StatLab Statistical & GIS Consultants
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StatLab consultants help Yale faculty and students take advantage of our tools by offering application support and guidance with statistical methods and software. Our consultants, all graduate students, represent a diverse array of academic backgrounds who are hired based on their demonstrated quantitative and analytical performance.
How we work:
Our consultants work in a question-and-answer mode to help people with particular questions that come up in their research or classroom activities using statistical software and statistical methodologies. Consultants can offer advice including:
- "How should I enter to my data"
- "What statistical program is best for my project"
- "How do I get the program to run the analysis I want"
The consultants can not provide long tutorial sessions. The consultants can help describe and explain methods and procedures, but they can not prescribe particular methodologies or interpret results of analyses.
If your needs go beyond this scope, we suggest you contact your faculty advisor or TA about further instruction and participate in course work and/or workshops that will increase your expertise in the area of concern. The StatLab offers workshops on the major statistical packages throughout the year.
Consultants specialize in particular academic fields of study and in particular software packages. Knowing in advance the best match for your particular questions is possible by reviewing their areas of expertise. However, each consultant has extensive expertise in statistical computing and software support and can help you get started in all the basic packages supported by the StatLab. Give us a call at 432-3277, Monday through Friday, or fill out our contact page for further information about our consultation services.
Filter by expertise:
|Expertise:||R, Postgres, Python. Familiarity: MATLAB, Mplus, Ruby and monetDB|
|Statistical Topics:||General data science (supervised & unsupervised machine learning, feature extraction/generation, dimension reduction, big data/distributed processing and storage, database design). Traditional statistics: regression, mixed-effects modeling (especially longitudinal and clinical trial data), multilevel survey analysis, generalized estimating equations.|
|Interests:||Mental healthcare; healthcare technology; personalized medicine; applied machine learning; socially useful data science. Cycling, hiking, soccer and backgammon|
|Expertise:||R, Matlab, Python|
|Statistical Topics:||Social Network, Community Detection, Principle Component Analysis, Clustering, Spectral Methods, Decision Theory, Machine Learning, Neuron Network, Deep Learning|
|Academic Interests:||network analysis (e.g. community detection); theory and application of deep learning; minimax theory|
|Languages:||English, Mandarin Chinese|
|Expertise:||SPSS, Excel, Qualtrics, SurveyMonkey, experimental and survey design|
|Statistical Topics:||Casual inference; hypothesis testing; analysis of variance; linear and logistic regression; non-parametric tests; factor analysis; moderation/mediation|
|Academic Interests:||Power, status, and hierarchy; gender stereotyping and prejudice; bias against powerful women; shifting moral standards|
|Expertise:||SPSS, Amos, Excel, DSTAT, R, Qualtrics|
|Interests:||political mobilization, intergroup hierarchy and social inequality, GLM, regression, conditional process analysis, structural equation modeling, meta-analysis|
|Languages:||English, German, Dutch, French|
|Department:||Forestry and Environmental Studies (FES)|
|Expertise:||ArcGIS, R, ENVI, Excel, Google EarthEngine|
|Interests:||Ecological topics, raster analysis, big data, cartography, data visualization, remote sensing, map accuracy, developing new spatial analytical tools|
|Expertise:||STATA, Matlab, R for time series data analysis and other econometrics analysis, Excel, SPSS|
|Interests:||Institutional economic history and development, East Asian and comparative financial economics, industrial organization and behavioral economics|
|Languages:||English, Mandarin Chinese, Shanghainese, French|
|Department:||Forestry and Environmental Studies (FES)|
|Expertise:||Python, ArcGIS, QGIS, Google Earth & EarthEngine, R, bash, DOS|
|Interests:||Scale and aggregation issues, MAUP, multi-scale and fractal techniques, lidar, forest biomass, environmental and social justice, public education policy|
|Methodological Interests:||numpy, arcpy, pandas, geopandas, ipython notebook, matplotlib, pyplot, statsmodels, ArcScene, spatial analyst/map algebra, model builder, geostatistical analyst, LasTools|
|Expertise:||R, Stata, Qualtrics|
|Interests:||causal inference, experimental method, international conflict, nuclear proliferation|
|Department:||Epidemiology of Microbial Disease (School of Public Health)|
|Expertise:||ArcGIS, R, QGIS, RedCAP, ENVI|
|Interests:||Spatial Statistics, Spatial Epidemiology, Remote sensing, multivariate statistics, GLM, mixed models, integrating ecological and epidemiological datasets.|
|Languages:||English, Spanish, Portuguese|
|Department:||Epidemiology of Microbial Diseases (School of Public Health)|
|Expertise:||Matlab (statistical toolbox, parallel toolbox, mapping toolbox), R, JAGS(in tandem with Matlab or R), Stata (including epitab), basic ArcGIS, cluster computing, Latex.|
|Interests:||Health policy in developing countries, languages, climbing.|
|Methodological Interests:||epidemiological methods, dependent outcomes, dynamic models of disease transmission, probabilistic modeling, regression analysis (GLM, mixed effect models), MCMC algorithms.|
|Department:||Computational Biology, Bioinformatics|
|Expertise:||R, C, Perl, Matlab, Python, Latex and Machine Learning Methods|
|Interests:||Genomics, Sequencing data analysis, Machine Learning, Statistical Modeling, Information Theory, Microeconomics and Econometrics|
|Languages:||English, Mandarin Chinese, Cantonese|
|Expertise:||ArcGIS, R for social network analysis and spatial data analysis, GeoDa, STATA|
|Interests:||maps; violent crime in U.S. cities, gender-based violence, social networks, neighborhood effects; working with administrative and survey data (including the PHDCN & FFCWS); culturally-competent program evaluation for social services|