Probability and Statistics (2018/2019)

Course code
Silvia Francesca Storti
Other available courses
Other available courses
    Academic sector
    Language of instruction
    Teaching is organised as follows:
    Activity Credits Period Academic staff Timetable
    Teoria 4 II semestre Silvia Francesca Storti

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    Laboratorio [Bioinformatica] 2 II semestre Silvia Francesca Storti

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    Laboratorio [Informatica] 2 II semestre Silvia Francesca Storti

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    Learning outcomes

    The course aims at providing the fundamental concepts of descriptive statistics and probability, with the task of modeling real problems by means of probability methods and applying to real problems statistic techniques.

    At the end of the course the student will have to demonstrate to understand the main statistical techniques for describing problems;
    to be able to interpret results of statistical analyses; to be able to develop know-how necessary to continue the study autonomously in the context of statistical analysis.


    MM: Theory
    (1) Descriptive Statistics. Describing data sets (frequency tables and graphs). Summarizing data sets (sample mean, median, and mode, sample variance and standard deviation, percentiles and box plots). Normal data sets. Sample correlation coefficient.

    (2) Probability Theory. Elements of probability: sample space and events, Venn diagrams and the algebra of events, axioms of probability, sample spaces having equally likely outcomes, conditional probability, Bayes’ formula, independent events. Random variables and expectation: types of random variables, expected value and properties, variance, covariance and variance of sums of random variables. Moment generating functions. Weak law of large numbers. Special random variables: special random variables and distributions arising from the normal (chi-square, t, F).

    (3) Statistical Inference. Distributions of sampling statistics. Parameter estimation (maximum likelihood estimators, interval estimates). Hypothesis testing and significance levels.

    (4) Regression. Least squares estimators of the regression parameters. Distribution of the estimators. Statistical inferences about the regression parameters. The coefficient of determination and the sample correlation coefficient. Analysis of residuals: assessing the model. Transforming to linearity. Weighted least squares. Polynomial regression and multiple linear regression.

    MM: Laboratory
    The course includes a series of laboratories in the computer lab with exercises in MATLAB environment. The exercises will cover an introduction to MATLAB and the main functions and tools useful for statistics, for the generation of random variables and the analysis of random data samples. The laboratories complement lectures by consolidating learning and developing problem-solving and hands-on practical skills.

    Teaching methods: lectures, class exercises and laboratory exercises. Educational material (powerpoint file) will be available on the eLearning platform.

    Assessment methods and criteria

    Written exam consisting of theoretical questions, problems, and laboratory questions.
    To pass the exam, the students must show that:
    - they have understood the basic concepts of probability theory and statistics;
    - they are able to use the knowledge acquired during the course to solve the assigned problem;
    - they are able to program in MATLAB environment in the statistical and probabilistic context.

    Reference books
    Activity Author Title Publisher Year ISBN Note
    Teoria Sheldon M. Ross Probabilità e Statistica per l'ingegneria e le scienze, Apogeo Education, terza edizione, 2015, ISBN: 978-88-916-0994-6 (Edizione 3) Apogeo Education 2015 978-88-916-0994-6