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:
Teachers
Name | Office Hours | Phone | Email (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 371-002 | TuTh 1:00-2:15 | Soils 270 | Gillett, John |
Discussion 321 | Tu 3:30-4:20 | Grainger 1185 | Trane, Ralph (Discussion TA) & Park, Chan (Support TA) |
Discussion 322 | Tu 4:35-5:25 | Social Sciences 5231 | Trane, Ralph (Discussion TA) & Park, Chan (Support TA) |
Discussion 323 | We 9:55-10:45 | Sterling 1313 | Trane, Ralph (Discussion TA) & Liu, Hongzhi (Support TA) |
Lecture 371-003 | TuTh 2:30-3:45 | Van Vleck B130 | Gillett, John |
Discussion 331 | Tu 4:35-5:25 | Psychology 121 | White, David (Discussion TA) & Pritchard, Nathaniel (Support TA) |
Discussion 332 | We 9:55-10:45 | Psychology 103 | White, David (Discussion TA) & Pritchard, Nathaniel (Support TA) |
Discussion 333 | We 11:00-11:50 | Ingraham 22 | White, 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 1 | 100 |
Exam 2 | 100 |
Final exam | 150 |
Homework | 48 |
Ask a question or make a comment in class | 2 |
Total | 400 |
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, χ^{2} goodness & independence Q&A review for Final Exam (1) |
HW14 5/3: ANOVA, regression, χ^{2} 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 |