ECO3431 Analysis of Economic Data (online)

Course Description

This course provides basic skills in graphing and analyzing economic data. The first two blocks of the course are composed of extensive coverage of probability and statistics that is necessary to understand the theory and practice of regression analysis. The third block of the course is devoted entirely to regression analysis. Some of the concepts discussed in the second and third block of the course are illustrated with widely-used statistics and econometrics software giving the student the opportunity to learn the application of some of the concepts discussed in class to economics data.

The course is composed of six modules. Each module will require two to three weeks to complete and consists of reading assignments, instruction videos, homework assignments, and quizzes. For each module, students should plan on spending 4 to 8 hours on the reading assignments, 2 to 4 hours for the videos, 5 to 10 hours for the homework, and 1 hour for each of the quizzes. Comments and questions on the module materials should be directed to the TA’s by email or through the discussion board on the course web page. In addition, there are two midterm exams and a final exam. All homework, quizzes, and exams will be implemented through Pearson MyEconLab during a specifically timed window. You will not be required to use a testing center.

Learning Objectives

At the completion of this course, students will be able to:

1. list the basic concepts of probability theory, the rules of probability, conditional probabilities, and statistical independence of events;
2. work with both discrete and continuous random variables, and use the probability distribution or density function as well as the cumulative distribution function to calculate probabilities;
3. explain the mean and variance of a random variable and to calculate the mean and variance of a discrete random variable;
4. describe the concepts of random sampling, sampling distributions, and the Central Limit Theorem, and will be able to find the sampling distribution of the sample mean;
5. explain what is a point estimator and a confidence-interval estimator, what are the desirable properties of an estimator, and to find point and confidence-interval estimators of the population mean;
6. explain the foundations of hypothesis testing, types I and II errors, size of a test, the p-value, and will be able to perform hypothesis testing for the population mean;
7. interpret scatter plots, covariance, and sample correlation;
8. describe the linear regression model, estimation of the linear regression model by the method of ordinary least squares (OLS), the sampling distribution of the OLS estimator, and confidence intervals and hypothesis testing in the linear regression framework;
9. interpret the output from estimation and hypothesis testing from widely-used statistics and econometrics software.