Estimation and Testing the Quotient of Two Models

Abstract: In the thesis, we introduce linear regression models such as Simple Linear Regression, Multiple Regression, and Polynomial Regression. We explain basic methods of the model parameters estimation, Ordinary Least Squares (OLS) and Maximum Likelihood Estimation (MLE). The properties of the estimates, and what assumptions need to be made for the model for the estimates to be the Best Linear Unbiased Estimates (BLUE) are given. The basic Bootstrap methods are introduced. The real world problem is simulated in order to see how measurement error affects the quotient of two estimated models.