The Statistical Consulting Lab
The Statistical Consulting Lab can assist your research by offering standard, advanced, and tailored made statistical methods, analysis and graphics:
Research Design
Assistance in planning experiments, sampling strategies, randomized treatment assignments in clinical trials and other experiments, determination of sample size, power analysis, development of research tools and questionnaires, including computerized data forms, logistical considerations and statistical help in preparation of research proposals.
Statistical Analysis
The Consulting Unit staff has expertise in a wide range of useful applied statistical methods:
- Data mining for large datasets and exploratory data analysis, visualization and graphical methods.
- Regression analysis: linear and non-linear models, logistic regression, non parametric regression (splines, and other smoothing methods), model selection for large problems. Cox proportional hazards regression, logit and Poisson regression.
- Analysis of variance (ANOVA) and covariance (ANCOVA), mixed models and repeated measurements.
- Multiple comparisons, FDR methodology, simultaneous and selective inference.
- Multivariate analysis, log linear analysis for contingency tables.
- Non-parametric statistics
- Bayesian methods
Application areas
We also have specialized knowledge in specific application areas:
- Genomics and bioinformatics
- Microarray Experiments
- Quality control design and management.
- Survival analysis.
- fMRI experiments
- Medical studies
- Clinical trials
- Geology, meteorology and climatology
- Geography, zoology and environmental studies
- Behavioral studies
- Marketing
Statistical Computing
Expertise in widespread statistical packages like SPSS as well as state-of-the-art applications and tailor-made graphics and methods in R (the open-source version of Splus), including their web implementations.
Report Writing
Summaries of the quantitative conclusions from statistical analyses that can be incorporated into research publications.
