The least-squares regression method is a technique commonly used in Regression Analysis. It is a mathematical method used to
find the best fit line that represents the relationship between an independent and dependent variable.
The least squares regression method works by minimizing the sum of the square of the errors as small as possible, hence the name least squares.
Basically the distance between the line of best fit and the error must be minimized as much as possible. This is the basic idea behind the least
squares regression method.
A few things to keep in mind before implementing the least squares regression method is:
1. The data must be free of outliers because they might lead to a biased and wrongful line of best fit.
2. The line of best fit can be drawn iteratively until you get a line with the minimum possible squares of errors.
3. This method works well even with non-linear data.
KEY TAKEAWAYS :
1. The least squares method is a statistical procedure to find the best fit for a set of data points by minimizing the sum of the offsets or
residuals of points from the plotted curve.
2. Least squares regression is used to predict the behavior of dependent variables.