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To properly dive into the least squares regression line<\/strong> concept, first, we need to understand what regression analysis is. Regression analysis is simply a method of estimating the relationships between a dependant variable and a single or multiple independent variables. <\/p>\n\n\n\n Linear regression is the simplest form of regression method where we supposedly have a linear relationship between our independent and dependent variables. Hence, it is one of the simplest methods of developing a machine learning model to predict a value of an unknown variable with their linear relationship to the independent variable\/s. <\/p>\n\n\n\n Our focus is to understand the least squares regression<\/strong> and how to draw a least squares regression line<\/strong>. So we have to proceed with linear regression in mind. As a rule of thumb, a line is drawn when there is a linear relationship. You will understand what this means as you progress through this article.<\/p>\n\n\n\n Linear regression is used in machine learning to model linear relationships between a dependent variable and a single or multiple independent variables. Hence we have simple linear regression<\/strong> and multiple linear regression<\/strong> where multiple independent variables are considered.<\/p>\n\n\n\n The method is a widely used technique in regression analysis, hence in machine learning regression models as well. The least-squares regression technique for linear regression is a mathematical method of finding the best fit line that represents the relationship between the independent and corresponding dependent variable. When we mention the best fit<\/strong>, we are referring to minimize the errors (differences between real and anticipated values) as much as possible.<\/p>\n\n\n\n As I just explained the widely used application of least squares regression is the “linear<\/strong>” or “ordinary<\/strong>” method which is used for linear regression analysis. Its goal is to create a straight line that intends to minimize the total of the squares of the errors that are generated by the equations we use to generate the line. To calculate and minimize the errors, things like squared residuals (which we calculate by the differences in our predicted values and the real values) are considered based on our model.<\/p>\n\n\n\n Great! Now we have already started to embrace the core of this blog topic. The line of best fit<\/strong> drawn between two sets of variables by making the total of the squares of the individual errors<\/strong> as small as possible can be simply called the Least Squares Regression Line<\/strong>. This mathematical method to reduce the error is also known as the ordinary least squares method<\/strong>.<\/p>\n\n\n\n Are all these complicated mathematical terms starting to bothering you much? Let’s try to understand the concept by using a simple example. Shall we? \ud83d\ude42<\/p>\n\n\n\n Imagine that we’ve got a data set of a bunch of student grades. So, we’ve got many variables in that data set, including Grade 3<\/strong> data (G3<\/strong>) which are the students’ final grades<\/strong>, and Grade 2<\/strong> data (G2<\/strong>) which are their second-period grades<\/strong>. If we have to model the relationship between G2 <\/strong>and G3 <\/strong>in order to predict G3<\/strong> by using new G2 <\/strong>data, what should be the preferred method of doing so?<\/p>\n\n\n\n Let’s just plot the G2 vs G3 data on a scatter plot to see if they have a linear relationship or not.<\/p>\n\n\n\nLinear Regression in Machine Learning<\/h2>\n\n\n\n
What is Least Squares Regression & Line of Best Fit?<\/h2>\n\n\n\n
So what is Least Squares Regression Line<\/strong>? <\/h2>\n\n\n\n
Least Squares Regression Line Mathematical Example<\/h2>\n\n\n\n