Welcome to STA540!

Goal of the Course

One goal of STA 540 is to prepare you for work as a practicing statistician. Important skills required of practicing statisticians (in addition to having a great set of tools in the statistics toolbox!) include creativity, critical thinking, teamwork, the ability to identify needed new skills and to learn them with minimal direction, the ability to craft a statistical analysis plan to fit a scientific hypothesis or question, teamwork, and the ability to communicate to a variety of audiences (including other statisticians, experts in areas other than statistics, a supervisor at work, and the general public, among others).

Mini lectures will include some new material you may not have seen, as well as material that you have already seen but that is often poorly understood. My goal is for you to be as prepared as possible to tackle real-world problems. This means that questions of interest will typically not be framed in much statistical detail – part of your job will be to figure out which tools to use to analyze the data. You should expect to be working without a “play by play” guide.

Because of the nature of the course, we will encounter many more advanced topics that could easily be the subject of entire courses. For the benefit of those whose interest is piqued, I will post additional resources for further exploration. Part of the goal of this course is to learn how (and what!) to teach yourself in order to complete a project, but we’ll also delve more deeply into topics, time permitting.

Flexibility, work ethic, knowledge of statistical science, pragmatism, the ability to proceed with minimal direction, professionalism, communication skills, and the willingness to try new things are all strong predictors of success in STA 540 (as well as in the working world). Don’t worry if you haven’t mastered all of these – we’ll work on them in class!

Class Meetings

  Zoom (asynchronous)

  MTWThF (office hours with instructor or TA, some live lectures 2:00-3:15pm to be recorded)

Recordings will be placed on Sakai, arranged by date.

Teaching team and office hours

Note: office hours may vary. Revisions to office hours will be noted via announcements/e-mail.

Team member Office hours Location
Professor Amy Herring   Tuesday and Thursday, 3:15-4:00 EDT and by appointment Zoom (see Sakai)
TA Raphaël Morsomme   Monday and Wednesday, 7:00-8:00pm EDT and by appointment Zoom (see Sakai)

Materials

Texts and readings will be assigned as needed. We will support computation in R and RStudio.

Topics and Important Dates

STA 540 contain a mixture of short lecture, lab, and work sessions. Frequent smaller-scale assessments will be provided to check mastery and identify areas for additional focus. The schedule below lists deadlines for major assignments; the smaller-scale assessments will be assigned frequently (approximately every other day) with a tight turnaround to ensure no one falls behind.

Note: this schedule is approximate and is likely to be modified as the course progresses. Some sessions may be swapped due to course timing. Assignments are due by noon EDT unless otherwise specified.

Date Topic Deliverables
May 13 Welcome and Introductions, Course Goals, Writing Scientific Reports, and Popes
May 14 Case Study 1: Dosing Donkeys; Introduction of Lab Case Getting to Know You Video
May 15 What is a Good Model? Popes Assignment (Individual), Group Expectations (due Saturday by noon)
May 18 Evaluating Models; Writing Models Donkey Equation (Individual); Research Ethics and Privacy Forum Posts (Individual)
May 19 Loss Functions; Lab: Simulations
May 20 Q&A Individual Project Proposal (Report and 2 Minute Video)
May 21 Writing Methods, Results, and Discussion Sections; Lab Case Case Study 1 (Report, Video/Presentation, Reproducible Code); Feedback on Individual Proposals
May 22 Regression in Matrix Notation; Interaction (Watch Videos on Sakai) Case Study 1 Comments (including reproducibility); Individual Proposal Meetings
May 26 Case Study 2: China’s Economic Development and Household Income; Gini Index Case Study 1 Response and Revision
May 27 Recap of Case Study 1; Longitudinal Data Analysis: Mixed Effects Model; Lab on Simulation Individual Project Submission I: Introduction and Data Description
May 28 Mixed Effects Model, Covariance (Watch Videos - Work Day) Interaction Assignment (Individual)
May 29 Missing Data (Watch Video & Complete Quiz in Video); Meet about Project Feedback Comments on Individual Project Submission I
June 1 Interactions and Time Trends
June 2 Case Study 2 Q&A
June 3 Q&A on Individual Projects Case Study 2 (Report, Video, Reproducible Code)
June 4 Guest Lecture: Tom Milledge, Research Computing Case Study 2 Comments (peer and instructor, including reproducibility)
June 5 Lab: Working on the DCC
June 8 Case Study 3: Wearables and Stress; Lab: Data Processing on the DCC Case Study 2 Response and Revision
June 9 Team Work Day on Case Study 3 Individual Project Submission II: Methods and Preliminary Results (Report and Reproducible Code)
June 10 Brinnae Bent, Big Ideas Lab
June 11 Feedback on Individual Project Submission II; Q&A With Brinnae (7pm) Comments on Individual Project Submission II (peer and instructor)
June 12 Guest Lecture on Professional Opportunities: Prof David Banks; Feedback on Individual Project Submission II
June 15 Guest Lecture on COVID Modeling: Prof Alex Volfovsky
June 16 Individual Project Meetings Case Study 3 (Report, Video, Reproducible Code)
June 17 Individual Project Meetings
June 18 Individual Project Meetings Case Study 3 Comments (peer and instructor, including reproducibility)
June 19 Individual Project Videos and Discussion Individual Project Submission III (Report, Video, Reproducible Code)
June 22 Individual Project Meetings Case Study 3 Response and Revision; Individual Project Comments on Submission III (peer and instructor)
June 25 End of Summer I Celebration Individual Project Response and Final Revision (due at 2pm)