What Is the Least Squares Method?
The least squares method is a form of mathematical regression analysis used to determine the line of best fit for a set of data, providing a visual demonstration of the relationship between the data points. Each point of data represents the relationship between a known independent variable and an unknown dependent variable.
- The least squares method is a statistical procedure to find the best fit for a set of data points by minimizing the sum of the offsets or residuals of points from the plotted curve.
- Least squares regression is used to predict the behavior of dependent variables.
- The least squares method provides the overall rationale for the placement of the line of best fit among the data points being studied.
Understanding the Least Squares Method
This method of regression analysis begins with a set of data points to be plotted on an x- and y-axis graph. An analyst using the least squares method will generate a line of best fit that explains the potential relationship between independent and dependent variables.
The least squares method provides the overall rationale for the placement of the line of best fit among the data points being studied. The most common application of this method, which is sometimes referred to as "linear" or "ordinary," aims to create a straight line that minimizes the sum of the squares of the errors that are generated by the results of the associated equations, such as the squared residuals resulting from differences in the observed value, and the value anticipated, based on that model.
The Line of Best Fit Equation
The line of best fit determined from the least squares method has an equation that tells the story of the relationship between the data points. Line of best fit equations may be determined by computer software models, which include a summary of outputs for analysis, where the coefficients and summary outputs explain the dependence of the variables being tested.
Least Squares Regression Line
If the data shows a leaner relationship between two variables, the line that best fits this linear relationship is known as a least-squares regression line, which minimizes the vertical distance from the data points to the regression line. The term “least squares” is used because it is the smallest sum of squares of errors, which is also called the "variance."
In regression analysis, dependent variables are illustrated on the vertical y-axis, while independent variables are illustrated on the horizontal x-axis. These designations will form the equation for the line of best fit, which is determined from the least squares method.
In contrast to a linear problem, a non-linear least-squares problem has no closed solution and is generally solved by iteration. Carl Friedrich Gauss claims to have first discovered the least-squares method in 1795—although the debate over who invented the method remains.
Example of the Least Squares Method
An example of the least squares method is an analyst who wishes to test the relationship between a company’s stock returns, and the returns of the index for which the stock is a component. In this example, the analyst seeks to test the dependence of the stock returns on the index returns.
To achieve this, all of the returns are plotted on a chart. The index returns are then designated as the independent variable, and the stock returns are the dependent variable. The line of best fit provides the analyst with coefficients explaining the level of dependence.
What Is the Least Squares Method?
The least squares method is a mathematical technique that allows the analyst to determine the best way of fitting a curve on top of a chart of data points. It is widely used to make scatter plots easier to interpret and is associated with regression analysis. These days, the least squares method can be used as part of most statistical software programs.
How Is the Least Squares Method Used in Finance?
The least squares method is used in a wide variety of fields, including finance and investing. For financial analysts, the method can help to quantify the relationship between two or more variables—such as a stock’s share price and its earnings per share (EPS). By performing this type of analysis investors often try to predict the future behavior of stock prices or other factors.
What Is an Example of the Least Squares Method?
To illustrate, consider the case of an investor considering whether to invest in a gold mining company. The investor might wish to know how sensitive the company’s stock price is to changes in the market price of gold. To study this, the investor could use the least squares method to trace the relationship between those two variables over time onto a scatter plot. This analysis could help the investor predict the degree to which the stock’s price would likely rise or fall for any given increase or decrease in the price of gold.