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Statistics For Biodiversity And Conservation (SFBC01R)

5th May 2025 - 9th May 2025

£400.00
Statistics For Biodiversity And Conservation (SFBC01R)

Event Date

Monday, January 31st, 2022

COURSE FORMAT

Pre Recorded

About This Course

The way statistics are used in biology, and especially ecology, is changing, with a shift from statistical tests of significance to fitting statistical models to data to explain causation and draw inferences to wider situations. And a new enlightened Bayesian world of statistical inference is also emerging.

An understanding of statistical modelling is no longer a luxury, and it is an expectation that postgraduates and post-doctoral researchers, as well as ecological practitioners possess an understanding of this approach. This change has been unleashed by an explosion in computing power and the advent of powerful and flexible software, such as R, that permits users to wrangle, analyse and visualise their data in novel ways.

This course is aimed at introducing researchers to analysing ecological and environmental data with GLMs using R. Study design will be discussed, as well as data analysis and statistical interpretation. Sessions will be a blend of interactive demonstrations and lectures, where learners will have the opportunity to ask questions throughout. Prior to the course, you will receive R script and datasets and a list of R packages to install.

By the end of the course, participants should be able to:

  • Apply data exploration techniques and avoid the common pitfalls in tackling a data analysis
  • Recognise common problems associated with analysis of ecological data and how to address them
  • Understand and apply alternative approaches to model selection
  • Apply statistical modelling methods to ecological data using GLMs
  • Recognise the distinction between frequentist and Bayesian approaches to model fitting
Intended Audiences

Post graduate or post-doctoral level researchers who wish to learn how to manipulate and analyse ecological data using R

Applied researchers and analysts in the environmental/ecological sector with a role in handling and analysing data

Course Details

Last Up-Dated – 03:06:2022

Duration – Approx. 35 hours

ECT’s – Equal to 3 ECT’s

Language – English

Teaching Format

Coming soon..

Assumed quantitative knowledge

Coming soon..

Assumed computer background

Coming soon..

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.
SFBC01R PRE RECORDED
SFBC01R PRE RECORDED
£ 400.00
Unlimited

PLEASE READ – CANCELLATION POLICY

Cancellations/refunds are accepted as long as the course materials have not been accessed,.

There is a 20% cancellation fee to cover administration and possible bank fess.

If you need to discuss cancelling please contact oliverhooker@prstatistics.com.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Day 1 – approx. 8 hours

Introduction to R and RStudio

  • Getting started with R and RStudio
  • Basic points
  • Navigating RStudio
  • Basic settings in RStudio
  • Basic principles in R
  • Setting the working directory
  • Importing data
  • Functions and packages in R

Data exploration

  • Six-step data exploration protocol
  • Outliers
  • Normality and homogeneity of the dependent variable
  • Lots of zeros in the response variable
  • Multicollinearity among covariates
  • Relationships among dependent and independent variables
  • Independence of response variable
  • Results of data exploration

Testing differences between two groups

  • European hedgehogs
  • Outliers
  • Normality and homogeneity of the dependent variable
  • Zeros in the response variable
  • Multicollinearity among covariates
  • Relationships among dependent and independent variables
  • Independence of response variable
  • Results of data exploration
  • Comparing two groups of normal unpaired data: unpaired t-test
  • Comparing two groups of normal paired data: the paired t-test
  • Comparing two groups of non-normal unpaired data: the Mann-Whitney test
  • Comparing two groups of non-normal paired data: the Wilcoxon test
  • Presenting results

Testing association between two continuous variables: correlation

  • Barn owls
  • Outliers
  • Normality of the variables
  • An excess of zeros
  • Multicollinearity among covariates
  • Relationships between variables
  • Independence of variables
  • Results of data exploration
  • Testing association between two continuous normal variables: Pearson’s correlation
  • Testing association between two continuous non-normal variables: Spearmann’s rank correlation
  • Testing association between two continuous non-normal variables with small sample size and ties: Kendall’s Tau correlation
  • Presenting the results

Day 2 approx. 8 hours

Modelling two continuous variables with linear regression

  • Northern pike length-fecundity relationship
  • Outliers
  • Normality and homogeneity of the variables
  • An excess of zeros
  • Multicollinearity among covariates
  • Relationship between variables
  • Independence of variables
  • Results of data exploration
  • Bivariate linear regression
  • Model validation
  • Homogeneity of variance of the residuals
  • Normality of residuals
  • Plot of the linear regression model
  • Absence of influential observations
  • Conclusions from model validation
  • Data transformation
  • Refit linear regression with transformed data
  • Model re-validation
  • Homogeneity of variance of the residuals
  • Normality of residuals
  • Plot of the linear regression model
  • Absence of influential observations
  • Model presentation and interpretation

Gaussian General Linear Model (GLM)

  • Diet of weatherfish in different seasons
  • Data exploration
  • Outliers
  • Normality and homogeneity of the variables
  • Lots of zeros in the response variable
  • Multicollinearity among covariates
  • Relationships among dependent and independent variables
  • Independence of response variable
  • Model fitting
  • Model validation
  • Homogeneity of residual variance
  • Model misfit
  • Normality of residuals
  • Absence of influential observations
  • Model presentation

Day 3 approx. 8 hours

Poisson Generalised Linear Model (GLM)

  • Abundance of freshwater mussels
  • Data exploration
  • Outliers
  • Lots of zeros in the response variable
  • Multicollinearity among covariates
  • Relationships among dependent and independent variables
  • Independence of response variable
  • Model fitting
  • Model validation
  • Overdispersion
  • Model misfit
  • Simulating from the model
  • Model presentation

Negative binomial Generalised Linear Model (GLM)

  • Species diversity of chironomids
  • Data exploration
  • Outliers
  • Lots of zeros in the response variable
  • Multicollinearity among covariates
  • Relationships among dependent and independent variables
  • Model fitting
  • Model validation
  • Overdispersion
  • Model presentation

Day 4 approx. 8 hours

Gamma Generalised Linear Model (GLM)

  • Common seal dive duration
  • Data exploration
  • Outliers
  • Normality and homogeneity of the variables
  • Lots of zeros in the response variable
  • Multicollinearity among covariates
  • Relationships among dependent and independent variables
  • Independence of response variable
  • Model fitting
  • Model validation
  • Homogeneity of residual variance
  • Model misfit
  • Normality of residuals
  • Absence of influential observations
  • Model presentation

Model selection

  • Common seal dive duration
  • Data exploration
  • Model selection
  • No model selection
  • Hypothesis testing
  • Classical stepwise selection
  • Information theoretic (IT) approach

Bernoulli Generalised Linear Model (GLM)

  • The presence of red spots on pumpkinseed fish
  • Data exploration
  • Outliers
  • Lots of zeros in the response variable
  • Multicollinearity among covariates
  • Relationships among dependent and independent variables
  • Independence of response variable
  • Model fitting
  • Model validation
  • Overdispersion
  • Model misfit
  • Absence of influential observations
  • Model presentation

Day 5 approx. 8 hours

Gaussian Generalised Linear Mixed Model (GLMM)

  • Body condition of European tree frogs
  • Data exploration
  • Outliers
  • Normality and homogeneity of the dependent variable
  • Lots of zeros in the response variable
  • Multicollinearity among covariates
  • Relationships among dependent and independent variables
  • Independence of response variable
  • Results of data exploration
  • Model fitting
  • Model validation
  • Homogeneity of residual variance
  • Model misfit
  • Normality of residuals
  • Absence of influential observations
  • Refit model
  • Model validation
  • Homogeneity of residual variance
  • Model misfit
  • Normality of residuals
  • Absence of influential observations
  • Refit model with random term
  • Model validation
  • Homogeneity of residual variance
  • Model misfit
  • Normality of residuals
  • Model presentation

Bayesian inference

  • Introduction to Bayesian inference
  • European bitterling territoriality
  • Data exploration
  • Outliers
  • Normality and homogeneity of the dependent variable
  • Lots of zeros in the response variable
  • Independence of response variable
  • Model fitting
  • INLA
  • Posterior (marginal) distributions
  • Comparison with frequentist Gaussian GLM
  • Model validation
  • Homogeneity of residual variance
  • Model misfit
  • Normality of residuals
  • Model presentation

 

Course Instructor

 
 
 

Dr. Carl Smith

Works at – 
Teaches – 

Dr. Mark Warren

Works at – 
Teaches – 

Details

Start:
5th May 2025
End:
9th May 2025
Cost:
£400.00
Event Category:

Venue

Recorded
United Kingdom + Google Map

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.
SFBC01R PRE RECORDED
SFBC01R PRE RECORDED
£ 400.00
Unlimited