Teacher
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DELFINO Ines
(syllabus)
Scientific method and experiment design. Measurement operations. Resolution of an instrument. Experimental errors in direct and indirect measures. Result of a measure. Statistical tools for the analysis of experimental data. Basic concepts of statistics. Sampling. Histograms of experimental data set. Probability. Density and Cumulative Distribution. Probability distributions. Hypothesis testing. Analysis of variance. Hypothesis testing: hypothesis, interpretation of the p-value, types of errors, power. Multiple tests / comparisons. Confidence intervals. Linear regression and simple correlation. Covariance and correlation. Numerical methods for analyzing data from optical spectroscopies. Noise reduction algorithms. Multivariate analysis methods. Analysis in principal components (PCA, Principal Component Analysis): definitions, meaning of the main components and weight. Notes to the Partial Least Squares regression (PLS) and to the Cluster Analysis.
During the course practical exercises will be carried out during which the students will be able to apply what has been explained during the theoretical lessons and to analyze experimental data related to techniques and applications of biotechnological interest, using a special software.
(reference books)
H.W. Daniel, "Biostatistica" Edises Editore; A.Camussi, F.Moeller, E.Ottaviano, M.SariGorla, "Metodi statistici per la sperimentazione biologica", Zanichelli Editore
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