Statistical toolkit for ecologists (STKE01) CANCELED – Myuna Bay Sport & Recreation Centre was closed on Friday 29th March 2019 due to safety concerns. Details below
21 May 2019 - 24 May 2019£240 - £500
Myuna Bay Sport & Recreation Centre was closed on Friday 29th March 2019 due to safety concerns. The Office of Sport closed the Centre following advice from Origin Energy Limited that in the event of a major earthquake the nearby Eraring Power Station ash dam wall may break, posing a risk to a significant portion of the Myuna Bay Sport and Recreation Centre.
This course will cover advanced statistics regarding the analysis of data using comprehensive and state-of-the-art techniques. All methods will be illustrated using the free, open-source software package R. The course is designed for attendees that have a basic knowledge of R and elementary statistics and wish to advance this to up-to-date journal publication or conference proceedings standards. The course will cover mixed effects models, model selection and multi-model inference, generalized linear models (i.e. analysis of non-normally distributed datasets), random and fixed effects, variance nesting and spatial as well as temporal elements of data analytics. Participants will gain a deeper understanding of the models being fitted, and be able interpret the results appropriately as well as produce quality graphs. Participants are encouraged to bring their own data sets for applying the techniques taught as well as potential discussion with the course tutor.
This workshop is ideal for any scientists seeking an advancement to quantitative data analysis in ecology, population biology, environmental sciences, and veterinary epidemiology. No prior knowledge of the methods that will be covered is required. However basic prior R knowledge is required (loading data, loading R packages, basic R environment and basic R commands), as well as basic statistics knowledge, (what is a linear model, what is ANOVA) are required.
Venue – Myuna Bay Sport and Recreation, Wangi Road, Myuna Bay, New South Wales 2264 Australia, – Google Map – The nearest train station is Dora Creek railway station, Address: Dora Creek NSW 2264, Australia, it is located on the Main Northern line in New South Wales, Australia. It serves the City of Lake Macquarie town of Dora Creek. It is a 5 minute drive from the centre so a short affordable taxi ride.
Availability – 20 places
Duration – 4 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English
We offer COURSE ONLY and ACCOMMODATION PACKAGES;
• COURSE ONLY – Includes lunch and refreshments.
• ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast, lunch, dinner, refreshments. Accommodation is multiple occupancy (max 3 people) single sex en-suite rooms. Arrival Monday 20th PM and departure Friday 24th PM.
To book ‘COURSE ONLY’ with the option to add the additional ‘ACCOMMODATION PACKAGE’ please scroll to the bottom of this page.
Other payment options are available please email firstname.lastname@example.org
Cancellation policy: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact email@example.com Failure to attend will result in the full cost of the course being charged. In the unfortunate event that PRstatistics must cancel this course due to unforeseen circumstances a full refund for the course will be credited. However PRstatistics cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.
Dr Aristides (Aris) Moustakas
There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
Some prior knowledge of basic statistics is required (e.g. types of distribution, what is a dependent and independent variable (explanatory variables. No prior knowledge of R is required.
Assumed computer background
Basic computer knowledge is required; how to install new software, how to update packages etc.
Equipment and software requirements
A laptop/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links.
It is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) internet access may not always be available.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK firstname.lastname@example.org
Monday 20th –
Meet at the accommodation at approx. 17:00 onwards
Tuesday 21st – Classes from 09:00 to 17:00
Introduction, example data sets and challenges in their analyses
Revision: likelihood and inference
Revision: linear regression, GLMs
Analysing some example data sets
Fitting generalized linear models in real normally-distributed datasets
Checking model assumptions and residuals
Wednesday 22nd – Classes from 09:00 to 17:00
Generalizing the regression for many dependent variables
Model selection and multi-model inference
What is a fixed and what a random effect?
A full normally distributed data analysis, model fitting, ANOVA, residuals, plotting effects reporting results
Fitting mixed effects models with fixed and random effects
Nesting variances in random effects
Thursday 23rd – Classes from 09:00 to 17:00
Dealing with non-normally distributed data
Identifying the distribution of the data
Generalizing the linear model for non-normally distributed data
Plotting quality graphs
Identifying the distribution of the data through AIC model selection
Fitting the best model residual error structure in a generalised linear model
Understanding, reporting, plotting, and discussing results
Friday 24th – Classes from 09:00 to 16:00
Dealing with spatial & temporal data
Advantages of including spatial information
Problems induced by spatial or temporal autocorrelation
Plotting spatial data
Accounting for spatial or temporal autocorrelation
The instructors were excellent and clearly were the reasons for my previous comments. They both combined a deep understanding of statistics and ecology at the same level.Any questions or queries I’ve had, were thus first answered with an ecological point of view and then translated into statistical consideration thereby making much more sense on both side.In addition the course was very well organised, the course director and the two instructors were very friendly as well as professional. On the top of learning many useful things, I’ve also had a very good time during the week there.” Clement Garcia,
Spatial ecologist, Centre For Environment, Fisheries & Aquaculture Science (CEFAS), England
(Attended ADVR course)