I. Course Description
“An introduction to probability and statistical methods for empirical work in economics. Probability, random variables, sampling, descriptive statistics, probability distributions, estimation and hypotheses testing, introduction to the regression model. Economic data sources, economic applications, and the use of statistical software packages will be emphasized.”
The goal of the course is to equip students with the tools to apply statistical inference techniques on data. There are weekly problem sets that test you on the material covered in class. There are two midterms and a final exam. Each midterm takes place in between the respective halves of the semester.
Professor Mueller posts most of the slides before lecture and expects students to be somewhat familiar the concepts before coming to class, even though he goes over each concept in detail. However given that there is a lot of ground to cover, he expects students to understand the computational aspect of the course by themselves. Precepts are also meant for helping students with such problems. Practice problems and conceptual difficulties are covered in precepts.
II. Analysis of Learning Challenges
The primary challenge is the application of course material on real world examples. Students must not only understand statistical techniques but also understand the relevance of each technique i.e. know when to apply each technique.
There is not much focus on proofs, however they are essential in understanding the relevance of methods. Understanding the proofs makes the underlying rules much more clearer and makes it easier to know when to apply which rule.
However at the same time, the student need not spend too much time memorizing the proofs and knowing how to reproduce them for exams.
III. Suggestions for How to Engage the Course and Meet Its Challenges
Given that the course moves at great speed and the techniques that are learnt during the first half of the semester are applied extensively towards the later half, it is essential to move with the class. Falling behind early can make the course much more difficult than it all ready is and at the same time, do not gauge the difficulty of the course based on the first half, since it gets harder as the course progresses.
Most of the practice questions and problem sets focus on application of statistical methods and are usually relatively difficult questions combining several concepts together. If a student finds these questions difficult, then it is important to add more depth to ones ability i.e. do several basic questions on a concept and then gradually increase the difficulty level. This way one can comprehensively understand the concept and apply it in harder questions even when it may be combined with other questions. Since this is somewhat an introductory statistics course, the professor expects students to have this basic grasp of the material, which is why this technique can be really helpful.
IV. Description of What Students Can Expect to Take away from this Course
The course is great introduction to statistics and its application. After taking the class, students are better equipped to look at statistical surveys and analyze the validity of their conclusions. Students are taught the statistical definition of the difference between causality and association. At the same time, students by the end of course should be able to apply methods of statistical inference to data sets and gather useful information.