University of Wisconsin-Madison
Statistics 371: Introductory Applied Statistics for the Life Sciences

Check www.stat.wisc.edu/~jgillett/371 for updates to this tentative syllabus.

Goals

A student completing Statistics 371 can:

  1. Articulate the basics of probability and statistics.
  2. Make numeric and graphical summaries of simple data.
  3. Produce appropriate statistical analyses of simple data sets.
  4. Design simple experiments whose data will suit basic statistical analysis.
  5. Use RStudio, a free statistical software package, for statistical computations and graphs.
  6. Study and learn additional statistical methods.

Teachers
NameOffice HoursPhoneEmail (please ask most questions in person)
Gillett, John (Lecturer)Medical Sciences Center 1590   890-3216   jgillett@wisc.edu
Liu, Hongzhi (Support TA)Medical Sciences Center 1275A hliu438@wisc.edu
Park, Chan (Support TA)Medical Sciences Center 1205C chan.park@wisc.edu
Pritchard, Nathaniel (Support TA)Medical Sciences Center 1475 npritchard@wisc.edu
Trane, Ralph (Discussion TA)Medical Sciences Center 1219 rtrane@wisc.edu
White, David (Discussion TA)Medical Sciences Center B315 dmwhite5@wisc.edu

Class Times
Lecture 371-002TuTh 1:00-2:15Soils 270Gillett, John
  Discussion 321Tu 3:30-4:20Grainger 1185Trane, Ralph (Discussion TA) & Park, Chan (Support TA)
  Discussion 322Tu 4:35-5:25Social Sciences 5231Trane, Ralph (Discussion TA) & Park, Chan (Support TA)
  Discussion 323We 9:55-10:45Sterling 1313Trane, Ralph (Discussion TA) & Liu, Hongzhi (Support TA)
Lecture 371-003TuTh 2:30-3:45Van Vleck B130Gillett, John
  Discussion 331Tu 4:35-5:25Psychology 121White, David (Discussion TA) & Pritchard, Nathaniel (Support TA)
  Discussion 332We 9:55-10:45Psychology 103White, David (Discussion TA) & Pritchard, Nathaniel (Support TA)
  Discussion 333We 11:00-11:50Ingraham 22White, David (Discussion TA) & Pritchard, Nathaniel (Support TA)

Prerequisite
Math 112 (Algebra) and 113 (Trigonometry) or Math 114 (Algebra and Trigonometry).

Textbook
No textbook is required. I'll provide course notes (revising them as we go along). A recommended text, for those who want one, is "An Introduction to Statistical Methods and Data Analysis (Sixth Edition)" by R. Lyman Ott and Michael Longnecker (amazon).

Computing
A calculator is required for exams and homework: you must be able to find the mean and standard deviation of a one-variable data set and the correlation and slope and intercept of the regression line for a two-variable data set. (Here is an example of a suitable $15 calculator.) A computer is required for homework. We will use R, a statistical programming language, via RStudio, a free integrated development environment. We won't study R as such, but will use it by copying and modifying example R code. You may bring a laptop to discussion for help with R.

Help
The TAs and I are eager to help in class and office hours. Free drop-in tutoring is available each week day in Medical Sciences Center 1274: see www.stat.wisc.edu/courses/Tutorial_Schedule.

Grades

This 3-credit face-to-face course meets twice for 75 minutes each week (plus one 50-minute discussion) and carries the expectation that students will work on the course for about 3 hours out of class for each 75-minute class.

Grades are at https://canvas.wisc.edu/courses/139591. These points are available:
Exam 1100
Exam 2100
Final exam150
Homework48
Ask a question or make a comment in class2


Total400

At the end of the semester, we'll grade on a curve by ranking students according to course percentage and then assigning grades according to the percentile scale, A = 70, AB = 50, B = 30, BC = 20, C = 10, D = 5, F = 0. (That is, earning an A requires performing better than 70% of the class (and is unrelated to earning 70% of the points); we'll assign 30% A grades, 20% AB, 20% B, 10% BC, 10% C, 5% D, and 5% F; and the average course GPA will be 3.00, or a B. Here is a graph of this curve.)

If you anticipate religious or other conflicts with course requirements, or if you require accommodation due to disability, you must notify me during the first three weeks of class. You may not make up missed course work except in the rare case of a documented, serious problem beyond your control.

I encourage you to discuss the course with others, but you must write your exam and homework solutions yourself and prevent others from copying your work. (See the UW Academic Integrity policy.)

The registrar says the add deadline for our session (Regular) is 2/1/19 and the drop deadline is 3/29/19.

