Regression Analysis

1. Linear regression can be done by hand or with the use of computer programs. A linear regression analysis generates a graph with a best-fit regression line through the data. A series of statistics will also be supplied, and typically includes the following:
a. Slope of the line, with the equation of the form y = mx + b, where the slope of the line is m.
b. Intercept of the line, which is equal to b in the line’s equation. The intercept is the value of y where the line crosses the y-axis.
c. Coefficient of determination, r². This number, which is between 0 and 1, is a measure of how well the data fits the line. A result of r² = 1, indicates the line is a perfect fit, but as r² gets decreases, the fit becomes increasingly poor, resulting in less accurate predictions based on the line.
d. Confidence interval is often 95 percent. This is a range of values around one or more of the previous statistics. You can be 95 percent certain that the true value of a given statistic is within the provided range.