The Least Squares Method is a mathematical technique used to find the line of best fit for a set of data points. It minimizes the sum of the squared differences between the actual data points and the predicted values from the line.
The purpose of using the Least Squares Method is to find the best-fitting line for a set of data points in order to make predictions or draw conclusions about the relationship between the variables represented by the data.
The Least Squares Method is calculated by finding the slope and y-intercept of the line of best fit using a formula that takes into account the sum of the squared differences between the data points and the predicted values.
In simple linear regression, there is only one independent variable, while in multiple linear regression, there are multiple independent variables. The Least Squares Method can be used for both types of regression, but the calculations are more complex for multiple linear regression.
One limitation of the Least Squares Method is that it assumes a linear relationship between the variables, which may not always be the case. Additionally, it can be sensitive to outliers in the data, which can greatly affect the results. It also does not account for other factors that may influence the relationship between the variables.