Add/drop deadlines

The last date to add/drop this course without grade designation is 2025-01-22. Please act accordingly.

Time/Place

  • Discussion: Thursdays 11:00-12:30
  • Office hours: Mondays 16:00-17:00

Weekly schedule

Date Week Topic Due
Jan 13 1 Introduction and review  
     
Jan 20 2 Factor variables HW1
    Organizing your work ~ Factor variables  
Jan 27 3 Model formulas HW2
    Model formulas  
Feb 03 4 Building linear models  
    Building linear models  
Feb 10 5 Logistic regression HW3
    Logistic regression  
Feb 17 6 Generalized linear models HW4
    Generalized linear models ~ Journal reporting requirements  
Feb 24 7 Log linear models  
    Log linear models  
Mar 03 8 Tidyverse: Data transformations & tibbles HW5
    Data transformations ~ Tibbles  
Mar 10 9    
       
Mar 17 10 Tidyverse: Tidy data and relational data HW6
    Tidy data ~ Relational data  
Mar 24 11 Tidyverse: Strings  
    Strings, factors, and dates  
Mar 31 12 Data manipulation ecosystems in R HW7
    base vs tidyverse vs data.table ~ A taste of data.table  
Apr 07 13 Censored data HW8
    Survival analysis  
Apr 14 14 Divide and gather  
    Many models  
Apr 21 15 Linear mixed models HW9
    Linear mixed models  
Apr 28 16 Overflow HW10, Optional project draft (V0)
       
May 05 17 Overflow Compulsory project draft (V1)
    Project guidelines ~ BXD longevity data  
May 12 18 Student presentations Presentations, Final project (V2)
       

Course objectives

  • Apply sound project organization and reproducibility principles
  • Understand the model formula syntax for specifying regression models
  • Apply advanced regression techniques such as and generalized linear models (eg. logistic regression)
  • Apply tidy data principles to data manipulation and analysis
  • Analyze project data using appropriate statistical and computational methods

Other topics such as survival analysis and hierarchical models may be covered depending on student interest.

Pre-requisites

BIOE805 or consent of instructor.

Course director

Saunak Sen, PhD
Professor, Division of Biostatistics
Department of Preventive Medicine
645 Doctors Office Building
66 North Pauline Street
901-448-4590
sen@uthsc.edu

Grading

  • Participation: 20 (1 point per class, 4 points overall)
  • Homeworks: 40 (4 points per homework)
  • Final project: 40 (30 for paper and code, 10 for presentation)
Points Grade
00-40 F
41-50 D
51-60 C
61-80 B
81-100 A

See grading page for more details.

Course policies

General

  • Please be on time, and bring a laptop to class. If nobody shows up within the first 15 minutes of a regularly scheduled session or office hours, the meeting will terminate.
  • All assignments must be submitted on Blackboard by the time specified there.
  • Students are expected to follow the honor code.
  • Co-operation between students is permitted, and should be cited (eg. “Jane Doe helped me debug the code for producing the scatterplot.”).
  • If you have any questions, use the Blackboard discussion forum. This way everyone benefits. Use email only if the question is confidential.

Use of generative AI

  • Follow UTHSC policy.
  • Use of generative AI is permitted, but (a) you have to cite it and (b) you have to generate the prompts in your own words. Copying any class material into a generative AI application is violation of class policy.