Cultural skills (knowledge of): - Descriptive statistics and inferential statistics - Hypothesis formulation and verification - Statistical models in ecology - Outline of multivariate analysis.
Methodological skills (knowing how to perform): - Practical use of statistical software R - Knowing how to collect, organize and interpret ecological data - Knowing how to perform hypothesis tests and choose appropriate tests - Knowing how to correctly report statistical results - Knowing how to interpret and understand statistical analyzes in scientific articles.
Methodological skills (knowing how to perform): - Practical use of statistical software R - Knowing how to collect, organize and interpret ecological data - Knowing how to perform hypothesis tests and choose appropriate tests - Knowing how to correctly report statistical results - Knowing how to interpret and understand statistical analyzes in scientific articles.
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 (given sufficient time: ordination and classification)
PRACTICALS
Introduction to the statistical software R – Types of variables – Graphical functions – Performing analyses in R – 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) - Advanced methods in R (given sufficient time: for loops, writing functions, randomizations).
Suggested textbooks:
Crawley, M.J. (2007) The R Book. Wiley.
Gotelli & Allison. A Primer in Ecological Statistics, Sinauer Ass. Inc.
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 by appointment via email: marta.carboni@uniroma3.it
Programme
LECTURESStatistical 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 (given sufficient time: ordination and classification)
PRACTICALS
Introduction to the statistical software R – Types of variables – Graphical functions – Performing analyses in R – 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) - Advanced methods in R (given sufficient time: for loops, writing functions, randomizations).
Prerequisiti
No prerequisites. Good reading and understanding in English is helpfulCore Documentation
Materials, PDFs of lecture slides and scripts are made available during the courseSuggested textbooks:
Crawley, M.J. (2007) The R Book. Wiley.
Gotelli & Allison. A Primer in Ecological Statistics, Sinauer Ass. Inc.
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 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 RAttendance
Regular attendance is strongly recommendedType of evaluation
There will be two mid-term exams and a final exam. The midterms will be practical exams and will consist in the analysis of a dataset using R and in the preparation of a report to frame and interpret the results. Only the second midterm will be graded, while the first one is thought as a learning test and for self-evaluation. 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 in a scientific reèport. The final exam will also include an oral discussion starting from the prepared report and script.