2 Day 2 (January 22)

2.1 Announcements

• Correction about Canvas

  • Journals should be uploaded to Canvas 24 hours after lecture ends
  • Activity 1 will eventually be uploaded to Canvas (but no due date yet)

• Please start activity 1

• Questions/clarifications from journals

  • “What data set can I use for the class project?”
  • “My question is how to deal with uncertainty or any unexpected situation that happens.”
  • “I definitely need to confess that I don’t have a solid understanding of Bayesian statistics.”
  • “The brief mention of telemetry data made me wonder whether we’ll need to deal with very large, irregularly sampled, or noisy time-series data right away, or if we’ll build up to that gradually.”
  • “Difference between dynamic and descriptive approaches”

2.2 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)

2.3 Statistical models

• Read pgs. 77 - 106 in Wikle et al. (2019)

• What is a model?

  • Simplification of something that is real designed to serve a purpose

• What is a statistical model?

  • Simplification of a real data generating mechanism
  • Constructed from deterministic mathematical equations and probability density / mass functions
  • Capable of generating data
  • Generative vs. non-generative models