sense. This plan is called the "least squares criterion." In tiny, the least squares criterion tells us that in order to find the equation of the best fitting line: y^i=a1+bxi.
