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iAL's Nicole Satherley to present on social mobility project at two international conferences in May

Nicole Satherley will present on iNZight Analytics’ social mobility project at two international conferences in May 2026. 

Abstracts for the project were accepted for presentations at the International Association for Official Statistics conference in Vilnius, Lithuania during 12 – 14th May, and the RC28 Social Stratification (of the International Sociological Association) Spring Meeting in Seville, Spain during 20 – 22nd May.

The presentations will allow us to discuss initial project findings and the strengths of New Zealand’s national data resources to international experts in social stratification and official statistics.

You can read more about the project here, or at the link below.

20 March 2026
Publication

iAL's 2026 Summer Studentships come to a close

iAL are proud to have hosted another four students over the 2025/2026 summer break, all from the University of Auckland.

Gemma Ngari worked to develop a working prototype of a web app to visualise projections of the total population, working age population, and voting age population across a diverse range of Pacific nations.

Kasish Prasad's work looked at addressing challenges in classifying Pacific ethnic identity in official statistics. This had a particular focus on how statistics about Indo-Fijian populations is collected in New Zealand, Australia, and other countries, including Fiji.

Ken Deng's summer project aimed to improve iNZight software's iNZightRegression package by creating user-friendly tools for verifying model assumptions. Ken developed a comprehensive suite of interactive functions that guide users through checking key regression assumptions, including normality, linearity, multicollinearity and constant variance, combining statistical tests with clear visualisations.

Erena Tanabe's project focused on implementing Bayesian parameter estimation to the iNZight software using analytical methods. This included researching appropriate conjugate priors to use for the types of variables and variable combinations seen when exploring and analysing data, and coding methods for these to perform parameter estimation.

Supporting talented students is an important part of iAL's commitment to building a future applied statistics and data science workforce, and we're looking forward to seeing where these four take their careers

28 February 2026
Publication
27 January 2026
Publication