State the principle of ordinary least square (OLS)?

The principle of ordinary least squares (OLS) is a method used in statistical regression analysis to estimate the unknown parameters in a linear regression model. It works by minimizing the sum of the squared differences between the observed values and the values predicted by the model. This method assumes that the errors in the model are normally distributed and that the relationship between the independent and dependent variables is linear.

The principle of Ordinary Least Squares (OLS) is to find the line that best fits the given data points, by minimizing the sum of the squared residuals.

The residual is the vertical distance between the data point and the line. The OLS principle finds the line that has the smallest possible sum of the squared residuals. This means that the line is as close as possible to all of the data points.