| 1 |
Introduction and Basic Concepts |
moment statistics, distributions, transformations, t-test, correlation, chi-square, Dean’s roadmap of inferential statistics |
| 2 |
Resampling |
randomization, bootstrap, jackknife, Monte Carlo methods |
| 3 |
ANOVA MOdels |
single factor, factorial, nested and mixed models, pairwise comparisons, type I, II, and III SS |
| 4 |
Regression Models |
regression, multiple regression, analysis of covariance (ANCOVA) |
| 5 |
Matrix Algebra and LM |
review of matrix algebra; deriving the linear model in matrix form |
| 6 |
Multivariate Data and the GLM |
properties of multivariate data and data spaces, distance measures and metrics, MANOVA, MANCOVA, |
| 7 |
Interactions and Patterns of Change |
interpreting GLM interaction terms, understanding what causes significant interactions, dissecting patterns of change |
| 8 |
Ordination |
principal components analysis (PCA), and related approaches: principal coordinates (PCoA), multidimensional scaling (MDS), canonical variates/discriminant function analysis (CVA/DFA), correspondence analysis (CA) |
| 9 |
Clustering |
SAHN methods (UPGMA, WPGMA, etc.), K-means clustering |
| 10 |
Multivariate Association and Canonical Ordination |
Mantel tests, canonical correlation, partial least squares, RV coefficient, CVA, canonical correspondence analysis (CCA), redundancy analysis (RDA) |
| 11 |
Model Comparisons |
AIC, BIC, LRT methods |
| 12 |
Phylogenetic Comparative Methods |
phylogenetic non-independence, phylogenetically independent contrasts (PIC), phylogenetic anova/regression (PGLS), phylogenetic signal, rates of evolution |
| 13 |
Spatial Statistics |
point patterns, autocorrelation, correlograms/semivariograms, spatial non-independence (in GLS) |
| 14 |
Meta-Analysis |
combining results across studies, effect sizes, summary analyses, graphical approaches (funnel plots, etc.) |