Least Square Method Graph
Let us have a look at how the data points and the line of best fit obtained from the least squares method look when plotted on a graph.
The red points in the above plot represent the data points for the sample data available. Independent variables are plotted as x-coordinates and dependent ones are plotted as y-coordinates. The equation of the line of best fit obtained from the least squares method is plotted as the red line in the graph.
We can conclude from the above graph that how the least squares method helps us to find a line that best fits the given data points and hence can be used to make further predictions about the value of the dependent variable where it is not known initially.
Limitations of the Least Square Method
The least squares method assumes that the data is evenly distributed and doesn’t contain any outliers for deriving a line of best fit. But, this method doesn’t provide accurate results for unevenly distributed data or for data containing outliers.
Check: Least Square 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.