Stat 412/512 is an introduction to multiple regression, including inference and model checking in multiple regression, two-way analysis of variance, adjustment for serial correlation, repeated measures and strategies for variable selection.
After taking this course, you should be able to: recognize data sets for which multiple regression or two-way analysis of variance is appropriate; be able to implement such analyses in R; know how to correct for serial correlation and other kinds of dependence; recognize designs that are based on repeated-measures or multivariate responses; summarize statistical findings in a precise yet nontechnical manner; and (ST 512 students only) prepare text summarizing methods and results that would be appropriate for submission to a scientific journal.
From the catalog: ST 411/511
We will be closely following chapters 9–16 in The Statistical Sleuth by F. Ramsey and D. Shafer, 3rd Ed. The 2nd Edition is also fine to use, but it is your responsibility to check the numbering of sections and problems match. If you are buying used, you do not need the accompanying CD.
Course materials (lecture notes, homeworks and labs) will be posted at stat512.cwick.co.nz. Class email announcements and grades will be distributed through canvas. You need to have an ONID account in order to use canvas.
The lab sessions are designed to give you time to interact with data in R and practice the tools covered in lecture. Lab sessions are in Milne Computer Center 201 and are directed by the teaching assistants. There will be a lab in the first week. To participate in the labs, you must have an ONID account. Know your user name and password when you come to the lab.
Each week (except the last), either a homework or a data analysis will be due on Wednesday at noon on canvas. Homeworks are shorter, more directed and submitted as compiled R notebooks. They will be the same for ST412 & ST512. Data analyses are longer, less directed and submitted as formal written reports. They will be different for ST412 & ST512.
Both homeworks and data analyses are individual, you may discuss the assignments but your submission must be entirely your own work.
Quizzes will be in canvas except for the first one. They will be open for a limited time and timed. They will consist of multiple choice and short answer questions on any material up to the day they are administered.
Quizzes are individual, you cannot discuss the content with anyone until the quiz has closed and been graded. You may use your book and notes, but you shouldn’t need to.
The final exam will cover all material. It will be a mixture of quiz type questions, and interpretation of analyses.
Participation grades will be based on a journal submitted by your study group. Groups should contain between 3 and 6 people. You should meet at least three times during the quarter. Your group should maintain a study journal that records the dates you met and topics you discussed. Each member of the group will submit this journal on Blackboard before the final exam.
A regression in your field assignment will be provided in week 4 and due (tentatively) in week 7. It will involve a short (less than one page) written report on a journal article you select.
Final percentages will be converted to letter grades according to the following scheme:
|95 – 100||A|
|88 – 94.9||A-|
|80 – 87.9||B+|
|75 – 79.9||B|
|70 – 74.9||B-|
|65 – 69.9||C+|
|60 – 64.9||C|
|55 – 59.9||C-|
|45 – 54.9||D|
|0 – 45||F|
If you would rather be sleeping, reading the newspaper, messaging your friends, facebooking, tweeting, shopping or gaming I suggest you do it somewhere more comfortable than in the classroom.
Academic dishonesty is a serious offense and will be addressed following the guidelines set out in the Academic Regulations of OSU (go to http://catalog.oregonstate.edu, click on Registration Information → Academic Regulations, and read AR 15).
The Student Conduct Code defines Academic dishonesty as
… an act of deception in which a Student seeks to claim credit for the work or effort of another person, or uses unauthorized materials or fabricated information in any academic work or research, either through the Student’s own efforts or the efforts of another.
Examples include, but are not limited to, the following:
Accommodations are collaborative efforts between students, faculty and Disability Access Services (DAS). Students with accommodations approved through DAS are responsible for contacting me prior to or during the first week of the term to discuss accommodations. Students who believe they are eligible for accommodations but who have not yet obtained approval through DAS should contact DAS immediately at (541) 737-4098.