how to do OLS estimation

OLS (Ordinary Least Squares) estimation is a statistical method used to estimate the parameters of a linear regression model. Here are the steps to perform OLS estimation:

Define the model: Specify the dependent variable and the independent variables in the linear regression model. For example, if you want to estimate the relationship between a person's age and their income, age would be the independent variable and income would be the dependent variable.
Collect data: Collect data on both the independent and dependent variables for a sample of individuals.
Check assumptions: Before performing OLS estimation, you need to check the assumptions of the linear regression model. These assumptions include linearity, normality, homoscedasticity, and independence. If any of these assumptions are violated, corrections may need to be made before performing OLS estimation.
Estimate the coefficients: Use OLS estimation to estimate the coefficients (beta values) of the regression equation. The OLS method minimizes the sum of the squared errors between the predicted values and the actual values.
Test the coefficients: Test the statistical significance of the estimated coefficients using hypothesis testing. The null hypothesis is that the coefficient is equal to zero, indicating no relationship between the independent variable and the dependent variable.
Evaluate the model: Evaluate the model's fit by analyzing the R-squared value, which measures the proportion of the variation in the dependent variable explained by the independent variables.
Interpret the results: Interpret the estimated coefficients and their statistical significance in the context of the problem being studied.