Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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Mathematical analysis
2° Year It will be activated in the A.Y. 2026/2027
| Modules | Credits | TAF | SSD |
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3° Year It will be activated in the A.Y. 2027/2028
| Modules | Credits | TAF | SSD |
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One module to be chosen among the following| Modules | Credits | TAF | SSD |
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Mathematical analysis
| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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One module to be chosen among the following| Modules | Credits | TAF | SSD |
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Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Statistical Methods for Engineering (2026/2027)
Teaching code
4S012386
Credits
6
Language
Italian
Also offered in courses:
- Statistica e probabilita' of the course Bachelor's degree in Human Centered Medical System Engineering
- Statistica e probabilita' - Laboratorio of the course Bachelor's degree in Human Centered Medical System Engineering
- Statistica e probabilita' - Teoria of the course Bachelor's degree in Human Centered Medical System Engineering
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Courses Single
Authorized
The teaching is organized as follows:
Teoria
Laboratorio
Learning objectives
The course aims to provide the basic concepts of descriptive statistics and probability calculus, focusing on the application of these concepts to the engineering context. The goal is to provide the student with the necessary tools to model and solve concrete problems through the use of probabilistic methods and to apply the main statistical techniques to engineering for the analysis of real problems. At the end of the course the student will have to demonstrate knowledge and understanding of the main statistical techniques for the description and analysis of the phenomena under study; have the ability to apply the acquired knowledge and understanding to interpret the results of the statistical analyzes applied in a critical and proactive way, also through the tools shown; knowing how to develop the skills necessary to continue their studies independently in the field of statistical analysis.
Prerequisites and basic notions
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Program
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MM: Teoria
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(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.
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MM: Laboratorio
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The course includes a series of laboratories in the computer lab with exercises in MATLAB environment. After an introduction to MATLAB and to the main functions and tools useful for statistics, some exercises will be proposed on descriptive statistics and probability; for computing the probability density function (pdf) and cumulative distribution function (cdf) of special random variables, for generating random data and estimating parameters; on hypothesis testing for distributions and linear regression. The laboratories complement lectures by consolidating learning and developing problem-solving and hands-on practical skills.
Bibliography
Didactic methods
Regular lectures with power point presentation and blackboard and laboratory exercises. Educational material will be available to students enrolled in the course on the Moodle platform. This material includes lecture presentations in PDF format and material related to laboratory activities. For further details and supplementary materials, please refer to the reference books.
Learning assessment procedures
The exam consists of a computer test via Moodle. The exam consists of theoretical questions (test with multiple choice), problems, and laboratory questions (open questions).
Evaluation criteria
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.
Criteria for the composition of the final grade
The final grade will be the average of the three grades (theory, exercises, laboratory). To pass the exam, a minimum score of 18 out of 33 is required. Distinction is awarded for scores above 30.
Exam language
Italiano
