The McGraw-Hill Series Economics essentials of economics brue, McConnell, and Flynn Essentials of Economics

repeated sampling
that is the basis of most of classical statistical inference,
as we shall see in Chapter 5. We shall provide several examples of Monte Carlo experi-
ments by way of exercises for classroom assignment. (See Exercise 3.27.)
24
In practice it is assumed that 
u
i
follows a certain probability distribution, say, normal, with certain
parameters (e.g., the mean and variance). Once the values of the parameters are specified, one can
easily generate the 
u
i 
using statistical packages.
Summary and
Conclusions
The important topics and concepts developed in this chapter can be summarized as follows.
1. The basic framework of regression analysis is the 
CLRM.
2. The CLRM is based on a set of assumptions.
3. Based on these assumptions, the least-squares estimators take on certain properties sum-
marized in the Gauss–Markov theorem, which states that in the class of linear unbiased
estimators, the least-squares estimators have minimum variance. In short, they are
BLUE.
4. The 
precision
of OLS estimators is measured by their 
standard errors.
In Chapters 4
and 5 we shall see how the standard errors enable one to draw inferences on the popula-
tion parameters, the 
β
coefficients.
5. The overall goodness of fit of the regression model is measured by the 
coefficient of
determination,
r
2
.
It tells what proportion of the variation in the dependent variable,
or regressand, is explained by the explanatory variable, or regressor. This 
r
2
lies between
0 and 1; the closer it is to 1, the better is the fit.
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Chapter 3
Two-Variable Regression Model: The Problem of Estimation