Laboratorio: analisi quantitativa dei dati
A.Y. 2025/2026
Learning objectives
Provide theoretical and practical foundations for quantitative data analysis. Introduce fundamental concepts of probability and statistical distributions. Develop skills in using Excel for statistical calculations, linear models, and data analysis.
Correctly interpret the p-value and confidence intervals. Apply statistical concepts to a practical project based on real-world data. Foster the ability to make data-driven decisions.
Correctly interpret the p-value and confidence intervals. Apply statistical concepts to a practical project based on real-world data. Foster the ability to make data-driven decisions.
Expected learning outcomes
At the end of the course, students will be able to:
Understand the basic concepts of probability and statistical distributions.
Calculate and interpret mean, variance, and other measures of dispersion.
Use Excel to build and analyze linear models.
Interpret the p-value and confidence intervals to evaluate statistical significance.
Complete a data analysis project on a real dataset, with results presentation.
Communicate the results of the analysis in a clear and understandable manner.
Understand the basic concepts of probability and statistical distributions.
Calculate and interpret mean, variance, and other measures of dispersion.
Use Excel to build and analyze linear models.
Interpret the p-value and confidence intervals to evaluate statistical significance.
Complete a data analysis project on a real dataset, with results presentation.
Communicate the results of the analysis in a clear and understandable manner.
Lesson period: Second semester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Course syllabus
Introduction to statistics and probability. Basic concepts of descriptive statistics. Mean, variance, median, and other measures of position and dispersion. Events, conditional probability, the sum and product rules. Probability distributions (e.g., Normal, Binomial). Normal distribution, t-Student, and other useful distributions. Linear models and data analysis in Excel. Introduction to linear models. Simple and multiple linear regression. Interpretation of coefficients. p-value. Confidence interval. Using Excel for analysis.
Prerequisites for admission
None
Teaching methods
Lectures and tutorials.
Teaching Resources
References and readings will be provided at the beginning of the course.
Assessment methods and Criteria
Team project and presentation.
- University credits: 1
Laboratories: 18 hours