This textbook makes learning the basic principles of econometrics easy for all undergraduate and graduate students of economics. It specifically caters to the syllabus of the Introductory Econometrics course taught in the third year of the Bachelor of Economics program in many universities.

It takes the readers step-by-step from introduction to understanding, first introducing the basic statistical tools like concepts of probability, statistical distributions, and hypothesis tests, and then going on to explain the two variable linear regression models along with certain additional tools like use of dummy variables, various data transformations amongst others.

The most innovative feature of this textbook is that it familiarizes students with the role of R, which is a flexible and popular programming language. With its help, the student will be able to implement a linear regression model and deal with the associated problems with substantial confidence.

Multiple Linear Regression

Multiple linear regression


In this chapter, we will study the multiple linear regression model. The regression model that we studied in Chapter 4 was a bivariate regression model, in that it took the form:

In this regression, there is just one independent variable, X. However, we might be interested in the independent effects on Y of more than one independent variable. For example, infant mortality might depend upon income and access to health facilities in addition to literacy. We might specify a more general linear regression model as:

where Yi is the ith observation on the dependent variable Y and Xi1,…….Xin are the ith observations on the independent variables X1,…….Xn respectively. eis are the stochastic disturbance terms that we have met in Chapter 4. We ...

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