Introduction to statistics and R for everyone (IRFE01) DEADLINE 11th March
https://www.prstatistics.com/course/introduction-to-statistics-and-r-for-anyone-irfe01/
This course will be delivered by Dr. Aris Moustakas from the 18th – 22nd of March 2019 at the Pasteur Hellenic Institute, Athens, Greece.
Please email [log in to unmask] with any questions.
Course Overview:
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.
Course Programme
Monday 18th – Classes from 09:00 to 17:00
Lecture
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
Practical
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
Lecture
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
Practical
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
Lecture
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
Practical
3-1) A full normally distributed data analysis
3-2) Model fitting
3-3) ANOVA
3-4) Residuals
3-5) Plotting effects reporting results
Thursday 21st – Classes from 09:00 to 17:00
Lecture
4-1) Time-to-event (survival analysis)
4-2) Logistic regression
4-3) Mixed effects models – fixed effects and random effects
Practical
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
Lecture
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
Practical
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
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