Cultural skills: knowledge of – descriptive and inferential statistics – formulating and testing hypotheses – statistical models in ecology
Methodological skills: practical use of the statistical software R – ability to collect, organize and interpret ecological data – ability to carry out hypothesis testing in R using the appropriate statistical test
Methodological skills: practical use of the statistical software R – ability to collect, organize and interpret ecological data – ability to carry out hypothesis testing in R using the appropriate statistical test
teacher profile teaching materials
Statistical Analysis in Biology and Ecology – Descriptive, inferential and predictive statistics – Measures of dispersion – Mean and median – Probability distributions – Formulating and testing hypotheses – Null hypothesis – One-tailed and two-tailed tests – Parametric and non-parametric tests – t test and non-parametric analogues – Analysis of frequency data- Analysis of variance (ANOVA) – Analysis of covariance – Statistical power and robustness – Correlations and regressions – Generalized linear models – Advanced statistical models in ecology (mixed effects models, model selection, testing model performance) – Multivariate analysis (PCA, discriminant analysis) – Matrix analysis and Mantel tests – Monte Carlo methods (simulations, algorithms)
Introduction to the statistical software R – Types of variables – Graphic functions – Performing analyses in R – Advanced methods in R (for loops, writing functions, randomizations).
Suggested textbooks:
Gotelli & Allison. A Primer in Ecological Statistics, Sinauer Ass. Inc.
Crawley, M.J. (2007) The R Book. Wiley.
Software:
R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
Office hours are by appointment via email: marta.carboni@uniroma3.it
Programme
Detailed program:Statistical Analysis in Biology and Ecology – Descriptive, inferential and predictive statistics – Measures of dispersion – Mean and median – Probability distributions – Formulating and testing hypotheses – Null hypothesis – One-tailed and two-tailed tests – Parametric and non-parametric tests – t test and non-parametric analogues – Analysis of frequency data- Analysis of variance (ANOVA) – Analysis of covariance – Statistical power and robustness – Correlations and regressions – Generalized linear models – Advanced statistical models in ecology (mixed effects models, model selection, testing model performance) – Multivariate analysis (PCA, discriminant analysis) – Matrix analysis and Mantel tests – Monte Carlo methods (simulations, algorithms)
Introduction to the statistical software R – Types of variables – Graphic functions – Performing analyses in R – Advanced methods in R (for loops, writing functions, randomizations).
Core Documentation
Materials, PDFs of lecture slides and scripts are made available during the courseSuggested textbooks:
Gotelli & Allison. A Primer in Ecological Statistics, Sinauer Ass. Inc.
Crawley, M.J. (2007) The R Book. Wiley.
Software:
R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
Office hours are by appointment via email: marta.carboni@uniroma3.it
Type of delivery of the course
The course will include theoretical lectures and in-class practical sessions with the statistical software RType of evaluation
The practical exam will consist in the analysis of a dataset using R and in the preparation of a report to frame and interpret the results. Both achieving the correct results and the script preparation will be evaluated, as well as the ability to correctly interpret results and formulate appropriate hypotheses and conclusions.