For statistical analysis, we have professionals who can help with the following:

Measurement

• Validity and reliability of existing and proposed measures; bias and error patterns in measurement; dimensionality reduction; performance improvement; measurement instrument evaluation; latent trait analysis; vector and field measures; transformations; frequency-domain measurement techniques (spike separation, signal processing, wavelets, filtering).

Research design

• Factorial designs, blocking, split-plot designs, randomization, power analysis, contrasts, multiple comparison adjustments, response surface designs, repeated measures and covariates, cross-over and time series designs, retrospective studies, prospective single- and double-blind studies, case control studies, cohort studies, quasi-experimental designs.

Sampling

• Probability sampling: random, systematic, stratified, size, cluster, and multistage sampling; non-probability sampling: quota, purposive, and accidental sampling; political polling; census methods; sampling for industrial quality control; bootstrap resampling techniques.

ANOVA-related methods

• Univariate and multivariate analysis of variance, analysis of covariance, ANOVA with repeated measures, ANOVA for complex experimental designs.

Regression-related methods

• Multiple, polynomial, logistic, weighted, and nonlinear regression; data mining; survival analysis; principal components and factor analysis; errors-in-variables models; path analysis; Bayesian regression; robust and non-parametric regression; splines; time series analysis; spectral and cross-spectral analysis; forecasting.

Genomics and informatics

• DNA and RNA microarray analysis; BLAST-like algorithms; structural genomics; Monte Carlo methods; error modeling and analysis.

Filtering and image methods

• Kalman filtering; signal processing; image analysis and pattern recognition; ensemble and particle filters for spatial filtering.

Computational statistics

• Assistance with R, SAS, MatLab/Maple, Mathematica, Data Desk, and SPSS; numerical methods; statistical modeling and simulation; genetic algorithms; neural networks; hierarchical, partition, and spectral clustering; data fusion; dynamic and persistent statistical databases.

Mathematical statistics

• Derivation of maximum likelihood and Bayesian estimators; proofs of convergence; nonparametric and robust statistics in Hilbert space; stochastic calculus; stochastic differential and integral equations; stable distributions; multimodal distributions; implicit equation models; statistical theory for differential topology; statistical theory for nonlinear and chaotic systems; fuzzy statistics.