Overview

Description

CALENDAR DESCRIPTION:

An introduction to the use and interpretation of statistical analysis in the context of data typical of economic applications. Students with a minimum grade of A- in BUEC 232 or STAT 270 can take BUEC 333 after 30 units. Students seeking permission to enrol based on their BUEC 232 or STAT 270 grade must contact the Undergraduate Advisor in Economics. Students with credit for ECON/COMM 236 may not take this course for further credit. Quantitative.

COURSE DETAILS:

The course will introduce you to the statistical analysis of economic data, also called econometrics. We will focus on linear regression, which is by far the most common method for analyzing the relationship between two or more variables: we will emphasize both the use and interpretation of linear regression methods. Topics covered in this course include: hypothesis testing, specification, multicollinearity, serial correlation and heteroskedasticity. You will also get some experience using the statistical software R, and establish a foundation for further econometric study. 


  Additional important information:

  

• There will be regular graded assignments consisting of both exam-type questions and computer exercises. The assignments will be posted on the course webpage. You are expected to work independently on the assignments. Cheating of any kind will result in at least a failing grade in the course.

 

• The computer exercises will involve some real econometric analysisusing the statistical software R: R can be downloaded for free to use on your own computer; it is also currently installed on the lab computers. The use of R is essential and it is strongly suggested that you become familiar with the software before the start of the semester. A good place to start: follow the steps described at http://swirlstats.com/students.html

• The exam-type exercises will involve statistical computations and derivations. I encourage you to prepare for BUEC 333 by reviewing material from your introductory statistics course:  e.g. probability distributions, sampling distributions, hypothesis testing. 

• Details regardinglab hours will be announced during the first week of class. It is your responsibility to make yourself available for the lab hours to get extra help on the computer exercises.

•  It is your responsibility to check the course webpage regularly for up-to-date information regarding assignments, deadlines and reading materials. 

Grading

Materials

REQUIRED READING: