Metodi quantitativi di analisi dei dati
A.Y. 2025/2026
Learning objectives
The course provides a solid introduction to statistical principles and methods, with an emphasis on conceptual understanding rather than the mechanical application of tests.
· Introduce the theoretical foundations of descriptive and inferential statistics, with particular attention to their application in the context of scientific research.
· Provide practical knowledge of probability distributions, confidence intervals, and hypothesis testing.
· Develop practical skills for data exploration and visualization using statistical software (Stata).
· Train students to critically evaluate data quality and to identify the most appropriate statistical techniques based on the experimental or observational design.
· Strengthen the ability to interpret statistical results, with particular attention to the scientific communication of data and conclusions.
· Introduce the theoretical foundations of descriptive and inferential statistics, with particular attention to their application in the context of scientific research.
· Provide practical knowledge of probability distributions, confidence intervals, and hypothesis testing.
· Develop practical skills for data exploration and visualization using statistical software (Stata).
· Train students to critically evaluate data quality and to identify the most appropriate statistical techniques based on the experimental or observational design.
· Strengthen the ability to interpret statistical results, with particular attention to the scientific communication of data and conclusions.
Expected learning outcomes
By the end of the course, the student will:
· Have acquired a thorough understanding of key statistical concepts and methods, including probability distributions, hypothesis testing, and regression analysis.
· Have developed the ability to explore, visualize, and descriptively analyse real-world datasets, ensuring data quality control.
· Be able to critically evaluate and independently apply appropriate statistical techniques based on the research design and type of data.
· Have gained the ability to rigorously interpret and communicate the results of statistical analyses.
· Have acquired a thorough understanding of key statistical concepts and methods, including probability distributions, hypothesis testing, and regression analysis.
· Have developed the ability to explore, visualize, and descriptively analyse real-world datasets, ensuring data quality control.
· Be able to critically evaluate and independently apply appropriate statistical techniques based on the research design and type of data.
· Have gained the ability to rigorously interpret and communicate the results of statistical analyses.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Course syllabus
The course covers the main concepts and methods of descriptive and inferential statistics, using examples drawn from scientific research. Topics include:
· Introduction to statistics: its role in research, types of variables, measurement scales, study designs
· Univariate and bivariate descriptive statistics: measures of central tendency and dispersion, graphical representations, correlations
· Fundamentals of probability: events, random variables, discrete and continuous distributions (binomial, Poisson, normal)
· Sampling and sampling distributions; central limit theorem
· Confidence intervals: construction, interpretation, applications
· Hypothesis testing: formulation, type I and type II errors, tests for means and proportions (z, t), chi-square test
· Group comparisons: t-tests for independent and paired data
· Introduction to simple and multiple linear regression
· Logistic regression: basic concepts, interpretation of coefficients, applications
The use of Stata software will accompany the theoretical presentation of the content, with the aim of promoting a practical understanding of the techniques covered.
· Introduction to statistics: its role in research, types of variables, measurement scales, study designs
· Univariate and bivariate descriptive statistics: measures of central tendency and dispersion, graphical representations, correlations
· Fundamentals of probability: events, random variables, discrete and continuous distributions (binomial, Poisson, normal)
· Sampling and sampling distributions; central limit theorem
· Confidence intervals: construction, interpretation, applications
· Hypothesis testing: formulation, type I and type II errors, tests for means and proportions (z, t), chi-square test
· Group comparisons: t-tests for independent and paired data
· Introduction to simple and multiple linear regression
· Logistic regression: basic concepts, interpretation of coefficients, applications
The use of Stata software will accompany the theoretical presentation of the content, with the aim of promoting a practical understanding of the techniques covered.
Prerequisites for admission
No specific prerequisites are required, but a basic knowledge of mathematics is helpful.
Teaching methods
· Front teaching
· Guided practical exercises using Stata
· Guided practical exercises using Stata
Teaching Resources
Biostatistica: Concetti di base per l'analisi statistica delle scienze dell'area medico-sanitaria
W. W. Daniel, C. L. Cross
W. W. Daniel, C. L. Cross
Assessment methods and Criteria
Assessment will be based on a final written exam consisting of both multiple-choice and open questions. The open questions will involve data analysis and interpretation, designed to evaluate students' theoretical understanding of statistical concepts and their ability to apply them in practical contexts.
Professor(s)