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Wildlife ecology research at Auburn University

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WILD 7150 - Advanced Analysis for Ecological Sciences



Note that this course has now been complete integrated into canvas. This website has been left up for those seeking information about the course. 

Course Materials:


Syllabus

Readings

Checklist of considerations one needs to make about their data and analysis

Panopto folder of recordings

Lecture and Lab Material

  Class Date Topics, Code, Data, and Readings for next class
  Lecture 1 08/19  Syllabus; Purpose of statistics; Truth versus facts; Reading for next lecture: Johnson 1999; Dushoff et al. 2019
  Lecture 2 08/21 Discuss Johnson 1999
  Lab 0 08/21 Introduction to R; CODE; - write some basic code in R
  Lecture 3 08/23 Basic linear regression; Data; CODE; Quiz 1 Due
  Lecture 4 08/26 Basic linear regression continued; presenting results;
  Lecture 5 08/28 Basic linear regression - testing assumptions; CODE; Data;
  Lab 1 08/28 Assignment #1 - Basic linear regression
  Lecture 6 08/30 Assumptions continued; Quiz 2 Due
    09/02 No Class - Memorial Day
  Lecture 7 09/04 Basic linear regression - prediction; CODE
  Lab 2 09/04 Assignment #2 - Advanced regression topics
  Lecture 8 09/06 T-tests, ANOVA, and general linear modeling; Data; Data2; CODE; Reading for next lecture: Ruxton and Beauchamp 2008; Quiz 3 Due
  Lecture 9 09/09 Post-hoc tests and contrasts; Data; CODE;
  Lecture 10 09/11 Continuous or Categorical?; discuss readings; Data; CODE; Reading for lecture: Cottingham et al. 2005, Steury and Murray 2005;
  Lab 3 09/11 Assignment #3 - Analysis of Categorical Data
  Lecture 11 09/13 Introduction to multiple variable modeling - why multiple variables?; Data; CODE; Quiz 4 Due
  Lecture 12 09/16 Multivariable modeling continued - collinearity; Powerpoint; optional reading: Freckleton 2011
  Lecture 13 09/18 Collinearity continued - confounding variables; Data; CODE;
  Lab 4 09/18 Assignment #4 - Collinearity
  Lecture 14 09/20 Multi-variable modeling - interactions; Data; CODE; Powerpoint; Quiz 5 Due
  Lecture 15 09/23 Interactions continued - meaning of coefficients; Reading for next lecture: Odadi et al. 
  Lecture 16 09/25 Discussion of Odadi paper
  Lab 5 09/25 Assignment #5 - Interactions
  Lecture 17 09/27 Random-effects models; Data; Code; Quiz 6 Due
  Lecture 18 09/30 Random- and Mixed-effects models continued; Data1; Data2; Code; Data3
  Lecture 19 10/02 Repeated Measures; Data; Code
  Lab 6 10/02 Assignment #6 - Mixed Effects Models
  Lecture 20 10/04 Nested designs; Code; Data1; Data2; Quiz 7 Due
  Lecture 21 10/07 Nested designs continued; Code; Data1; Data2; Reading for next lecture: Hurlbert 1984
  Lecture 22 10/09 Pseudoreplication; Nested designs continued
  Lab 7 10/09 Assignment #7 - Nested Designs
    10/11 No Class - Fall Break; Quiz 8 Due
  Lecture 23 10/14 Multi-variable modeling - tying it all together; Discussions of student data;
  Lecture 24 10/16 Discussion of student data - recognizing structure in data; Review for Mid-term
    10/16 Mid-term practical exam
    10/18 Generalized linear models - analysis of non-normal data; Poisson regression; CODE; Data
  Lecture 25 10/21 Review of mid-term. Generalized linear models continued
  Lecture 26 10/23 GLMs continued - Logistic regression;  CODE; Data1; Data2; Readings for next lecture: Murtaugh 2008; Whittingham et al. 2006
  Lab 8 10/23 Assignment #8 - Generalized Linear Models
  Lecture 27 10/25 Model selection - theory; Forward and backwards stepwise selection; Readings for next lecture: Anderson et al. 2000; Quiz 9 Due
  Lecture 28 10/28 Model selection; AIC; Optional readings for next lecture: Arnold 2010, Anderson and Burnham 2002; Data; Excel Table; Code;
  Lecture 29 10/30 Model Evaluation; Data; Code;
  Lab 9 10/30 Assignment #9 - Model selection and AIC
  Lecture 30 11/01 Introduction to multivariate analysis; Principle components analysis; Data; Code; Quiz 10 Due
  Lecture 31 11/04 Multivariate analysis continued - Factor analysis; Code; Data1; Data2
  Lecture 32 11/06 Multivariate analysis continued - Discriminant function analysis; Code; Data1
  Lab 10 11/06 Assignment #10 - Multivariate analysis
  Lecture 33 11/08 Statistics when there are no statistics - into to randomization procedures; Example; Quiz 11 Due
  Lecture 34 11/11 Advanced Bootstrapping; Data; Code;
  Lecture 35 11/13 Basics of Good Experimental Design; Readings for next lecture: Dennis 1996; Ellis 2004
  Lab 11 11/13 Assignment #11; - Boostrapping
  Lecture 36 11/15 No Lecture - Instructor Traveling; Quiz 12 Due
  Lecture 37 11/18 Basics of Bayesian analysis; Bayesian analysis in R; Data; Code;
  Lecture 38 11/20 Bayesian analysis of Ellis Data;
  Lab 12 11/20 Assignment #12 - Bayesian Analysis
  Lecture 39 11/22 Theory and assumptions of structural equation modeling; Quiz 13 Due
      Fall Break - November 25-29, no class!
  Lecture 40 12/02 Structural equation modeling;  Readings for next lecture: Karels et al. 2008; Data1; Data2; Code1; Code2
  Lecture 41 12/04 Structural equation modeling continued; Karels' analysis;
  Lab 13 12/04 Assignment #13 - Structural Equation Modeling;
  Lecture 42 12/06 Review and sum it all up; Final reading: Guthery 2008; Quiz 14 Due
  Final Exam 12/12 Final Exam at 8:00 AM
       

Auburn University School of Forestry and Wildlife Sciences © Todd Steury 2008