OPIM 5603 (Statistics in Business Analytics)
Instructor: Prof. Do
Office: MONT 130
Office hours: by appointment
Email: cuong.do@engineer.uconn.edu, cuong.do@math.uconn.edu
Office phone: 860.486.7132
Description
This course offers an advanced level exploration of statistical techniques for data analysis. We shall study the concepts of population and sample; discuss the difference between population parameters and sample statistics, and how to draw an inference from known sample statistics to usually unknown population parameters. Topics will focus on rigorous statistical estimation and testing. The course will also prepare students with the skills needed to work with data using analytics software. In particular, the course will introduce and use the R software platform to illustrate the material. R is a very powerful open source statistical computing and programming language that is being increasingly adopted by organizations. The main objectives of the course are the following: (a) introduction to R to provide students familiarity and confidence with the environment to conduct core statistical analysis, (b) to design and conduct basic statistical analysis from first principles, and (c) explore some advanced topics through add-on packages. By the end of the course, each student will have demonstrated an ability to use R in characterizing and analyzing a dataset of their choosing. No prior programming experience is assumed.
Software
Programming languages: R (together with R Studio)
Schedule
The tentative content is as follows, dates are TBA.
1. Class overview, statistics introduction, R basics
2. More on R, descriptive measures, visualizing data
3. Probability and probability distributions
4. Sampling distributions, confidence intervals, hypothesis testing
5. First Exam
6. Maximum likelihood estimation
7. Spring recess
8. Linear regression
9. Generalized linear models
10. Non-parametric statistics
11. Other topics
12. Second Exam
13. Project Presentation
Textbook
There is not a required textbook The following textbooks and notes are not required, but they can be helpful as background or follow-up reading:
1. Paradis, E. “R for Beginners”. Available at ftp://cran.r-project.org4mb/R/doc/ contrib/Paradis-rdebuts en.pdf
2. Zhao, Y. “R and Data Mining: Examples and Case Studies.”
Available at ftp://cran.r-project.org4mb/R/doc/ contrib/ Zhao R and data mining.pdf
3. Crawley, M “Statistics: An Introduction Using R”, second edition, Wiley, 2015. ISBN 978-1-118-94109-6
4. Dalgaard, P. “Introductory Statistics with R”, second edition, Springer, 2008. ISBN 978-0-387-79053-4
Exams, Assignments, and Project
| Exam 1 | 20% |
| Exam 2 | 20% |
| Assignments | 30% |
| Course Project | 30% |
| TOTAL | 100% |
There are 2 exams in class. Assignments are given weekly. Students work in team for the assignments and course project.
Teaching Materials
Teaching material (slides, instruction, code, and data) will be posted on HuskyCT at http://huskyct.uconn.edu.
Grading
TBA