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Data Analysis (Statistics 3022)
Zack W. Almquist
Fall Semester, 2013
Class Schedule
Lecture: MWF 11:15–12:05 Phys 166 Lab 16410: Tu 12:20–1:10 FordH 110 Lab 16411: Tu 2:30–3:20 FordH 110 Lab 21412: Tu 11:15–12:05 FordH 110
Class Website
URL: http://moodle.umn.edu Note: Requires UMN login and registration in class to access.
Professor
Name: Zack W. Almquist Office: 372 Ford Hall Office Hours: F 10:00-11:00 AM Email: almquist@umn.edu Telephone: 612-624-
Teaching Assistant
Name: Yang Yang Office: 313 Ford Hall Office Hours: TU 1:10-2:10PM & 3:30-5:30PM Email: yang3175@umn.edu
Course Objectives
This course is an introduction to modern statistical methods and software. Here, we will focus first on classic statistical hypothesis testing (e.g., t-test), and then continue on to
various model based methods of analysis (e.g., the linear and generalized linear models). All mathematical and statistical concepts covered in this course will be paralleled with the necessary tools to perform the analysis in the R statistical programming environment.
Prerequisites
STAT 3011, STAT 3021 or equivalent; students are assumed to have an elementary under- standing of the basic concepts of probability and statistics. English language proficiency appropriate to an upper-division university class is assumed.
Course Requirements
Computers
It is not required that students bring their laptops to lecture and lab (if one is owned), but it is highly recommended since both lecture and lab will make extensive use of the computer software R. Computer labs are available on campus, please consult with the TA if you have trouble finding the various locations that computer labs reside on campus.
Readings
Weekly readings are assigned on the course syllabus. All readings are assumed to be completed before each lecture.
Homeworks
There will be weekly homework assignments. These homework assignments will be a combi- nation of problem solving exercises, computer based exercises and comprehension exercises. The purpose of the homeworks are two fold: first to review the concepts covered in class and second to test mastery of these of concepts.
Unless announced otherwise, homeworks will be made available on Wednesday and due the next Wednesday at 5:00 through Moodle. Homeworks will be graded on combination of completeness (i.e., problems attempted) and correctness (i.e., problems completed cor- rectly).
ONLY the version of homework reports turned in through Moodle will be accepted. No late homework will be accepted. The lowest homework grade will be dropped for the final grade calculation.
Lectures, readings, labs, and review sessions are provided for each student’s benefit. It is the responsibility of the student to take advantage of these opportunities to acquire and demonstrate mastery of course material, so as to achieve his or her desired grade.
Letter grade assignment
A 93%+ A- 90-92.99% B+ 87-89.99% B 83-86.99% B- 80-82.99% C+ 77-79.99% C 73-76.99% C- 70-72.99% D 60-69.99% F <59.99%
Required Texts
Fred Ramsey and Dan Schafer (2013), The Statistical Sleuth (3rd Edition). ISBN-10: 1-133-49067-0 — ISBN-13: 978-1-133-49067-8.
Required Software
We will be using the R statistical programming language. R can be downloaded at http: //www.r-project.org/.
Recommended Software
RStudio IDE (Integrated Development Environment) is a software application which fa- cilitates interaction with the R statistical programming language. It is often preferred to the GUI (Graphic User Interface) made available through CRAN. You can download it at http://www.rstudio.com/.
Course Policies
Missed Exam
Exams can be made up for legitimate (documented) absences, such as varied illness with a letter assigned by a physician, jury duty, military service, and religious observances. If you
must miss the exam for legitimate reasons, you have to CONTACT THE INSTRUCTOR AT LEAST ONE WEEK BEFORE THE TIME OF THE EXAM. In that case, makeup exams may be arranged to be taken any time before the exam is returned to the class. If you have a legitimate reason, but fail to take a makeup exam, an incomplete may be granted. If you miss any exam without legitimate reason, you will receive a zero for that exam. Note that social/vacation plans are not legitimate reasons for missing an exam.
Incompletes
An incomplete will only be given if: The student has a documented case of extreme hard- ship. The student has, up until the point of the request, been completing coursework and exams. The student’s average at the point of the request is a 70% or above. If these conditions are met, the student must request the incomplete from the instructor and it is still within the instructor’s rights to refuse the request. The student who is granted an incomplete must take the initiative to finish the course or the grade will revert to an F.
