Course Information
Statistics 355 -- Spring 2021
http://www.stat.purdue.edu/~tlzhang/stat355/stat355.html
Course Description
An introduction to statistical methodologies for Monte Carlo simulations and real data analysis for undergraduate students. The topics will include matrix computations, random number generators, sampling distributions, and summary statistics. Fundamental statistical concepts, such as confidence intervals and hypothesis testing problems, will be included. Important statistical methods, such as linear regression, classification, and clustering, will be introduced. Essential use of the R programming language is made throughout. Intended exclusively for students majoring in Data Science.
Prerequisite:
- MA161, MA 265, and STAT 301/350.
COVID-19 Issues:
- Due to the risk of COVID-19, everybody must wear a face mask in the lecture.
- All homework assignments and exams must be submitted electronically.
- Please try to upload your files via Purdue Brightspace.
- Solutions must be typed in.
- No hard copies will be distributed or collected.
Schedule
- Class: TTh 9:00-10:15am, STWE 302 (Stewart Center 302).
- Midterm: Take-home; Time: TBA.
- Final: Take-home; Time: TBA.
Instructor
- Tonglin Zhang, Associate professor of Statistics
- Email: tlzhang@purdue.edu
- Office: 172 HAAS
- Phone: (765)496-2097, Fax: (765)494-0558
- Office Hour (1:00-3:00pm on Wednesday, web-based): no in-person office hours. The office hours will be all web-based. The time will be 1:00-3:00pm on Wednesday. Please send me a link such that we can meet on the web. If you are not available during this period, you can also make an appointmeht for other time slot, but this needs to be approved. In addition, students can send emails for questions at any time. I will reply the emails as soon as possible.
TA/Grader
- Liu, Chuanghui: liu2306@purdue.edu.
- Send an email to the grader and cc to me if you have any grading questions about the homework.
Textbooks
- No textbook is required. I am going to post lecture notes and R code via the course page.
- There are a lot of webpages available, such as this.
Topics to be covered
- Basic R operations: numbers, vectors, and matrices.
- Random number generators: uniform, normal, and etc.
- PDF, CDF, quantiles, and random numbers.
- Monte Carlo simulations for estimation problems.
- Confidence interval and hypothesis testing problems.
- ANOVA models.
- Linear regression problems.
- Classification.
- Clustering.
Web Page
All course information will be available on www.stat.purdue.edu/~tlzhang/stat355/stat355.htm.
Homework
Questions and solutions of homework assigments will be posted.
Exams
Take-home: open-book and open notes (due to COVID-19 issues).
Makeup: must be pre-approved at least one week before the official exam time. Students cannot decide it by themselves. The instructor can deny a makeup exam if it is not pre-approved. At least 20% deduction if a student does not follow the rule.
Emergency events will be considered.
Course Requirements and Grades
The final grade has the following
components:
- Homework (30%) will be posted on the course web page mostly. No late due is accepted and solutions will be available soon after due. The lowest score will be dropped.
- Midterm (30%). Time: 24-hours.
- Final (35%). Time: 24-hours, in or before the Final Week.
- Course Evaluation (5%): please upload your receipt to the BrightSpace.
Final Grade will consider the rank in the class. Initially, it is
A(+/-): 90-100; B(+/-): 80-90; C(+/-): 70-80; D (+/-): 60-70; F: below 60.
The criterion could be lower but not higher.