The purpose of using least square is to find the best possible mathematical model that fits a given set of data points. This is achieved by minimizing the sum of the squared differences between the actual data points and the predicted values from the model.
The least square method works by calculating the sum of the squared differences between the actual data points and the predicted values from a mathematical model. The model is then adjusted iteratively until the sum of the squared differences is minimized, resulting in the best fit for the data.
Least square can be used to fit a wide range of data, including linear, polynomial, exponential, and logarithmic functions. It can also be used for both continuous and discrete data sets.
One advantage of using least square is that it provides a systematic and objective approach to finding the best fit for a given set of data points. It also allows for the identification of outliers and the evaluation of the goodness of fit for different models.
While least square is a powerful tool for fitting data, it does have some limitations. It assumes that the data is normally distributed and that there is a linear relationship between the variables. Additionally, it may not work well for data sets with a large number of variables or when the data is highly skewed.