What is the line of best fit? Why do we want the sum of the residuals to be as close to zero as possible?

Step-by-step explanation:

What sis line of best fit?

The line of best fit may be explained as a straight line which is drawn to pass through a set of plotted data point which gives the best and most approximate relationship between the data points. A line of best fit is required to give the best approximate value between the set of plotted data points such that it allows making inference on new data points while also ensuring the least possible deviation from the original data points.

Why do we want the sum of the residuals to be as close to zero as possible?

The line of best fit will be the line which gives the least value of residual error. The residual error is reffered to as the difference between the line drawn and the individual data point plotted. These errors are squared and summed together, the line which produces the least residual error is Considered as the leading ne of best fit for the data.

We want the sum of our residual error to be as close to zero as possible, this is to reduce the deviation between our original or plotted data and the modeled data produced by our line of best fit.

example 2 – simplify: step 1: completely factor both the numerators and denominators of all fractions. step 2: change the division sign to a multiplication sign and flip (or reciprocate) the fraction after the division sign; essential you need to multiply by the reciprocal. step 3 : cancel or reduce the fractions.

your answer is

a.

5

step-by-step explanation: