Introduction to statistics and R for everyone (IRFE01)
18 March 2019 - 22 March 2019£450
This course will provide attendees with the opportunity to learn how (a) to understand and read modern statistics reported in scientific studies, (b) use basic modern statistics for analysing their own data, using the open access R statistical software. The course will focus more on data deriving from life sciences namely medicine, biology, and ecology. The course will initially revise basic statistical knowledge of what is a sample and a distribution, and what is hypothesis testing. Sequentially, attendees will be introduced to the R statistical software. Then the course will proceed with the use of generalised linear models and their equivalency with t-tests, ANOVA, MANOVA and ANCOVA for analysing normally as well as non-normally distributed data and ultimately quantify results, errors, and uncertainty. Attendees will also learn how to produce quality graphs and figures.
This workshop is ideal for scientists any scientists seeking an introduction to statistical inference and data analysis coming from diverse scientific disciplines such as medicine, biology, and ecology. No prior knowledge of R is required. However, some prior knowledge of basic statistics is required (e.g. types of distribution, what is a dependent and independent variable (explanatory variables, e.g. body weight).
Venue – Pasteur Hellenic Institute, Athens, Greece – Google map
Availability – 25 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English
We only offer a COURSE ONLY package for this course.
• COURSE ONLY – Includes lunch and refreshments.
To book ‘COURSE ONLY’ please scroll to the bottom of this page.
Other payment options are available please email email@example.com
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 firstname.lastname@example.org 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.
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Monday 18th – Classes from 09:00 to 17:00
1-1) Revision of basic statistics: what is a distribution, sampling, data types, factors, basic statistical tests
1-2) Introduction to the R environment
1-3) Packages, names, data types
1-4) Read, write, access, manipulate data
1-1) Install R packages
1-2) Load datasets
1-3) Perform basic statistics, t-tests, ANOVA
Tuesday 19th – Classes from 09:00 to 17:00
2-1) Experimental design, probability distributions, parameter estimation, conﬁdence intervals
2-2) Null hypothesis testing
2-3) Multiple comparisons, Generalized ANOVA, MANOVA, MANCOVA and their equivalency (and easiness of doing so) using a Generalized Linear Model
2-1) Simple linear regression
2-2) Fitting generalized linear models in real normally-distributed datasets
Wednesday 20th – Classes from 09:00 to 17:00
3-1) Generalizing the regression for many dependent variables
3-2) Model selection and multi-model inference
3-3) Plotting effects
3-4) Checking model assumptions and residuals
3-1) A full normally distributed data analysis
3-2) Model fitting
3-5) Plotting effects reporting results
Thursday 21st – Classes from 09:00 to 17:00
4-1) Time-to-event (survival analysis)
4-2) Logistic regression
4-3) Mixed effects models – fixed effects and random effects
4-1) Survival analysis and plotting results
4-2) Fitting mixed effects models, understanding the difference between random and fixed effects
4-3) Plotting all effects
Friday 22nd – Classes from 09:00 to 16:00
5-1) Dealing with non-normally distributed data
5-2) Identifying the distribution of the data
5-3) Generalizing the linear model for non-normally distributed data
5-4) Data visualisation – Plotting publication quality figures
5-1) Identifying the distribution of the data through AIC model selection
5-2) Fitting the best model residual error structure in a generalised linear model
5-3) Understanding, plotting, interpreting (reporting) and discussing results
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)