Academic Integrity
From the OSCAI Website: Student Academic Integrity and Scholastic Dishonesty Aca- demic integrity is essential to a positive teaching and learning environment. All students enrolled in University courses are expected to complete coursework responsibilities with fairness and honesty. Failure to do so by seeking unfair advantage over others or misrepre- senting someone else?s work as your own, can result in disciplinary action. The University Student Conduct Code defines scholastic dishonesty as follows:
Scholastic Dishonesty: Scholastic dishonesty means plagiarizing; cheating on assignments or examinations; engaging in unauthorized collaboration on aca- demic work; taking, acquiring, or using test materials without faculty permis- sion; submitting false or incomplete records of academic achievement; acting alone or in cooperation with another to falsify records or to obtain dishonestly grades, honors, awards, or professional endorsement; altering forging , or mis- using a University academic record; or fabricating or falsifying data, research procedures, or data analysis.
Within this course, a student responsible for scholastic dishonesty can be assigned a penalty up to and including an “F” or “N” for the course. If you have any questions regarding the expectations for a specific assignment or exam, ask.
In addition, I will file a claim with OSCAI if I have evidence of cheating. I understand that people often end up in difficult situations beyond their control, but these situations are no excuse for scholastic dishonesty. If you find yourself in a difficult situation, please come talk to me about options for the course. I will keep all conversations confidential.
Course Outline: Readings and Exams
Week 1 : Univariate statistics and hypothesis tests: Z-test, t-test and CI
- Reading: 1-2.2. Week 2 : Two-sample t-test, p-value and Type 1 and Type 2 error
- Reading: 2.3-3.4 and 3.5.1-3.5. Week 3 : Comparisons among several samples (ANOVA)
- Reading: 5.1-5.3, 5.5, 5.6.1; 6.1-6. Week 4 : Comparisons among several samples continued
- Reading: 6.3-6.5.2; 6.
- Midterm # Week 5 : Linear regression
- Reading: 7.1-7.4.3, 7.5-7.6; 8.1-8.5.2, 8.6.1, 8.6.2, 8.6.4; 9.1-9.2, 9. Week 6 : Linear regression: Inference
- Reading: 9.3-9.4; 10.1-10.3, 10.4.1, 10. Week 7 : Linear regression: Model checking and variable selection
- Reading: 11.1-11. Week 8 : Linear regression: Model checking and variable selection continued
- Reading: 12.1-12. Week 9 : Analysis of variance for two-way classification
- Reading: 13.1-13.4.3, 13.5. Week 10 : Introduction to time-series: Autocorrelation
- Reading: 15.1-4, 15.
- Midterm # 2 Week 11 : Odds ratio
- Reading: 18.1-18. Week 12 : Introduction to count data
- Reading: 18.4; 19.1-19. Week 13 : Logistic regression
- Reading: 19.6; 20.1-20. Week 14 : Logistic regression continued
- Reading: 20.4-20.6; 21.1-21. Week 15 : Review Week 16 : Final Exam
Calendar
Monday Tuesday Wednesday Friday Sep 2nd 1 3rd 2 LAB
4th 3 6th 4
9th 5 10th 6 LAB
11th 7 homework 1 due
13th 8
16th 9 17th 10 LAB lab 1 due
18th 11 homework 2 due
20th 12
23rd 13 24th 14 LAB lab 2 due
25th 15 homework 3 due
27th 16 Midterm # 1
30th 17 Oct 1st 18 LAB
2nd 19 4th 20
7th 21 8th 22 LAB lab 3 due
9th 23 homework 4 due
11th 24
14th 25 15th 26 LAB lab 4 due
16th 27 homework 5 due
18th 28
21st 29 22nd 30 LAB lab 5 due
23rd 31 homework 6 due
25th 32
28th 33 29th 34 LAB lab 6 due
30th 35 homework 7 due
Nov 1st 36
4th 37 5th 38 LAB lab 7 due
6th 39 homework 8 due
8th 40 Midterm # 2
11th 41 12th 42 LAB
13th 43 15th 44
18th 45 19th 46 LAB lab 8 due
20th 47 homework 9 due
22nd 48
