Statistics for Economists

Statistics for Economists
Introduction to matrix algebra and statistics in preparation for regression analysis. Other topics include: probability, random variables, density and distribution functions, estimation, hypothesis testing.
 Hours3.0 Credit, 3.0 Lecture, 0.0 Lab
 PrerequisitesECON 110 & MATH 112
 TaughtFall, Winter, Spring, Summer
 ProgramsContaining ECON 378
Course Outcomes: 

Econ 378 students will be able to:

  1. Demonstrate an understanding of the basic principles and terminology of probability, simple distribution theory, statistics, and matrix algebra in preparation for a successful introduction to econometric methods in Economics 388. These principles and concepts include:

The language, intuition, and notation necessary to understand basic approaches to probability, including basic combinatoric counting techniques, the additive and multiplicative laws of probability, conditional probability and independence, the law of total probability, and Bayes' rule, and to solve basic probability problems using these techniques.

Random variables, both univariate and bivariate, including: distribution functions, density functions, conditional density, expected values, variance, covariance, and correlation coefficients. Specific examples of useful random variables.

Statistics and sampling distributions.

Law of Large Numbers and Central Limit Theorem.

Parameter estimators and their derivation using the Method of Moments and Method of Maximum Likelihood.

Statistical inference, including the idea of sampling error, sampling distributions, the construction of confidence intervals, and formal hypothesis tests.

Basic matrix theory and the properties of matrix operations, including addition, subtraction, matrix products, matrix determinants, and matrix inversion.

  1. Use these statistical techniques to analyze data in diverse applications.
  2. Recognize the usefulness of statistics in a variety of career fields.