What is the Least Square Method?
The Least Squares Method is used to derive a generalized linear equation between two variables, one of which is independent and the other dependent on the former. The value of the independent variable is represented as the x-coordinate and that of the dependent variable is represented as the y-coordinate in a 2D cartesian coordinate system. Initially, known values are marked on a plot. The plot obtained at this point is called a scatter plot. Then, we try to represent all the marked points as a straight line or a linear equation. The equation of such a line is obtained with the help of the least squares method. This is done to get the value of the dependent variable for an independent variable for which the value was initially unknown. This helps us to fill in the missing points in a data table or forecast the data. The method is discussed in detail as follows.
Least Square Method Definition
The least-squares method can be defined as a statistical method that is used to find the equation of the line of best fit related to the given data. This method is called so as it aims at reducing the sum of squares of deviations as much as possible. The line obtained from such a method is called a regression line.
Least Square Method
Least Square Method: In statistics, when we have data in the form of data points that can be represented on a cartesian plane by taking one of the variables as the independent variable represented as the x-coordinate and the other one as the dependent variable represented as the y-coordinate, it is called scatter data. This data might not be useful in making interpretations or predicting the values of the dependent variable for the independent variable where it is initially unknown. So, we try to get an equation of a line that fits best to the given data points with the help of the Least Square Method.
In this article, we will learn the least square method, its formula, graph, and solved examples on it.