|
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 |
|
|
|
|