Foundations of Data Science II - Statistics
Spatial Statistics & Design of Experiments by Chris Brunsdon, MU, & Dr Norma Bargary, UL.
Date and time
Location
Kemmy Business School, University of Limerick (KBG12)
Plassey Avenue University of Limerick Limerick IrelandAbout this event
Day four of the Foundations of Data Science II is a full statistics day delivered by Prof Chris Brunsdon, MU, & Dr Norma Bargary, UL. Please ensure you bring a laptop for this interactive session.
Prof Chris Brunsdon will present on Spatial Statistics in the AM.
Abstract
In his talk Chris will explain the key ideas of spatial statistics, and provide a number of practical examples using R, and in particular the ‘mgcv’ package for analysis, and the ‘tmap’ and ’sf’ packages to manipulate and visualise geographical data. Practical worked examples will be available in blog form for reference after the talk is finished.
The talk is broadly divided into three sections:
1. Working with geographical data in R - including how read in spatial data, and use it to create maps via ’sf’ and ’tmap’
2. Spatial Statistical models with area-based data - including the use of Markov random fields to model broadband uptake in Ireland, based on census data for Irish electoral divisions
3. Spatial Statistical models with point-based data - including the analysis of historical rainfall data in Ireland.
Dr Norma Bargary will presents on the Design of Experiments in the PM.
Abstract
Experiments are widely used in science/engineering to: reduce the time required to design/develop new products or processes, improve current process performance, improve reliability of products, evaluate materials, etc. Experimental design is a means of collecting data on a system such that the data produced provide meaningful information about that system. It is a means of organising an experiment properly to ensure that the right type of data, and enough of it, is available to answer the question(s) of interest as clearly and efficiently as possible. This workshop will introduce the fundamental concepts underpinning good experimental design, familiarising students with the theory and applications of experimental design. It will demonstrate the importance of good experimental design when conducting research. It will introduce some commonly used experimental designs, e.g. completely randomised designs, randomised block designs, factorial and fractional factorial designs, and response surface methodology. It will discuss the advantages and disadvantages of each design and will show how to analyse the data produced using R.
Please see below details of the full week's training as you may be interested in other training days available:
Monday 2nd December (Applied Maths)
Tuesday 3rd December (Machine Learning)