What do statisticians do all day?

Yesterday and today we had talks by our postgraduate students about their short research projects

• An investigation into criticism of the FST software. (forensic DNA analysis: you want it to be right)
• Comparison of accuracy between different algorithms for solving the Normal Equations in Regression Analysis (It’s hard to get big rounding errors nowadays)
• Multi-catchment Streamflow Modelling by Reduced-rank Regression (improving hydropower modelling)
• A Study on Equity in Academic Outcomes in First Year Statistics Courses (Could do better)
• Costs and Financing of Routine Immunisation (estimating cost components in low-middle income countries)
• Statistical Examination of the Relationship between Maternal Diet, Metabolome and Gestational Diabetes Mellitus (predicting diabetes is hard, especially in the future)
• Robustness of Spatial Capture-Recapture Models to Misspecified Detection Functions (if you’re listening for whales or gibbons, how close do you need to be?)
• An Examination of Participant Perspectives on the Scampy Tool (teaching the ideas of randomness at an introductory level)
• Who lives in deprived places? The association between individual and area level socioeconomic position (guessing someone’s income from where they live isn’t all that reliable)
• Is LIBS a reliable technology for the forensic analysis of glass? (more forensics, but with lasers)
• De-batching data from a complex experiment (life would be simpler if you didn’t have to worry about lab ‘batch effects’ when studying ocean acidification)
• Optimal path in random graphs (maths about really big networks)
• Spatial Distribution of fish on the Chatham Rise (if you want to count them, you need to know where to look)
• Exploring climate variables (in particular, extreme values like hottest place, rainiest day)
• Interactive Tools for Climate Data (for browsing through NZ historical weather data)
• How old is that mud: Convex Biclustering Applied in Tephrostratigraphy of the Orakei Basin (Looking for volcanic ash layers in mud samples)
• Comparison of Methods for Inferring Granger Causality (time series techniques for economists)
• Predicting Patronage (how does Patreon support vary over time, and can you predict it?)
• Expected Information Matrices for Some Poisson Variants. (Calculations and software for some new counting models)