About

The Department of Mathematical Sciences has a long history of training applied statisticians. Currently, the Department offers a MS degree in Mathematics, a PhD in Mathematics, and a PhD in Biostatistics. The Center for Applied Statistical Consulting and Collaboration Initiative (ASCI) exists as a unit within the Department of Mathematical Science and provides a separate but symbiotic environment towards statistical consulting. ASCI provides advice and support to all IUPUI researchers and other non-university clients within statistics and biostatistics, including but not limited to: consultation, collaboration, and educational outreach.

ASCI provides advice on statistical problems arising in the preparation of studies, the analysis of data and the interpretation of results. This service can provide, for example, direct assistance in carrying out data analysis and report writing.

Mission and Purpose

Following the IUPUI life science related initiative, ASCI provides special emphasis on statistical research related to biology and medical science. The ASCI can provide help on statistical issues arising at any stage of the research process:

• NIH/Federal grant writing support related to Statistical Analysis
• Power analysis and Sample size determination
• Design of Experiments and Surveys
• Preparation of Proposals
• Data Management
• Data Analysis and Modeling
• Interpretation of Results
• Preparation of Reports
• Statistical Software Issues
• Long-term Collaborative Work

To see individual faculty expertise please visit our research web page.

Areas of Expertise

Under the supervision of faculty members, ASCI provides assistance in:

• Questionnaire development
• Bayesian data analysis
• Time series and analysis of financial data
• Longitudinal studies
• Quality control (Chart selection and data interpretation)
• Optimization
• Validation of models and assumptions
• Behavioral and Rehabilitation studies
• Modification or creation of novel statistical methodology when assumptions are violated
• Multiple comparisons and tests
• Analysis of high dimensional data
• Statistical data mining