09464 - Biometry and Statistics

Learning outcomes

At the end of the course, the student will possess knowledge of both univariate and multivariate statistical methods dedicated to naturalistic experimentation. In particular, the student will be able to:

• design biology experiments in the field and in the laboratory;
• plan naturalistic sampling;
• analyze experimental data from field and laboratory observations."

Course contents

Theory

Introduction to Statistical Theory - Data matrix and types of variables.

Univariate Statistics - Data visualization and description. Frequency tables. Graphs. Measures of central tendency: mean, median, mode. Measures of variability.

Bivariate Statistics - Two-way tables: joint, marginal, and conditional distributions. Association, covariance, correlation.

Linear Regression

Probability and Statistical Inference - Statistical models, population and sampling. Parametric inference: parameter estimation, confidence intervals, and hypothesis testing.

R Laboratory

Introduction to R - What is R? Basic commands and data structures. Data import and cleaning. Functions, conditionals, and iteration. Descriptive statistics and basic graphs.

Statistical Analysis with R - Application of the statistical methods explained in the first module through R (univariate statistics, bivariate statistics, linear regression, and statistical inference).

Readings/Bibliography

MODULO 1

M.C. Whitlock, D. Shculter (2022) "Analisi statistica dei dati biologici".

G. Cicchitelli, P. D'Urso, M. Minozzo (2017) "Statistica: principi e metodi", 3a edizione. Pearson Italia.

Slidesand any documents/materials shared by the teachers.

MODULO 2

Peter Dalgaard. Introductory Statistics with R. Springer, New York, 2002.

John Verzani. Using R for Intoductory Statistics. Chapman & Hall/CRC, Boca Raton, FL, 2005.

Stefano M. Iacus, Guido Masarotto . Laboratorio di statistica con R. McGraw-Hill, seconda edizione

Franco Crivellari. Analisi Statistica dei dati con R. Apogeo, 2006

Slides

Teaching methods

Lectures and laboratory with R.

Assessment methods

Final exam:

• Modulo 1: 1 hour written test with multiple choice questions aimed at verifying the general theory.

• Modulo 2: 1 hour written test with TRUE/FALSE closed-ended questions and practical exercises.
  
  
• Oral exam (optional): the oral exam can be carried out to obtain up to 3 bonus points.

The final grade will be composed as follows:

• 50% written test M1
• 50% written exam M2
• up to 3 bonus points which will be added to the average of the two written tests.

Teaching tools

Slides and material provided. Materials and online platforms useful for integrating and deepening the subject will also be indicated.

Office hours

See the website of Elisa Iori

See the website of Cinzia Franceschini