CSTAT continues to conduct meetings and consultations via Zoom, but here we see part of the CSTAT team enjoying their lunch break in the beautiful MSU gardens.

Congratulations to Sichao Wang and Sanket Jantre on their outstanding work in supporting the teaching and learning missions of Michigan State University, and for earning the recognition of being one of this year’s #iteachmsu Educator Award recipients.

CSTAT invites applications for a Postdoctoral Research Associate starting in Fall 2021. Candidates must have a Ph.D. in Statistics, Biostatistics, or a quantitative STEM field (e.g. Social Sciences, Natural Sciences). Specific anticipated research topics are one of the following: high dimensional data analysis, text mining, natural language processing, Bayesian modeling.

In the Philosophy of Data Science Series here is an interview Andrew Gelman (Columbia University) (Columbia) and Megan Higgs (Critical Inference LLC) to discuss critical roles that statistics plays in science

April 5-9 is Graduate student appreciation week.
We appreciate all graduate students at CSTAT: Graduate students explaining their research projects to us and who are trying to figure out best statistical approaches as well as the CSTAT RAs who are excellent collaborators and consultants.

The Data Storage Finder is a self-service tool that allows you to determine which storage services are most suitable for your academic and research needs.

The Center for Statistical Training and Consulting (CSTAT, http://cstat.msu.edu) will be interviewing graduate students for possible 1/2 time Research Assistant (RA) positions in Summer 2021 and for the 2021-2022 academic year, with possible continuation into future semesters depending on performance, staffing needs, and available funding.

Statistical Modelling in Health Research: Best Practices for Description, Explanation and Prediction

A systematic process for Initial Data Analysis and clear reporting of the findings are essential to understand data properties or potential shortcomings of a dataset. This may have consequences for the planned statistical analysis and for the interpretation and presentation of the research results.

Data quality will be defined and an overview of selected data quality frameworks in medicine will be used to show the complexity of comprehensive data quality assessments. Tools for efficient data quality assessments to promote harmonized data quality reporting will be discussed.