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Syllabus: MS 5217 Statistical Data Analysis

Semester A 2021/22

Course Overview:

This course covers fundamental statistical concepts and necessary computational tools in data analysis. The goal is to learn how to perform descriptive, analytical, and predictive data analysis based on the real-world problems. This course also serves as a quantitative foundation for elective courses in marketing, finance, economics, and more advanced data science courses.

Prerequisites and Workloads:

There is no prerequisite for this course. I welcome students from all backgrounds. However, a student is expected to spend at least three hours on studies after each class.

Evaluation and Policy:

Homework assignments: 40%; Exam: 40%; Individual Project: 20%.

● Two homework assignments are due one week after posting. These assignments can be done in groups (max size 3). You should submit only one write-up per group. Homework should be submitted to Canvas. There is a 20%*K penalty for any late submission after K days.

● I am always generous with bonus points for quality questions or answers (1 point corresponds to 1% in your total grade). The maximum bonus points collected are allowed to be 10% in the overall grade. Students should write to TAs about bonus points right after the class.

● You are suggested to post questions on Canvas Discussions. Also, students who provide first quality solutions to help others will be rewarded with bonus points.

● We will have 12 lectures and a comprehensive final exam. According to the university guidelines, the exam has to be taken in a face-to-face mode. Following the university guidelines, I also plan to offer a different version of the exam to online exam takers. The exam date is Oct. 11th, 2021.

– For students who take the on-campus exam, you are allowed to bring ONE A4 ONE-SIDED cheatsheet with hand-written notes. The cheatsheet needs to be submitted with your exam solution. Please leave your name and email on the other side of the cheatsheet.

● The individual project will be posted right after the exam and due in one week. You have a chance to apply your statistical concecpt to a real-life problem and prepare for a statistical report.

Optional Reference:

There are no required textbooks. All lecture notes, assignment instructions, data, and other materials are available on Canvas. Below are two recommended optional references.

● Statistics for Business: Decision Making and Analysis, by Robert Stine and Dean Foster

● OpenIntro Statistics, by David Diez, Mine Cetinkaya-Rundel, Christopher Barr, and OpenIntro (This book is free in PDF format at https://www.openintro.org/stat/textbook.php.)

● Naked Statistics, by Charles Wheelan (I highly recommend this book. There is also a Chinese version.)

● AIQ: How People and Machines Are Smarter Together, by Nick Polson and James Scott

R for Computing:

We will use R in the class and you have to learn some R programming. R is the most convenient statistical language for computing. Also, R is free and available on both Windows and Mac. I expect most students have little programming experience and will offer an introductory lecture to R. Both TAs would also love to help you debugging codes during their office hours. Please challenge yourself and start programming in my class. There is a great chance you would see some R codes and outputs in the exam.

Tentative Class Topics:

● Basic R programming

● Exploratory data analysis

● Basic probability

● Bayes’ Theorem

● Discrete Probability Distribution

● Continuous Probability Distribution

● Sampling distribution

● Central Limit Theorem

● Statistical estimation

● Confidence intervals

● Hypothesis testing

● Basic linear regression