University of Minnesota
Course Information for Stat 5302
Fall Semester, 2015, September x – December xx, 2015
Lectures: MWF 1:25-2:15 in 155 Nicholson Hall
Sections: Thursday: Lab 2: 11:15-12:05 213 Vincent Hall; Lab 3 12:20-1:10, 213 Vincent Hall,
Lab 4: 1:25-2:15 213 Vincent Hall; Lab 5: 2:30-3:20 121 Smith Hall
Instructor S. Weisberg, 313 Ford Hall, 625-8355, sandy@umn.edu. Office Hours: Tu 9-10, W
2:30-3:30, or by email or appointment.
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Text S. Weisberg (2014). Applied Linear Regression, fourth edition. New York: Wiley. The
website for the book is http://z.umn.edu/alr4ed. You can read the book in a web browser
for free, and possibly download it for a fixed period of time, from the University Library,
http://z.umn.edu/umnalr4, but you cannot print or copy the book. The book is available in
the University Bookstore. For other legal buying options, see http://z.umn.edu/wileyalr4.
Any electronic copy of the textbook from a non-US website, or obtained without payment, is
likely to be illegal.
Web page
The Moodle webpage for this class is http://z.umn.edu/5302moodle; you must be a registered
student to view this page. The webpage will include copies of all handouts and assignments, and
links to additional resources such as videos, and links to other web resources such as the website
for the textbook. At least at first, homework will be submitted via the Moodle page.
Computing
Since this is an applied statistics course, the computing requirements are fairly heavy. All computing
will be done using R and RStudio. See the website for the book for more information on R. If you
are new to R and are of University of Minnesota student, you can do an online tutorial. Go
to lynda.umn.edu, type R in the search box, and then select the course ”R Statistics Essential
Training”. Alternatively, you can start R or RStudio, and in the console window type
> library(alr4)
> alr4Web("primer")
for an on-line R Primer designed for use with the textbook.
Computers in the Classroom
You may bring your laptop or tablet to class. Please, however, refrain from using electronic
devices as a distraction; don’t plan your wedding in class, don’t send text messages, don’t buy
concert tickets or watch cute cat videos.
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Homework
There will be approximately weekly assignments. Odd-numbered problems in the textbook have
solutions on the book’s website, or by typing in R
>
alr4Web("solutions")
Even numbered and extra problems must be submitted for grading. Late homework is generally
not accepted. Each homework set counts for an equal number of points in the final grade.
Every homework assignment will require using R to do computing,and RStudio to format the
homework. Typical problems will require submitting your computer code, your computer output, and a written description of what you have done and conclusions you
have reached. Computer code without description earns no credit. All homework will be
submitted on Moodle.
Academic Honesty and Academic Dishonesty
The following definition of student academic integrity and scholastic dishonesty is slightly modified
from the webpage of the University’s Office for Student Conduct and Academic Integrity, http:
//www.oscai.umn.edu:
Scholastic dishonesty means plagiarizing; cheating on assignments or examinations; engaging
in unauthorized collaboration on academic work; taking, acquiring, or using test materials without
faculty permission; 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 misusing a University academic record; or fabricating
or falsifying data, research procedures, or data analysis.
All School of Statistics teaching faculty are instructed to refer students who violate
the policy for academic honesty and dishonesty to the Office of Student Conduct and
Academic Integrity. Within this course, a student responsible for scholastic dishonesty can in
addition 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.
Some additional points that are relevant specifically to Statistics 5302:
1. The purpose of homework is to help you learn the material covered in this class. Many
students find working on homework in groups to be helpful to their learning, and so this is
specifically allowed. Even if you study and do homework in groups, however, your
submitted solutions must include your own computer coding, computer output
that you produced, and descriptions/summaries of results in your own words.
2. Solutions for all Applied Linear Regression problems may have been illegally and
unethically posted on the internet, or may have been distributed to students taking this
course in previous years. Using these solutions for your homework is not permitted.
3. After assignments are due, solutions to even-numbered problems may be posted on Moodle.
You may use these to help you study for exams. Sharing these solutions either on the internet
or with future students at the University or elsewhere is not permitted.
4. Use of copyrighted intellectual property, such as a textbook, without the permission of the
publisher or payment of a royalty, is not permitted.
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Exams and Grading
There will be two in-class exams (25% of the grade each) and an in-class final exam (40% of the
grade). Homework will account of the remaining 10% of the grade. The exams generally require
writing several short answers.
The final exam is scheduled for 10:30 a.m.-12:30 p.m., Saturday, Dec. 13. If you cannot attend
the final at that time, do not enroll in this class. The first midterm exam will given after we finish
Chapter 5 in the book, on or about October 13. The second midterm exam will be given on or
about November 17.
Grading is on “the curve”, with a separate curve for graduates and undergraduates.
Students who fail to complete all work will receive a grade based on the work done. Grades of
incomplete will be given only in extraordinary circumstances, and only after negotiation with the
instructor.
Outline
The course will generally follow the textbook, completing Chapters 1–10 and 12.1–12.3. The Table
of Contents is given at http://z.umn.edu/wileyalr4.
Accomodation Statement
The University of Minnesota is committed to providing equitable access to learning opportunities
for all students. Disability Resource Center (DRC) is the campus office that collaborates with
students who have disabilities to provide and/or arrange reasonable accommodations.
If you have, or think you may have, a disability (e.g., mental health, attentional, learning,
chronic health, sensory, or physical), please contact DRC at 612-626-1333 to arrange a confidential
discussion regarding equitable access and reasonable accommodations. If you are registered with
DRC and have a current letter requesting reasonable accommodations, you are encouraged to
contact your instructor early in the semester to review how the accommodations will be applied in
the course.
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