StatLab

We work with teachers, learners, and researchers at Yale University to support statistics and data analysis.

Our services:

Drop-in help: Questions on correlations, calling APIs, or causal effects? Our virtual drop-in help sessions are designed to tackle your specific needs. All of our consultants can discuss basic statistical and data analytic techniques. However, when you are looking for help with a specific program or topic, we encourage you to find the right consultant and check our schedule before you visit. See our consultant schedule.

Workshop instruction: Getting started with a new tool or program? Attending our introductory and topical workshops can make it easier. Check out the workshop schedule.

Our Consultant Team

Our consultation team consists of graduate students from across Yale University.

Each consultant can discuss basic statistical and data analytic techniques. However, when you are looking for help with a specific program or topic, we encourage you to find the right consultant and check our schedule before you visit.

What expertise are you looking for?

Our consultants have a working knowledge of many techniques and tools. However, they also have expertise on specific tools and methodologies, do their own research in particular academic fields, and provide consultation services at different locations across campus.

Samantha Dean	Department: Biostatistics Software and Computing Topics: R, Python for data science, basic HPC / parallelization Methodological Topics: Causal inference, generalized linear models, survival analysis, data wrangling and visualization, simulations Research Interests: Causal inference, policy evaluation, methods for infectious disease epidemiology Spoken Languages: English
Srishti Goel	Department: Psychology Software and Computing Topics: R, SPSS, MATLAB Methodological Topics: Data wrangling and visualization in R, generalized linear and logistic regression including mixed-effects models, survey design, reliability testing, hierarchical clustering, topic modeling, basic knowledge of data wrangling and visualization in MATLAB Research Interests: Affective science, social and cognitive psychology, cross-cultural psychology Spoken Languages: English, Hindi
Mahima Kaur	Department: Yale School of Public Health - MSc Health Informatics Software and Computing Topics: R, SAS, SPSS, Python, Excel Methodological Topics: Data Wrangling and Visualization, Hypothesis Testing, Statistical Analysis and Interpretation, Regression Analysis, Unsupervised and Supervised learning Research Interests: Data Mining, Machine Learning, and Natural Language Processing of Electronic Health Record Data Spoken Languages: English, Hindi, Punjabi
Jinge Li	Department: Yale School of the Environment Software and Computing Topics: R, Stata, Python Methodological Topics: Statistical analysis and interpretation, data visualization (ggplot, levelplot), GIS, data wrangling Research Interests: Environmental and resource economics Spoken Languages: English, Chinese
Polina Ovchinnikova	Department: Yale School of Public Health (Health Informatics) Software and Computing Topics: R, SAS, MATLAB, Python, LaTeX, Excel Methodological Topics: data analytics, statistical analysis, data visualization (ggplot, adobe), simulations Research Interests: Data Compression and error-correction, as well as health information exchange Spoken Languages: English, Russian
Lucinda Sisk	Department: Psychology Software and Computing Topics: R, Python, MATLAB, SPSS Methodological Topics: Data wrangling, data visualization, data analysis, linear regression, linear mixed-effects models, lasso and ridge regression, machine learning techniques Research Interests: The effects of childhood environment on structural and functional brain development Spoken Languages: English

Drop-In Help

StatLab offers drop-in help sessions to support statistics and data analytics in a consultative setting.

Before you come for a drop-in help session, take a few minutes to get ready. There are four simple steps.

Read about the service
Find a consultant (see above)
Find a time
Get ready and prepare your questions (see below)

How do drop-in help sessions work?

We work to make the service as accessible and valuable as possible.

We do not charge a fee for the service
We welcome anyone affiliated with Yale University
No appointments are necessary; we help on a drop-in basis
Consultations are only available virtually, via Teams
We welcome you to use as many sessions as are needed, but our team may limit the amount of time we work with you on a specific day if others are waiting
The sessions are active two-way conversations; you will need to work with our consultants as they suggest solutions

The best sessions are driven by your questions. We’ve got some suggestions on how to get ready for a session. Here are examples of questions you might ask us.

How do I import this data into SPSS or R?
Which tool is best for complex web scaping?
What does this error message in R mean?
Should this variable be a fixed or random effect? How do I model it in R or Stata?
How do I visualize these data?
How can I create lagged variables in Stata?
What type of regression model is appropriate for my data?
How do I move my data from wide to long format?
How do I know if my machine learning model is any good?

Even when questions do not have a single “right” answer, we will do our best to help you by walking through the problem and brainstorming possible solutions.

When are drop-in help sessions NOT recommended?

Drop-in help sessions are not always the best resource for your problem.

We do not provide tutoring, but we can help you find one from the Center for Teaching and Learning or one of the residential colleges
Drop-in help is not dedicated and sustained support for an individual project, but we may be able to provide you suggestions for finding a more long-term consultation partnership on campus
Consultants cannot act as course-dedicated teaching fellows, but we work with instructional teams in other ways to provide curricular support. We have guidelines that inform how we work with learners in courses and others using course materials.

Get Ready

Our goal is for consultation sessions to be as valuable and effective as we can make them. Based on our experience, we have several suggestions to help you get the most out of the experience.

Be ready to teach us. We have a lot of experience and expertise. But you have been thinking about your research and problem longer than we have. We don’t know the idiosyncrasies of your situation, data, and approach. We’ll have questions that you’ll have to answer. They might seem obvious to you, but it is part of the process.
Be ready to learn. We want to solve your problem. But we also want you to be able to solve it in a month or two when it happens again.
Find the right consultant. Everyone on our team uses different tools and has different skills. We share that information with you so you can find the right match.
Open your code and data. We’ll be able to spend more time talking about your problem if you are ready to jump in and have already eliminated any issues with reading in your data.
Get your environment ready. If you are using a laptop, we want to help you solve the problem on that same laptop. To help us do that, you should make sure you have any required software installed and that you can reproduce the problem you are having.
- Simple examples that reproduce or illustrate your issue are easier for us to work with and you’ll get the solution faster. The StackOverflow page on reproducing issues is helpful.
Ask yourself questions. Just like doctors and nurses, we ask questions to get a quick understanding of the symptoms you are experiencing. We use diagnostics to pinpoint a cause, and we give you suggestions for treating the problem. You can get into that mindset if you start by asking yourself some questions before you come.

What type of question do you think you have?
- Is it conceptual and software agnostic?
- Is it specific to a particular tool or program?
- Is it about finding resources (e.g., tutorials, datasets)?
What is your specific goal for this session (or multiple sessions)? How would you briefly explain your goal to somebody with no background in your particular research area?
Are there examples of research (published or unpublished) that use the kinds of tools and techniques you are using?
Are there examples of people with the same problem? What worked in those cases (you might look at StackOverflow or CrossValidated)?

Drop-In Help Schedule

Please check the calendar for times and locations, as they may change.

Consultations may be limited to 30 minutes in length.

Workshop Calendar

The StatLab offers a range of workshops for beginners and topical workshops for users to get familiar with new-to-you functionality and features.

Statistical Support Workshop Materials

*iFrame not loading? You can also access the materials here: https://yale.box.com/s/0c4v1an8hlozxxj4j3dgdwg1k8znmh80

Yale Library