STA 101 Advanced Applied Statistics for the Biological Sciences
Lecture: 3 hours
Laboratory: 1 hour
Basic experimental designs, two-factor ANOVA without interactions, repeated measures ANOVA, ANCOVA, random effects vs. fixed effects, multiple regression, basic model building, resampling methods, multiple comparisons, multivariate methods, generalized linear models, Monte Carlo simulations.
Prerequisite: course 100
In their course and lab work, undergraduate and graduate students from the Biological Sciences routinely apply statistical tests and other inferential procedures.STA 101 aims at developing facility in using statistical methods most commonly seen in applications in the Biological Sciences. The course gives an overview of the kinds of statistical analyses that can arise in scientific contexts and gives examples of applications using real data. The discussion of models and techniques involved is accompanied by the introduction of the relevant computational tools. After completing the course successfully, the student should be able to use a set of statistical methods useful for the Biological Sciences and beyond, be able to develop and execute with statistical software the statistical analysis for a scientific question of interest, be able to interpret the results of statistical analysis, and be able to communicate in the form of a scientific report.
Summary of course contents:
- Linear models: data sets, basic experimental designs, two-factor ANOVA without interactions, repeated measures ANOVA, ANCOVA, random effects vs. multiple effects, multiple regression, computing with, say, R packages lm and lme4.
- Model building: stepwise methods, model selection criteria (AIC, BIC). Resampling methods: bootstrap and permutation models.
- Multiple comparisons: family-wise error rate and false discovery rate. Multivariate methods: Principal components analysis, clustering and MANOVA (optional).
- Generalized linear models: logistic regression and Poisson counts data. Monte Carlo simulations.
- Dalgaard, P. (2008). Introductory Statistics with R, 2nd ed., Springer-Verlag, New York.
- Faraway, J.J. (2009). Linear Models with R., Chapman and Hall/CRC, Boca Raton, FL.
- Faraway, J.J. (2005). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Chapman and Hall/CRC, Boca Raton, FL.
- Ramsey, F. and D. Schafer (2012). The Statistical Sleuth: A Course in Methods of Data Analysis, 3rd ed., Brooks/Cole Cengage Learning, Boston MA.
Science & Engineering
There is some overlap with STA 106 and STA 108, but STA 101 is taught at a lower technical level, with a focus on computing skills and applications, and data analysis in the Biological Sciences.
First offered Spring 2015