Statistical Consulting Hub

  1. Macquarie University
  2. Faculty of Science and Engineering
  3. Schools and departments
  4. School of Mathematical and Physical Sciences
  5. Engage with us
  6. Statistical Consulting Hub
Karol Binkowski See how we can support your statistical needs Learn more about our statistical expertise We are involved in a variety of consulting projects

Top statisticians providing tailored support

We are excited to announce the pilot of the Statistical Consulting Hub within the School of Mathematical and Physical Sciences at Macquarie University.

The Hub unites our statisticians to provide tailored support for research and industry-facing projects. We help organisations without in-house statisticians turn their data into clear, confident decisions. Leveraging our industry experience, academic background, and a network of statistical experts, we translate complex problems into simple, actionable insights.

Our clients use their data with clarity and purpose for:

  • tailored workshops (in-house or on-campus)
  • expert advice
  • data analysis
  • manuscript assistance.
WorkshopDateContent
Designing statistical studies and modelsTBA

Design of statistical studies (identifying variables, their types; finding appropriate statistical models)

GAMM Tuesday 16 June 2026, 10.30am – 2pm

Generalised Additive Mixed Models (GAMM)

Time series + visTuesday 14 April 2026, 10.30am – 2pm

Temporal data:

  • time series analysis
  • data visualisation for time series.
PCA/factor analysis+ BayesianTBA

Multivariate:

  • PCA and factor analysis
  • Data visualisation (communicating multivariate patterns)
  • Bayesian approaches (for latent structures and uncertainty).
LMM precursor Wednesday 19 November 2025, 10.30am – 2pm

Precursor to Linear Mixed Model (LMM):

  • assumptions of linear models in R and SPSS
  • LMMs: model structure, EBLUPs, likelihood method
  • random intercept and random slope in R.
LMM Wednesday 23 September 2025, 10.30am – 2pm

Linear Mixed Models

  • linear mixed effects modelling in R
  • estimation, EBLUPs, and model diagnostics
  • model selection, likelihood ratio tests.