1 Day 1 (January 20)

1.1 Welcome

• About the instructor

• Syllabus

  • Recommended book
  • Recomended software
  • Office hours
  • Zoom office hours
  • Reproducibility requirement (data analysis can be successfully repeated by someone other than you)
  • Academic Honesty: working in groups, sharing code, use of AI, etc.

1.2 Course pre-assessment

• Download here
• Due Friday January 23th

1.3 Course format

• Previous course reflections and changes

• My philosophy and what I can offer

  • Is the course right for you?
  • How to learn statistics?
  • How to spend your time?
  • A prediction about the future

• Course design

  • Phase 1-Crash course in spatio-temporal statistics
    • Standard lectures with a good amount of reading
    • Finish before spring break
  • Phase 2-Problem based learning
    • Phase 2 is designed to mimic research
    • Data/question motivated applications
      • Rainfall mapping from flood event (see here)
      • Heat/temperature mapping/prediction (see here)
      • Contact tracing (see here)
      • Earthquake prediction in space and time (see here)
      • Elevation of Dickens Hall parking lot!
    • If you have a data set or problem you would like me to consider using in class please send me an email with a short description
    • Phase 3-Class project of your choice

• Grades?!

  • Bi-weekly journal (40%)
  • Activities (10%)
  • Final project (50%)
  • What about traditional homework assignments and exams?
    • Activities vs assignments?

• How to best interact with me

  • Depends on your career path
  • Easy to access online once we are established
  • Please make good use of our time (e.g., zoom vs. in-person)

• How to best interact with students in this class

  • Huge diversity of majors and professions
  • Huge diversity of skills
  • A comment about your KSU degree and amount of statistics
  • Who is in this class?

  url <- "https://www.dropbox.com/scl/fi/79f3hgz4wwvyufgc5vnor/students_STAT_764.csv?rlkey=vqwnt0krz7d7xz44acowg5bop&dl=1"
  df <- read.csv(url)
  
  par(mar=c(13,2,2,2))
  plot(rev(sort(table(df$degreeProgram))),las=2,xlab="",ylab="Number of students",ylim=c(0,5))

  

  par(mar=c(13,2,2,2))
  plot(rev(sort(table(df$classLevel))),las=2,xlab="",ylab="Number of students",ylim=c(0,13))

  

1.4 Reading

• Read pgs. 1-15 in Wikle et al. (2019)

1.5 Opening example: Human movement

• The goal of this activity is to show you how cool spatio-temporal statistics is!
• Human movement modeling with the linear regression model and other fancy tools!
• Trajectories are a time series of the spatial location of an object (or animal).
  • We can usually pick the object and the time that we obtain its spatial location (i.e., time is fixed)
  • The location is a random variable in most cases, but time can also be a random variable.
• In-class marathon example (Download R script here)