Regression analysis is the process of making predictions of some variable, based on the relationship between this dependent variable and an independent variable (or set of variables). It is a scientist's task to find a model or an equation to make such predictions possible.

Single linear regression assumes a linear relationship between two factors: a dependent factor (y) and an independent factor (x). By using the linear equation y=a ₁x+a ₀, you can derive, estimate, determine, or predict the dependent factor (y) from the independent factor (x).

Figure 4.1 explains a bit of the terminology used in connection with linear regression. Let's assume that there is a linear relationship between hemoglobin percentage and the erythrocyte count in human blood. You determine what the independent factor is and plot that variable on the x axis. The linear equation y=a ₁x+a ₀ uses a slope (a ₁) and an intercept (a ₀). Thanks to this model, you can predict, determine, or estimate y based on x.

Figure 4.1

The actual, observed values are usually clustered around the linear regression line. RSQ or R ² is a "measure...