Applied Statistics

Term: 
Fall
Credits: 
2.0
Academic Year: 
Status: 
Core
Elective
Course Description: 

Statistics is an integral part of political science research. We live in a world where there is no shortage of numerical data and there is increasing demand for people who know how to make sense of it independent of the eld of work. The goal of this course is to turn you into one of these people. In a prior class you have learned the basics of statistical inference and the most commonly used statistical techniques found in political science research. The course is designed to give you the hands on knowledge you need to actually do the statistics you learned in practice. We will cover all the techniques from the rst 6 weeks of course and augment it with a solid overview of applied linear regression (and, if time allows, some additional techniques). To do all this, we will use the open source R software. R is a serious pain to use but climbing the early learning curve pays o quickly as you become more competent analysts. This will not be an easy ride, but I hope to make it fun along the way as you become competent statistical analysts. Bring your laptops to class. Install R (https://www.r-project.org/) and R-Studio (https://www.rstudio.com/).

The main goal of the course is to enable students to apply all the methods covered in the first 6 weeks to any given dataset. Thus, first four classes are dedicated to familiarizing students with statistical software R. This includes working with scripts, dierent types of data objects, and logical operators, as well as learning crucial data management techniques. The following two classes are dedicated to conducting simple statistical analysis: cross{tabulations, dierence tests, correlation, etc. Starting with 10th week, applied linear regression techniques are discussed. This is central and the most extensive part of the course. The last class is reserved for a bit more advanced linear regression topics. There will be 4 assignments. They will be provided a week and a half before they are due.

Learning Outcomes: 

The goal of the course is to provide students with the most basic tools to conduct quantitative political science literature. At the conclusion of the course students should be able to apply all the methods covered in the rst 6 weeks classes in a hands on way to any dataset using R. Additionally, we cover applied linear multiple regression, its assumptions and learn how to test these with R.

Assessment: 

The class will be assessed through assignments and a final paper. All assignments will be weighed equally. Additionally, extra credit assignments may be given if there is a need. Attendance (with timely arrival) is REQUIRED. Late arrivals are counted as unexcused absences. Two unexcused absences will lead to an automatic failure of the class. If you will miss a class or come late for any reason, make sure I know about it before the class. (Even if it is a few minutes before class.)

Grading
Assignments (4): 15% 4 = 60%
Research Paper: 40%
On a 100 point scale, the grading would be as follows:
A   94.00 - 100
A- 87.00  - 93.99
B+ 80.00  -  86.99
B 73.00  - 79.99
B- 66.00  - 72.99
C+ 59.00  - 65.99
F 0  - 58.99

Important note:

Complete academic honesty is expected of everyone. Failure to comply with this requirement will result in automatic failure in this course (and subsequently in the program) and additional disciplinary action on higher levels. This is an American university and American standards will be applied. For more information about these standards see: http://en.wikipedia.org/wiki/ Academic dishonesty (READ VERY CAREFULLY!)
All assignments are to be done individually. You can talk about how to do it, but none of the actual work can be done in a group. Any evidence to the contrary will be investigated.

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