Statistical Advisory Unit

Description

The Statistical Advisory Unit is run by statisticians in the Department of Mathematics.

We provide help and support on research activities in the Faculty of Science and Engineering when expertise in statistical modelling/data analysis is required.

The purpose is to enhance our research profile in terms of outreach and income.

How it works

If we think we may be able to help after reviewing your request, we will contact you to arrange an initial meeting for half an hour or so to discuss the problem. If it is a straightforward case, we will be happy to give advice there and then. Otherwise it is down to the individual adviser and yourself to come to an agreement as to the best way forward towards a solution, with help from the department's finance office if necessary when contracts and fees are involved.

Bring a Statistician on Board

Statistical support if required should be budgeted for in grant applications, like computer officer time.

It is better to include a statistician as co-investigator if new methodology needs to be developed. An adviser may or may not be prepared to spend time working on a problem that is part of an existing grant.

Fees

There will likely be a fee payable for time spent beyond the initial free consultation. It will have to be agreed by all parties before the clock starts ticking so feel free to come to us with queries. The adviser may decide to waive the fee in cases of collaborative research leading to publications as joint author and new grant applications as co-investigator. Any advice given for free will not carry any guarantee or warranties and a fee is no substitute for acknowledgement or co-authorship in publications.

Contact us

Please fill in the booking form (available under Documents) and send it to statisticaladvisory.unit@manchester.ac.uk.

Our expertise

Georgi Boshnakov
•Exploratory data analysis
•Forecasting
•Graphics
•Multivariate analysis
•Numerical analysis and optimisation
•Probability
•Simulation
•Spatial statistics
•Statistical computing
•Statistical inference
•Time Series

Christiana Charalambous
•Exploratory data analysis
•GLMs and other non-linear models
•Longitudinal data analysis
•Simulation
•Statistical computing
•Survival analysis

Alexander Donev
•Bioassay
•Calibration
•Censuses and surveys
•Clinical trials
•Design and analysis of experiments
•Exploratory data analysis
•GLMs and other non-linear models
•Graphics
•Multivariate analysis
•Numerical analysis and optimisation
•Pattern recognition and image analysis
•Quality methodology
•Sampling
•Simulation
•Statistical computing
•Statistical inference

Ian Hall
•Exploratory data analysis
•Visualisation of outputs
•Numerical analysis
•Operational Research
•Probability
•Simulation
•Spatial statistics
•Statistical inference

Yang Han
•Simultaneous inference
•Multiple comparisons
•Personalised medicine
•Bioequivalence
•Clinical trials
•Analysis of longitudinal, multilevel and survival data
•Epidemiology of ageing and chronic disease

Thomas House
•Exploratory Data Analysis
•Network Data
•Fitting Complex Models to Data
•Prediction Under Uncertainty
•Biostatistics
•Epidemiology
•Population Health

Peter Foster
•Multivariate analysis
•Non-parametric statistics

Saralees Nadarajah
•Multivariate analysis
•Non-parametric statistics
•Probability Distributions
•Reliability
•Sampling
•Simulation
•Statistical inference
•Time Series

Jianxin Pan
•Clinical trials
•GLMs and other non-linear models
•Longitudinal data analysis
•Multivariate analysis
•Non-parametric statistics
•Numerical analysis and optimisation
•Simulation
•Spatial statistics
•Statistical computing
•Survival analysis

Timothy Waites
•Design of experiments
•Bayesian statistics
•Computer experiments

Jingsong Yuan
•Exploratory data analysis
•Forecasting
•GLMs and other non-linear models
•Multivariate analysis
•Non-parametric statistics
•Pattern recognition and image analysis
•Simulation
•Spatial statistics
•Statistical computing
•Statistical inference
•Time Series


AcronymSAU