NOTE: This is the course webpage for the Spring 2018 offering of STAT 371, Lecture 3.
Important dates
- Tuesday 1/23/2018: first lecture
- Thursday 3/1/2018: Midterm 1 in lecture
- Thursday 4/12/2018: Midterm 2 in lecture
- Thursday 5/3/2018: last lecture
- Wednesday 5/9/2018, 5:05-7:05pm: Final Exam (Location TBA)
- Other important dates (e.g. drop deadline )
Topics covered
This course will provide students in the life sciences with an introduction to modern statistical analysis. Topics include descriptive statistics, probability and random variables, distributions, estimation, one-sample testing and confidence intervals, two-sample inference and analysis of variance. It also introduces and employs the freely-available statistical software R to explore and analyze data.
For more detailed information, see the course Syllabus.
Who / what / where / when
Lectures: Tuesday and Thursday, 1-2:15pm, Animal Science 212
Discussions:
Section | Place | Time |
---|---|---|
331 | Van Hise 494 | W 11-11:50 am |
332 | Education Science 212 | W 1:20-2:10 pm |
333 | Mechanical Engineering 1163 | R 8:50-9:40 am |
Discussions are optional and start in the first week of classes. Discussion locations might change through the semester, see your TA’s announcement. Discussion handout will be posted at LearnUW before each discussion by the Discussion TA.
Teaching staff:
Name | Office Hour | Office Location | |
---|---|---|---|
Duzhe Wang | Wednesday 3-4pm | R1475 MSC | dwang282@wisc.edu |
Hao Chen | Tuesday 9:50-10:50am | 1335 MSC | hchen434@wisc.edu |
Muhong Gao | Wednesday 3-4pm | 1275 MSC | mgao55@wisc.edu |
Yuetian Luo | Thursday 10-11am | B315 MSC | yluo86@wisc.edu |
Ning Fan | Monday 12:15-1:15pm | 1275 MSC | nfan@wisc.edu |
Prerequisites
Math 112 (Algebra) and 113 (Trigonometry) or Math 114 (Algebra and Trigonometry).
Textbook
There is no required textbook for the class. The recommend text is An Introduction to Statistical Methods by R.Lyman Ott and Michael Longnecker.
Programming
We will use the freely-available statistical software R and RStudio (See the Getting started with R from Professor Karl Broman and RStudio tutorial from John Gillett.)
Grading
The grading scheme for the course is as follows:
Component | Points |
---|---|
Homework | 120 |
Midterm 1 | 80 |
Midterm 2 | 80 |
Final | 120 |
Homework
There will be 8 homework assignments and each homework has 15 points. Check homework submission policies from the syllabus.
Exams
There is no makeup exam. If you miss the exam, you must provide a justified and documented reason to the instructor.
Q&A
We will use Piazza for course-related questions.