Data Analysis I

This course is designed to give students the necessary skills to analyze data for their research projects. Together with USP 654 Data Analysis II in the fall, the sequence will provide a thorough and reasonably comprehensive introduction to understanding, critically evaluating, conducting, and writing about analyses for most studies in social science- related disciplines. This course covers descriptive statistics, probability and distributions, hypothesis testing, association, and simple regression analysis. USP 654 Data Analysis II will cover regression analysis in more depth.


Course info
When Tue 4:00 - 7:30pm
Where URBN 225
Office Hours Tue 2 - 3:45pm & by appointment at URBN 350D

Instructor
Instructor Liming Wang  

Texts
OpenIntro Statistics: 3rd ed. Diez, D.M., Barr, C.D. and Çetinkaya-Rundel, M. CreateSpace Independent Publishing Platform, 2015
R for Data Science Grolemund, Wickham O'Reilly, 1st edition, 2017

Additional readings will be posted to D2L/Readings

Software

This course will use the R statistical software. R is free and available for download at http://cran.r-project.org. We will use RStudio (https://www.rstudio.com/) as our main interface to R. R and RStudio is installed on the lab computers across the campus. Labs will be offered weekly to assist in using R to complete the assignments and R examples will be used during regular sessions. I can provide additional assistance with the software. DataCamp is a good resource to learn R along with data analysis skills.

It is possible to use your choice of other stats software, such as SAS, SPSS, or Stata, for this class; I do encourage you to continue using one of these software if you are already using it. It is also a good idea to ask your graduate adviser and/or GRA supervisor which software would be most useful for you, and use it. If you plan to use one of these software, I will provide as much assistance as I can.

Hardware

Class meets in CUPA computer lab URBN 225; you are welcome to bring and use your own laptop (Windows, Mac, or Linux). Check out the Tools and Datasets section for instructions of installing necessary software on your own computer.

Attributions