This electronic document is the official course syllabus. Any changes to this document will be announced in class. It was produced using R Markdown; the source file is available at http://stat.psu.edu/~dhunter/470/index.Rmd

Course Information

OFFICIAL UNIVERSITY BULLETIN COURSE DESCRIPTION

This is a capstone course intended primarily for undergraduate statistics majors in their last semester prior to graduation. The course is designed to reinforce problem solving and communication skills through development of writing ability, interaction with peers and the SCC, statistical consulting center (SCC), and oral presentations. Course objectives are tailored to the needs of each cohort and may include the application of statistical reasoning to real-world problems and case studies, recognition or recommendation of appropriate experimental designs, proficient use of ANOVA GLMs with understanding of associated modeling assumptions, ability to identify concerns about the use or interpretation of statistical models in context, and both written and verbal communication of statistical findings.

Class Times and Locations:

Section Day/Time Location
002 M/W 2:30-3:45 113 Chem and Biomed Eng

Course Pre-requisites:

  • Stat 461 Analysis of Variance
  • Stat 462 Applied Regression Analysis
  • Software experience with R (Recommended)
  • Semester Standing 7+

Teaching Team

Instructor

David Hunter
Office: 310 Thomas Building
Email: dhunter@stat.psu.edu

Teaching Assistant

Roopali Singh
Office: 301 Thomas Building
Email: rus82 [at] psu [dot] edu

Office Hours
Day When Where Who
Mondays 4pm - 5pm 310 Thomas Bldg Hunter
Thursdays 8am - 9am 310 Thomas Bldg Hunter
Wednesdays 10am - noon 301 Thomas Bldg Singh

Also by appointment (to schedule: send an email with 3-4 possible times)

Course Objectives

Upon successful completion of the course, students will:

  • Be able to apply statistical knowledge to real world problems
  • Be able to recognize experimental design
  • Be proficient in ANOVA, and GLMs including understanding the modeling assumptions
  • Be able to identify concerns about the use or interpretation of statistical models in context
  • Be able to communicate statistical findings through written and verbal communication

Required Materials:

  • Canvas for assignments: https://psu.instructure.com
  • Broadening Your Statistical Horizons: Generalized Linear Models and Multilevel Models (BYSH) by Julie Legler and Paul Roback. Freely available at: http://pages.stolaf.edu/bysh/
  • Assigned journal articles and selected readings (on Canvas)

Computing

R is recommended for the computing component of the course, and R Markdown is required for submission of all reports. Here are some resources that I recommend (not necessarily in order).

  • (FREE online) Introduction to R Markdown from RStudio
  • (FREE online): R for Data Science by Garrett Grolemund and Hadley Wickham
  • (FREE online): A Student’s Guide to R by Nicholas Horton, Randall Pruim, & Daniel Kaplan
  • (FREE online): Advanced R by Hadley Wickham
  • (buy online): R for Everyone: Advanced Analytics and Graphics by Jared P. Lander
  • (buy online): R in Action: Data analysis and graphics with R by Robert Kabacoff
  • (FREE companion website to R in Action): http://www.statmethods.net/

Grading (Modifed since the midterm was dropped)

  • 48% Projects (11% for first, 16% for second, and 21% for final)
  • 30% Assignments
  • 12% Special topic talks
    • 6% Presentation
    • 6% End-of-semester assessment
  • 0% Exam (midterm)
  • 10% Professionalism
    • 5% Attendance
    • 5% Effort & Participation

Notes about grading

  1. This is a writing intensive course; all written work should be professional, easy to read, well-organized, and parsimonious. Grammatical correctness, clarity of writing, and accuracy of content influence grading.
  2. On the job, late work will degrade your standing in the organization. In this class, credit for late work (excluding exams and special topics talks) will be given as follows:
    • 75% credit for the first 24 hours after the due time/date;
    • 50% credit between 24 and 48 hours after the due time/date;
    • Zero credit awarded after 48 hours beyond the due date/time
  3. In addition to the assignment grade, late work may also influence the Effort & Participation grade (below).

Final grades

Grade Score
A > 93%
A- 90%
B+ 87%
B 83%
B- 80%
C+ 77%
C 70%
D 60%
F < 60%

Projects

Final Design Project

Students work in small groups to carry out a designed experiment or observational study from beginning to end. Each group writes a proposal for a project of their own devising; plans and implements the collection of data; analyzes the data in an appropriate manner; and finally writes a formal report of their findings. Groups will have at least 6 weeks to complete the study. The project must require the use of analyses more advanced than those seen in STAT 200.

Case Studies (Projects)

Students will work in a small group to address the questions of a “client” on several projects throughout the semester. Each group will be responsible for analyzing the data, writing a report appropriate for a non-statistician, and presenting the results. These projects will emphasize explaining methods and results without relying on technical jargon. These projects are also designed to challenge the students statistically. Professional conduct of each student in the meetings and in the final report will be evaluated and graded.

Homework Assignments

There will be 6-9 homework assignments throughout the semester. Some assignments will be on content from BYSH (class text), but other assignments will be based on assigned readings posted on CANVAS, or the class projects.

Special Topic Talks

On the job, you will need to be able to teach yourself new things and explain them to others. For this assignment, each student will have a turn leading an engaging and interactive class discussion with a partner on an assigned special topic related to statistics or data science. Together with your partner, you are expected to

  • become a “class expert” on your topic;
  • lead an engaging and interactive discussion for 20-25 minutes, including
    • an overview of the most important details of the topic (how it works),
    • description of 2-3 applications when the topic would be useful (when/why to use it),
    • software tools (i.e. R functions & packages),
    • walking the class through an example using real data with a context and purpose,
    • recommending additional resources where students can learn more;
  • answer questions from your classmates, professor, and/or TA on the subject;
  • prepare a one-sheet handout to share with the class as a summary of the topic.

