Stat 411/511


Fall 2015

Course Name: Methods of Data Analysis I
Course Number: ST411/511
Course Credits: This course combines approximately 120 hours of instruction, lab activities, and assignments for 4 credits.
Instructor: Charlotte Wickham

Course content

Stat 411/511 covers statistical tools for dealing with one-, two-, and k-sample comparisons of quantitative responses, and the modeling of a quantitative response as a function of a quantitative explanatory variable (simple linear regression).

Learning Objectives

After taking this course, you should be able to:

  • determine the appropriate statistical tool to use in data analyses involving one to several samples and simple linear regression
  • implement such analyses in R
  • summarize statistical findings in a precise yet nontechnical manner, and
  • (ST 511 students only) prepare text summarizing methods and results that would be appropriate for submission to a scientific journal.


From the catalog: ST 202 or ST 351 (this means an Introductory Statistics course).

To be prepared for ST 511 you should:

  • have already seen t- and Z- tests and confidence intervals
  • understand the terms random sample, probability distribution, normal curve, hypothesis test, and p-value.
  • be able to read t-tables and Z-tables.


We will be closely following chapters 1–8 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.

Readings will be assigned from the textbook. A couple of copies are also available on reserve in the Library.

Web site

Course materials (lecture notes, homeworks and labs) will be posted at Class email announcements and grades will be distributed through canvas. You need to have an ONID account in order to use canvas.

Labs and computing

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.


  • Homework20%
  • Data Analyses30%
  • Quizzes20%
  • Participation10%
  • Final20%

Each week, either a homework or a data analysis will be due on Friday. Homeworks are shorter, more directed and submitted as R notebooks. They will be the same for ST411 & ST511. Data analyses are longer, undirected and submitted as formal written reports. They will be different for ST411 & ST511

Both homeworks and data analyses are individual, you may discuss the assignments but your submission must be entirely your own work.

Quizzes will be every two weeks in canvas. 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 is scheduled for Thursday Dec 10th at 6pm. It will be a mixture of quiz type questions, and interpretation of analyses.

Participation grades will be based on forming and participating in a study group. Instructions will be provided in Week 4.

Final percentages will be converted to letter grades according to the following scheme:

Percent Grade
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

(Rough) Topic outline

  • Week 1 Review
  • Week 2 One sample and two sample t-tests
  • Week 3 Statistical inference
  • Week 4 Assumptions of the t-tools
  • Week 5 Alternatives to t-tools
  • Week 6 Comparing several samples (ANOVA)
  • Week 7 Linear combinations of means and multiple comparisons
  • Week 8 Linear regression
  • Week 9 Assumptions of linear regression
  • Week 10 Wrap up

Classroom culture

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 integrity

Academic dishonesty is a serious offense and will be addressed following the guidelines set out in the Academic Regulations of OSU (go to AR15 at

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:

  • verbatim copying of another student’s homework assignment
  • copying off another student’s exam
  • using prohibited materials (e.g., cell phone, cheat sheet) during an exam
  • communicating with another student during an exam
  • changing answers on an exam after the exam has been graded
  • unattributed use of material copied from an article, textbook, or web site
  • continuing to write on an exam after the instructor or TA has asked for the exams to be handed in.

Disability statement

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.