Course description:

(This is now obsolete because the restructuring of biostats courses in Biology
Department. This course will now cover mainly multivariate statistics, but with
studentrequested topics having priority)
Below you will find an outline of topics intended to be covered by the Applied Statistics grad course. The main goal is to understand the rationale of these statistical analyses, to use SAS to carry out the relevant statistical analysis and to properly interpret SAS output.
However, what is the most important for a biologist is to analyze and reanalyze the biological problem before any analysis of the data (or ideally before the experiment and data collection.
R. A. Fisher once remarked that "To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of."
(I may not be able to cover all the topics listed below.)
 Statistics and decision making
 SAS software
 Statistical estimation and significance tests (descriptive and hypothesisdrive research)
 Probability and statistical distributions
 Onesample hypothesis, power and sample size in statistical tests
 Dependent and independent variables (DV and IV), continuous and categorical variables
 Statistics involving one set of continuous DVs and one set of categorical IVs
 Twosample hypotheses (one continuous DV with one categorical IV of two categories)
 ANOVA (analysis of variance, one continuous DV with one or more categorical IVs of two or more categories), multiple comparisons, familywise error rate and false discovery rate.
 MANOVA (multivariate analysis of variance, multiple continuous DVs with one or more categorical IVs of two or more categories)
 ANCOVA (analysis of covariance, one continuous DV with one or more categorical IV plus one or more continuous covariates)
 Data transformation and normalization
 Statistics involving two sets of continuous variables
 With one set is DV and another is IV
 One continuous DV and one continuous IV: simple linear and nonlinear regression
 One continuous DV and multiple continuous IVs: multiple linear regression, polynomial regression
 Multiple continuous DVs and multiple continuous IVs: multivariate regression (optional)
 One binary DV and one or more continuous IVs: logistic regression.
 With no IV/DV designation
 Bivariate correlation
 One set contains a single variable, the other contains multiple variables: multiple correlation and partial correlation
 Both sets contains multiple variables: canonical correlation
 Other multivariate statistical methods
 Principal component analysis
 Correspondence analysis
 Cluster analysis
 Discriminant function analysis
 Data analysis of categorical variables
 Goodness of fit test
 Twoway contingency tables
 Loglinear models for multiway contingency tables
 Statistical criteria for model selection

Reference book

 Sokal, R. R., Rohlf, F. J., 1995. Biometry. Freeman, New York. (Any edition of the book would be fine)
 Zar, J. H., 1999. Biostatistical analysis. Prentice Hall, Upper Saddle River, New Jersey. (Any edition of the book would be fine)
 Any textbook on multivariate statistics for biologists