Topics are generally chosen in order to provide relevant background knowledge needed to tackle an upcoming case study/project.

Updated Jan. 26: This is the list of topics from which you may choose. You should sign up for one of these topics on Canvas:

  • Classification and regression trees
  • Data imputation
  • Empirical Bayes
  • Equivalence testing
  • Monte Carlo simulation
  • Neural networks
  • Permutation tests
  • Power analysis
  • Poisson regression with zero inflation
  • Principal components analysis
  • Response surface designs
  • Statistical process control
  • Survival analysis
  • Text mining

On the last day of class (the week of April 27) there will be an assessment based on the discussion topics. The questions will be related to what is on the hand-outs provided by the class. The assessment will be worth 5% of the final grade.

Professionalism

Professionalism includes attending class, arriving on time to class and meetings, being engaged while in class, and being respectful of the teacher and other students’ questions/comments and time. In presentations and meetings, each student is expected to be prepared, to actively participate, and to answer questions about all content presented even if it is not specifically something he/she contributed to the project.

Attendance

Canvas will be used to take attendance at the beginning of class. Since students will regularly complete assignments in teams, others are counting on you to be in class and engaged. Students with University excused absences (e.g., athletics trips) should notify the instructor as soon as possible. Absences will otherwise affect the professionalism grade. An unexcused absence on the day of a presentation or group meeting will additionally influence the project grade.

Effort & Participation (E & P)

Effort & participation (E & P) will be scored at the discretion of the professor and graduate teaching assistant. Students will begin with a 75% score on the first day of class, which will then go up or down as the course progresses based on demonstrated actions. E & P is intended to help you acclimate to the dynamic that will dominate your professional/performance evaluations in a future career. Raises, promotions, and terminations can happen at the discretion of management. If you just meet the minimum expectations, you might keep your job but you likely won’t get much farther. You will lose your job if you don’t consistently meet expectations, and you’ll only be rewarded with raises and advancement by going above and beyond the minimum expectations.

In our class, students who coast along and merely complete the minimum expectations of the course will generally earn a 70-79% score for E & P. Students who go out of their way to contribute to the success of their classmates, show initiative, emerge as leaders in groups, and consistently exceed expectations will earn higher E & P scores. Students who behave in ways that are problematic to others or degrade group dynamics will earn lower E & P scores.

Additional Details

Class may occasionally be cancelled to allow scheduling of small group meetings with the professor and/or TA for the consulting projects and design projects. These dates will be announced on Canvas and in class.

Many classes will be full or partial “Workshop Days”. On these days the class period will be driven by class discussions, problem solving work in small groups, and occasional small group meetings with the professor and/or TA.

Policies & Resources

Counseling and Psychological Services (CAPS)

Many students at Penn State face personal challenges or have psychological needs that may interfere with interfere with their academic progress, social development, or emotional wellbeing. The university offers a variety of confidential services to help you through difficult times, including individual and group counseling, crisis intervention, consultations, online chats, and mental health screenings. These services are provided by staff who welcome all students and embrace a philosophy respectful of clients’ cultural and religious backgrounds, and sensitive to differences in race, ability, gender identity and sexual orientation.

Counseling and Psychological Services at University Park (CAPS):

Penn State Crisis Line (24 hours/7 days/week): 877-229-6400

Crisis Text Line (24 hours/7 days/week): Text LIONS to 741741

ECoS Code of Mutual Respect

The Eberly College of Science Code of Mutual Respect and Cooperation embodies the values that we hope our faculty, staff, and students possess and will endorse to make the Eberly College of Science a place where every individual feels respected and valued, as well as challenged and rewarded.

Academic Integrity Statement

Academic dishonesty is not limited to simply cheating on an exam or assignment. The following is quoted directly from the “PSU Faculty Senate Policies for Students” regarding academic integrity and academic dishonesty:

Academic integrity is the pursuit of scholarly activity free from fraud and deception and is an educational objective of this institution. Academic dishonesty includes, but is not limited to, cheating, plagiarizing, fabricating of information or citations, facilitating acts of academic dishonesty by others, having unauthorized possession of examinations, submitting work of another person or work previously used without informing the instructor, or tampering with the academic work of other students.

All University and Eberly College of Science policies regarding academic integrity/academic dishonesty apply to this course and the students enrolled in this course. Refer to the following URL for further details on the academic integrity policies of the Eberly College of Science: http://www.science.psu.edu/academic/Integrity/index.html. Each student in this course is expected to work entirely on her/his own while taking any exam, to complete assignments on her/his own effort without the assistance of others unless directed otherwise by the instructor, and to abide by University and Eberly College of Science policies about academic integrity and academic dishonesty. Academic dishonesty can result in assignment of “F” by the course instructors or “XF” by Judicial Affairs as the final grade for the student.

Disability Policy

Penn State welcomes students with disabilities into the University’s educational programs. If you have a disability-related need for reasonable academic adjustments in this course, contact Student Disability Resources (SDR; formerly ODS) at 814-863-1807, 116 Boucke, http://equity.psu.edu/student-disability-resources. In order to receive consideration for course accommodations, you must contact ODS and provide documentation (see the guidelines at http://equity.psu.edu/student-disability-resources/guidelines).

Syllabus Changes

This syllabus is subject to change as circumstances warrant; all changes will be distributed in writing (usually electronically with Canvas) or announced in class.

This document was last modified on April 27, 2020