Tentative Schedule

Week: Dates Subject (number of lectures) (optional textbook sections) Homework Due 4:00 Friday
1: 1/22,24 RStudio and sample HW1 solution
1 Introduction (1.5) (1.1-1.3)
Discussion 1: RStudio and preview of Descriptive Statistics (bring laptop)
2 Descriptive Statistics (2.R) (1.5) (3.3-3.5)
give student survey
tip: Tutorial Schedule (link above)
read email
install R and then RStudio

HW1 1/25: RStudio
2: 1/29,31 (2 Descriptive Statistics, continued)
Discuss student survey (you may ignore its R code)
Discussion 2: descriptive statistics
3 Probability (1) (4.1-4.4)
emails: cold days
HW2 2/1 (extended to 2/8): descriptive statistics
3: 2/5,7 4 Random Variables and Distributions (3) (4.6-4.10) (normal table)
Discussion 3: probability, E() & VAR()
HW3 2/8: probability, E() & VAR()
4: 2/12,14 (4 Random Variables and Distributions, continued)
Discussion 4: binomial, normal
5 Estimation and a Known-σ Confidence Interval (2) (4.12, 5.2)
HW4 2/15: binomial, normal
5: 2/19,21 (5 Estimation and a Known-σ Confidence Interval, continued)
Discussion 5: QQ plot, CLT
demo CI simulator
6 Hypothesis Testing: Definitions and a Known-σ Test (1) (5.4-5.6)
HW5 2/22: QQ, CLT
6: 2/26,28 Q&A review for Exam 1 (1) (HW solutions are at Canvas)
Discussion 6: known σ (Z) CI (μ) & n, review
Exam 1: Thursday, February 28 (1) (formulas, rules, summer 2017 sample exam/key, fall 2017/key, spring 2018/key, summer 2018/key, fall 2018/key, spring 2019/key, histogram, regrade policy, Exam1 midterm grades)
HW6 3/1 (hint: do before Exam 1): known σ (Z) CI (μ) & n
7: 3/5,7 7 More One-Sample Confidence Intervals and Tests (5) (5.4-5.8, 10.2) (t table)
Discussion 7: testing, known-σ (Z) test (μ), unknown σ t CI & test (μ)
HW7 3/8: testing, known-σ (Z) test (μ), unknown σ (t) CI & test
8: 3/12,14 (7 More One-Sample CIs and Tests, continued)
Discussion 8: CI vs. test, power & n
HW8 3/15: (μ), CI vs. test, power & n
[3/19,21] [Spring Break]
9: 3/26,28 (7 More One-Sample CIs and Tests, continued)
Discussion 9: bootstrap CI & test (μ), sign test (M), proportion CI & test (π)
8 Comparing Two Populations via Independent Samples (3) (6.2-6.3, 10.3)
HW9 3/29: bootstrap CI & test (μ), sign test (M), proportion CI & test (π)
10: 4/2,4 Q&A review for Exam 2 (1)
Discussion 10: 2-sample t, review
Exam 2: Thursday, April 4 (1) (formulas, rules, summer 2017 sample exam/key, fall 2017/key, spring 2018/key, summer 2018/key, fall 2018/key, key, histogram, Exam 2 midterm grades)
HW10 4/5 (hint: do before Exam 2): 2-sample t
11: 4/9,11 (8 Comparing Two Populations via Independent Samples, continued)
Discussion 11: Welch's t, (2-bootstrap,) Wilcoxon
HW11 4/12: Welch's t, 2-bootstrap, Wilcoxon
12: 4/16,18 9 Comparing Two Populations via a Paired Sample (0.5) (6.4, 6.5)
Discussion 12: 2 proportions, paired data
10 ANOVA (2) (8.2-8.6, 9.3-9.5, 15.2) (chi-squared and F tables, Studentized Range q table)
HW12 4/19: 2 proportions, paired data
13: 4/23,25 (10 ANOVA, continued)
11 Correlation and Regression (1.5) (11.1-11.5, 11.7)
Discussion 13: ANOVA and multiple pairwise comparisons
HW13 4/26: ANOVA
14: 4/30,5/2 12 Goodness-of-fit and Independence Tests (1) (10.3-10.7)
Discussion 14: regression, χ2 goodness & independence
Q&A review for Final Exam (1)
HW14 5/3: ANOVA, regression, χ2 goodness & independence
Final Exam: Sunday 5/5/2019, 7:45am-9:45am (formulas)

Students in lecture 371-002 TuTh 1:00-2:15 should go to Chamberlin 2103
Students in lecture 371-003 TuTh 2:30-3:45 should go to Sterling 1310