Università degli Studi di Perugia

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Unit BIOMETRICS APPLIED TO ANIMAL SCIENCES

Course
Animal science
Study-unit Code
GP000819
Curriculum
In all curricula
Teacher
Francesca Maria Sarti
Teachers
  • Francesca Maria Sarti - Didattica Ufficiale
Hours
  • 60 ore - Didattica Ufficiale - Francesca Maria Sarti
CFU
6
Course Regulation
Coorte 2018
Offered
2018/19
Learning activities
Caratterizzante
Area
Discipline zootecniche e delle produzioni animali
Sector
AGR/17
Type of study-unit
Obbligatorio (Required)
Type of learning activities
Attività formativa monodisciplinare
Language of instruction
ITALIAN
Contents
Study of the main statistical models applied to animal science and statistical software.
Reference texts
1. A. PETRIE, P. WATSON Statistics for Veterinary and Animal Science, Blackwell Science ed., 1999.2. E. BALLATORI Statistica e metodologia della ricerca, Galeno ed.3. A. CAMUSSI, F. MOLLER, E. OTTAVIANO, M. SARI GORLA Metodi statistici per la sperimentazione biologica, Zanichelli ed.4. G.W.SNEDECOR, W.G.COCHRAN Statistical methods, VI edition, Ames, Iowa, USA.5. R. COCCARDA Manuale di Statistica, Maggioli ed.6. G.A.MACCARARO, B.CHIANDOTTO, R.DE CRISTOFARO, G.CHISCI, F.SALVI, E.OTTAVIANO, E.MARUBINI, M.BABBINI, N.MONTANARO, F.NICOLIS, M.TURRI Biometria principi e metodi, Piccini ed. 7. Lessons slides.
Educational objectives
1. acquire knowledge of biometrics livestock 2. acquire knowledge of computer science 3.build an experimental work 4. know the main statistical models 5.know the main statistical software 6. know how to choose the most appropriate model for the type of experiment 7. able to process the results with dedicated software 8. able to interpret the results 9. know how to organize the exposition of the results 10. able to organize a scientific work
Prerequisites
To attend the exam in a profitable way it’s necessary to know the basics of statistics, also it requires some familiarity with the Excel software.
Teaching methods
The course is organized as follows:front -lessons classroom on the topics listed in the program;guided exercises with the computer aid on the topics expalined during the course;final exercise with the final written test simulation.
Learning verification modality
The examination consists in a final written exam lasting about three hours in which students must demonstrate that they can analyze the data using statistical tests explained during the course.The oral exam will test the communication skills of the student with properties of language and the ability to apply the skills acquired.
Extended program
THEORETICAL LESSONS Matrix algebra Definition of matrix and vector, special matrices, the main operations with matrices and vectors (addition, subtraction, multiplication); Kronecker product; power of a matrix, properties of matrices, inverse of a matrix, determinant calculation, properties of the determinant; generalized inverse, Moore-Penrose inverse; linearly dependent vectors, linearly dependent vectors (LIN); solving systems of equations with matrices.Parametric and Nonparametric test Parametric tests (ANOVA one way and two way, regression). AMOVA.Nonparametric statistical tests (Kolmogorov-Smirnov test, test, or Wilcoxon rank test for comparison of groups in paired samples, similar to the t test, rank sum test for comparing two groups in independent samples , analogous to the t test, rank correlation test of Spearman nonparametric analogue of the Pearson coefficient. Test del chi-square).Statistical models Solutions and estimable functions: estimated averages, errors, comparisons and significance. Fitting model. Type or multi-factor models monofactorial with possible interactions. Linear statistical models with fixed effects factorial classification (cross-classification), hierarchical (nested) or mixed.Regression models Linear regression and correlation, the method of least squares, the statistical model of linear regression, confidence intervals of the estimates of the coefficients of the regression, correlation, simple linear, quadratic and cubic regression, stepwise regression.Animal experimentation Planning animal experimentation; as conducting a scientific experiment, setting of trial and selection of animals, test performance and data collection, processing and statistical evaluation. The statistical software R and Excel applications.PRACTICAL LESSONS Resolution of the models studied on the basis of cases applied to livestock production. Solving models with dedicated software R and Excel.
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