Difference between Dependent and Least Square Method
The least squares method is a form of mathematical regression analysis that finds the line of best fit for a dataset, providing a visual demonstration of the relationship between the data points. Each point of data is representative of the relationship between a known independent variable and an unknown dependent variable.
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 the least squares method, referred to as linear or ordinary, aims to create a straight line that minimizes the sum of the squares of the errors 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 the model.
This method of regression analysis begins with a set of data points to be graphed. An analyst using the least squares method will seek a line of best fit that explains the potential relationship between an independent variable and a dependent variable. In regression analysis, dependent variables are designated on the vertical Y axis and independent variables are designated 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.
This regression line in red is the best fit line that predict the sale of ice creams to best possible accuracy. One of the methods to draw this line is using the least squares method.
Least squares is one of the methods to find the best fit line for a dataset using linear regression. The most common application is to create a straight line that minimizes the sum of squares of the errors generated from the differences in the observed value and the value anticipated from the model. Least-squares problems fall into two categories: linear and non linear squares, depending on whether or not the residuals are linear in all unknowns.