Least-Squares Linear Regression:
• Linear regression basically means finding the ( equation for the ) straight line that is the closest “fit” to a set of ( x, y ) data points.
• Least-squares is a means of identifying the “best” fit between the line and the data. For each data point, the distance between the point and the line is calculated and squared, and the total of these squared distances is added up over all the data points. The straight line that yields the minimum value for this sum of squares is defined as the best fit.
• For this assignment you will also calculate the correlation coefficient, r, which is a metric for
how well the line fits the data, and if the correlation is positive or negative. This value always
lies between -1.0 and 1.0 inclusive. The closer it is to 1.0, the stronger the positive correlation;
The closer to -1.0 the stronger the negative correlation; and if the coefficient is close to zero,
then it shows a weak correlation ( if any ) between x and y.