Least Squares Criterion
Candlefocus EditorThe Least Squares Method is commonly used to analyze linear relationships in data sets. After loading data into a dataset, the sum of the squares of the differences of the values within the sample data is calculated. This sum is known as the residual sum of square (RSS). The regression line is then fit to the data, where the coefficients are obtained using the formula for the Least Squares Criterion.
The line that follows the data points most closely is determined by minimizing the RSS. The Least Squares Criterion is based on minimizing the RSS or sum of squares. The estimation of the least squares regression line is done by minimizing the sum of squares of the difference between the regression line and the actual data points. In this way, it determines the best-fit regression line for the given data points. Of course, the implications for this are that the line really accurately portrays the data that is specified. It also has implications for predicting data which may or may not have been observed yet.
In short, the Least Squares Criterion method is used to accurately portray the data points of a set of data and can be used to make estimates on how the data will behave in other situations. By minimizing the RSS, the Least Squares Criterion uses the regression line that fits the data points most accurately. This method is not only used in finance, economics, and investing, but is applicable in all situations where linear regression needs to be used. The Least Squares Criterion method gives users an approximate answer to their questions and gives them the ability to make predictions and effectively use data to their advantage.