4 Day 4 (June 4)

4.1 Announcements

• Friday donuts and extra credit!

• Recommended reading

  • Chapters 1 and 2 (pgs 1 - 27) in Linear Models with R

• Questions and comments from journal

  • If you showed up to class, handed in a journal but got a zero….
  • How do Bayesian models work?
  • More data leading to a better model?
  • “I think you touched on this a today when addressing the student’s comments, but something I struggle with is determining how to “build” a model and what that really means. Are there ever any assumptions you can’t put into a model. How do you know if an assumption is confounding? I think a lot of times in graduate school we are given some code/ and or “model” but not really taught what that means, and I am trying to undo the past confusion that was created around this terminology.”
  • “If both mathematical and statistical models are built on assumptions, how do we decide which assumptions are acceptable, and at what point does a model become too assumption-heavy to be trustworthy?”
  • “I would like to understand when using a point prediction is better than a distribution prediction. It seems like distribution predictions are what we usually use in the wildlife field. Do other fields also commonly use distribution predictions?”

4.2 Intro to statistical modelling: 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)

• Further reading/learning

  • Hooten, M.B, D.S. Johnson, D.S., B.T. McClintock, J.M. Morales (2017) Animal Movement: Statistical Models for Telemetry Data. CRC press