Add/drop deadlines

The last date to add/drop Fall term courses without grade designation is 2024-01-15. Please act accordingly.

Time/Place

  • Discussion: Thursdays 11:00-12:30

Weekly schedule

Date Week Topic Due
Jan 11 1 Introduction and review  
    HW1  
Jan 18 2 :snowflake: :snowflake: :snowflake: HW1
       
Jan 25 3 Factor variables  
    Organizing your work ~ Factor variables ~ HW2 ~ HW2 notes  
Feb 01 4 Model formulas HW2
    Model formulas  
Feb 08 5 Building linear models  
    Building linear models ~ HW3 ~ HW3 notes  
Feb 15 6 Logistic regression HW3
    Logistic regression ~ HW4 ~ HW4 notes  
Feb 22 7 Generalized linear models HW4
    Generalized linear models ~ Journal reporting requirements  
Feb 29 8 Log linear models  
    Log linear models ~ HW5 ~ HW5 notes  
Mar 07 9 Tidyverse: Data transformations & tibbles HW5
    Data transformations ~ Tibbles ~ HW6 ~ HW6 notes  
Mar 14 10    
       
Mar 21 11 Tidyverse: Tidy data and relational data HW6
    Tidy data ~ Relational data  
Mar 28 12 Tidyverse: Strings  
    Strings, factors, and dates ~ HW7 ~ HW7 notes  
Apr 04 13 Data manipulation ecosystems in R HW7
    base vs tidyverse vs data.table ~ A taste of data.table ~ HW8 ~ HW8 notes  
Apr 11 14 Censored data HW8
    Survival analysis  
Apr 18 15 Divide and gather  
    Many models ~ HW9 ~ HW9 notes  
Apr 25 16 Linear mixed models HW9
    Linear mixed models ~ HW10 ~ HW10 notes  
May 02 17 Overflow HW10, Optional project draft (V0)
       
May 09 18 Overflow Compulsory project draft (V1)
    Project guidelines ~ BXD longevity data  
May 16 19 Student presentations Presentations, Final project (V2)
    Order: Lee, Glasper, Belachew, Boateng, Gebreyesus, Twum, Nnamani  

Course objectives

  • Understand the model formula syntax for specifying regresison models
  • Implement advanced regression techniques such as and generalized linear models (eg. logistic regression)
  • Manipulate “messy” data into a “tidy” form

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 would constitute a violation of class policy.