Statistical Course Lorenzo Marini teaching

Introduction to Biostatistical Analysis Using R

This short course will try to cover the fundamentals in the design and analysis of observational and experimental studies. The course is directed to PhD students with basic knowledge in applied statistics. The participants will be instructed on how to decide what kind of analysis is most appropriate given the hypothesis of interest and how to interpret their results. Each lecture (3 hours) will consist of a theoretical part and practical PC exercises using the open-source software R, for a total of 5 lectures.

NOTE: As the aim of the course is to give fundamentals of statistics, the programme will not cover the R programming language. In the practicals, the students will use only very simple R scripts, which will be provided during the lectures.

Location
Lessons will be held at TESAF, Level 2, Room 2043 at 9.30-12.30.

Address:
Viale dell'Università 16 - 35020, Legnaro (PD)- Italy

Materials of the course (Fall 2011)
Below you can find the materials for the course (PowerPoint presentations and R scripts)

PowerPoints and practicals

Assessment guidelines

therbook

Useful links:

R Manuals:

An Introduction to R (Venables, W.N. & Smith, D.M.)
An Introduction to R: Software for Statistical Modelling & Computing (Kunhert, P., Venables, W.N.)
Angewandte Statistik fuer die biologischen Wissenschaften (Dormann, C. & Kuehn I.)
Multivariate analysis of Ecological Data
Practical Regression and Anova using R (Faraway, J.J.)
Statistics Using R with Biological Examples (Seefeld, K. & Linder, E.)
Using R for Data Analysis and Graphics (Maindonald, J.H.)
R Data Import/Export
Writing R Extensions
R Language Definition
gstat's user manual (Pebesma, E.J.)
Rappresentazione Analitica Delle Distribuzioni Statistiche Con R (Ricci, V.)
Principali tecniche di regressione con R (Ricci, V.)
Analisi delle Serie Storiche con R (Ricci, V.)

Suggested books:

Crawley, M.J. (2007) The R book. Wiley. [Comprehensive reference manual for the R language, including practical guidance and full coverage of the graphics facilities]

Legendre, P. & Legendre, L. (1998) Numerical Ecology. Elsevier. [best reference manual for multivariate analysis]

Quinn, G.P. & Keough, M.J. (2002) Experimental Design and Data Analysis for Biologists. Cambridge Press. [very good manual for classical statistics with some hints at Generalized Linear Models and multivariate analyses]

Sokal, R.R. & Rohlf, F.J. (1994) Biometry. 3rd Ed. Freeman Publishers. [great reference book for classical univariate statistics applied to biology]

Soliani, L. (2007) Manuale di statistica per la ricerca e la professione. http://www.dsa.unipr.it/soliani/soliani.html [goog manual with many examples, in Italian]

Zar, J.H. (2009) Biostatistical Analysis. 5th Ed. Prentice Hall. [great reference book for univariate statistics applied to biological sciences]

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