Insegnamento a.a. 2026-2027

The topics that will be discussed are:

• Review of frequentist Inference. Joint and conditional distributions, Independence, point estimation, Information, Confidence intervals; Hypothesis testing.

• Power functions and p-values.

• Large-sample approximation and convergences.

• Maximum likelihood estimators. Newton-Raphson algorithm.

• The multivariate normal distribution. The linear model. General linear hypotheses.

• Simultaneous confidence intervals. Bonferroni.

• ANOVA.

• Sample size determination: two-group testing.

• Binary data. Odds ratios. Stratification, homogenous association, conditional independence. Inference.

• Regression for binary outcomes: logistic regression.

• Classification. Screening tests. ROC and AUC. 

• Intro to Survival Analysis: Kaplan-Meier, Log-rank test, Modified Wilcoxon test.

Content of the labs:

1.    Introduction to R

2.    Statistical Inference

3.    Maximum Likelihood Estimation (MLE) and Introduction to the Linear Model

4.    The Linear Model and ANOVA

5.    Logistic Regression

6.    Classification and Survival Analysis

• Recognize the key guiding principles and concepts of statistical inference.
• Identify and describe a toolkit of methods and models.
• Summarize and defend concepts and knowledge useful in preparation of the courses on Machine Learning and Artificial Intelligence.

• Formulate suitable probabilistic models for statistical problems in the health sciences.
• Apply appropriate statistical methodology to estimation and testing problems.
• Interpret the outcome of the estimation procedures in view of the application at hand.

• Lectures
• Practical Exercises

• Face-to-face lectures. Theory, exercises, and some R will be blended during the lectures.
• PC-based labs ("Bring Your Own Laptop"). Labs will be devoted to exercises and hands-on sessions to appreciate the concepts and work on examples, also using the R programming language.

• A 2-hour in-class, closed book and closed notes exam will be held after the course, within each of the three exam sessions.
• The exam will be handwritten on paper.
• Some of the questions (for a total of 21 points out of 31) will cover the methods, models, and properties discussed in class, to assess the understanding of the theoretical aspects.
• Some of the questions (for a total of 10 points out of 31) will require the analysis of some data using R. Students will bring their own laptop to the exam and will not be required to submit any of the code or output: only the final answers of the analyses and the associated comments will need to be reported on the paper. No access to the internet will be allowed during the exam.
• There will be no difference in assessment method / exam program between attending and non attending students.

NOTE: While there will be no difference in assessment method / course content between attending and non attending students, attendance is strongly recommended.

Materials for the course will be distributed through the Blackboard platform. They will consist of class notes, R code, and additional handouts.

For more details on the topics we suggest the following volumes:

• Dalgaard P (2008). Introductory Statistics with R. Springer, New York, NY.
• Dobson AJ and Barnett AG (2018). An Introduction to Generalized Linear Models, Fourth Edition. Chapman & Hall / CRC, Boca Raton, FL.
• Lawless J (2003). Statistical Models and Methods for Lifetime Data. Wiley, New York, NY.