Download Single response variable Multiple response variables Multivariate

Single response
variable
Response variable
continuous
Response variable
non-continuous
Correlations
Linear and non-linear
regression
•General Linear Model: Ordinary least
squares regression (OLS)
•General Linear Model: Multiple
regression
•Robust regression
•Generalized regression
•Mixed model regression
Multivariate approaches
Goodness of Fit tests
•Traditional correlation analyses
(e.g. Pearson, Bray Curtis)
•Non-parametric correlation (e.g.
Spearman rank)
Predictor
variables
continuous
Multiple response
variables
Regression models
•Logistic
•Probit
•Discriminant Function Analysis
Data are
continuous
One sample test of
empirical to known or
statistical distributions
•Kolmogorov-Smirnov test (KStest), Wilks Shapiro
Two sample test: compare
empirical distributions
•Kolmogorov-Smirnov test (KStest)
Predictor
variables both
continuous and
categorical
General Linear Model:
•
Analysis of Covariance (ANCOVA)
Generalized Linear
Modeling
Logistic models
T-test family
•One sample
•Two sample
•Paired
Predictor
variables
categorical
General Linear Model:
Analysis of variance
(ANOVA)
Contingency tables
(comparisons of empirical
distributions)
•Chi square
Data are
categorical
Mixed Models
•Generalized Linear
Model:
Generalized Linear Modeling
•Log-Linear modeling
•Logistic regression
•Other Maximum liklihood
approaches
Non-parametric approaches
•Kruskal –Wallis
•Wilcoxon matched pairs
•Mann-Whitney test
Goodness of fit test
One sample test to known
or statistical distributions
•Chi square test
MULTIVARIATE
Multiple response variables
Canonical Correlation
analysis
Predictor
variables
continuous
Single or partial Mantel test
RELATE procedure linking
matrices
OTHER USEFUL ANALYSES
Resampling: exact p-values
for testing with variables
with complicated
distributions
General Additive Models:
use of local smoothers to fit
data. Great for fit - hard to
use for hypothesis testing
MANOVA
Predictor
variables
categorical
ANOSIM/SIMPER
PERMANOVA
Principal Component Analysis for
continuous data
Data Reduction
approaches
Ordination
approaches
Correspondence analysis for Categorical
data
Hierarchical Clustering
K – Means Clustering
Link-tree approaches
Multidimensional scaling
Partial Least Squares
regression: useful for
collinear predictor variables