Introduction to statistics with R (part II): linear and logistic regression models

INVEST Transversal Courses

This course provides a comprehensive introduction to statistical modeling, focusing on linear and logistic regression techniques. Through lectures and lab sessions, participants will learn how to apply these models, verify assumptions, estimate parameters, interpret coefficients, and assess model fit. By the end of the course, students will be equipped to perform linear and logistic regression analyses, utilizing R software for practical applications. The course is structured across two days, with Day 1 covering correlation and regression models, and Day 2 diving into generalized linear models, including logistic regression.

Target audience: Doctoral students from any discipline interested in the practical application of basic statistical methods for data analysis in scientific research.

  • ECTS: 1
  • Total hours: 8
  • Language: English
  • Mode of participation: The course will be taught in presence with the possibility of remote participation through streaming lessons and recordings
  • Course code: INV.TRSVL.11
  • Category: BASIC TECHNOLOGY SKILLS
How To Apply

Contact for registration: dottorati@unimib.it

  • OPENING of registrations: 10 Jan. 2026
  • CLOSING of registrations: 7 Feb. 2026

Course Program
Day 1:

  • Correlation and simple linear regression

  • Multiple linear regression

  • Lab session with R

Day 2:

  • Introduction to generalized linear models

  • Logistic regression model

  • Lab session with R

Tentative dates: 

  • 10/02/26 9 am -1 pm CET
  • 12/02/26 9 am -1 pm CET

Multiple-choice question test

4 - Quality Education

Objectives
The course, through lectures and lab sessions, aims to explain the fundamentals of statistical modeling, with a particular focus on linear and logistic regression models.
By the end of the course, participants will be able to recognize when to perform a linear or logistic regression, verify the validity of the required assumptions, estimate model parameters, correctly interpret model coefficients, and assess the model’s goodness-of-fit to the data.

Name of the faculty: Prof. Davide Paolo Bernasconi 

Contact for registration: dottorati@unimib.it

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