Final answer:
The least squares method identifies the linear equation that best fits a set of ordered pairs. It minimizes the sum of squared errors and results in a line that can be used to predict future outcomes within the data range.
Step-by-step explanation:
The least squares method identifies the linear equation that best fits a set of ordered pairs. This method is part of linear regression, a statistical process used to create a line (ý = a + bx) that best fits the data points on a scatter plot. The least squares method aims to minimize the sum of the squared errors (SSE) between the actual data points and the estimated values predicted by the linear equation. This best-fit line is useful as it allows us to predict outcomes for the dependent variable based on the independent variable, although predictions should only be made within the range covered by the data.
When calculating the regression line, we also determine the correlation coefficient (represented by 'r'), which indicates the strength and direction of the relationship between the independent variable (x) and the dependent variable (y).