Expected learning outcomes
KNOWLEDGE AND UNDERSTANDING
At the end of this educational activity, in a context of exercise or examination, the student must demonstrate that he has acquired the knowledge of the basic elements of the statistics and development of the ability to analyze the data related to experimental studies in the field of biotechnology, in agreement with as foreseen by the program.

ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING
At the end of this educational activity, the student must demonstrate that he / she understands the statistical and data analysis approaches and that he / she can choose the most suitable ones to solve problems of interest, analyzing the results in a critical way.

JUDGMENT AUTONOMY
At the end of the training activity the person must be able to analyze and interpret the experimental results obtained and discuss them logically.


COMMUNICATION SKILLS
The student must demonstrate that he / she is able to have acquired the necessary communication skills to disseminate the results of the experiments and analyzes conducted using appropriate forms of communication based also on the use of IT tools according to the type of interlocutors.

LEARNING SKILLS
At the end of this training activity, the student must demonstrate to be able to use the knowledge learned to investigate systems and phenomena of interest, different from those taken into consideration during the course.

CONTENTS
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.

H.W. Daniel, "Biostatistica" Edises Editore;
M.C. Whitlock, D. Schluter, "Analisi statistica di dati biologici", Zanichelli Editore