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 Email
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.