The main objective of the module is to provide students with the key inferential tools used in experimental contexts.
By the end of the course, students will be able to:
Understand the basic principles of sampling and experimental error. Apply basic statistical tests (e.g., t-test, chi-square) to compare groups and test hypotheses. Evaluate statistical results in relation to the experimental context, with a critical and informed approach.
The learning objectives that will be evaluated for the purpose of passing are summarized in the following scheme:
Knowledge and understanding: Knowledge of probabilistic techniques for the analysis of simple random phenomena; acquisition of basic statistical inference tools for estimation and hypothesis testing.
Ability to apply knowledge and understanding: Ability to carry out simple computations in situations of uncertainty; know how to apply the main statistical inference techniques; know how to read statistical analyses carried out with the methodologies presented in the course.
Making judgements: Be able to understand and comment on the results obtained from statistical analyses in practical examples based on the context of the application, thus being able to use the results in decision-making processes.
Communication skills: Acquire the basics of technical statistical language to communicate clearly and without ambiguity with both statisticians and non-statisticians.
Learning skills: Be able to correctly read the results of statistical analyses, also in contexts of greater complexity than those presented in the course.
None.
Blended teaching, with on-site and online classes.
Part I: Probability
Random experiments, outcomes, events.
The probability function and its axioms. Rules of probability.
Conditional probability and independence. Bivariate probabilities.
Bayes’ theorem.
Discrete random variables and their properties.
Bernoulli and Binomial distributions.
Continuous random variables and their properties.
Normal distribution.
Part II: Inference
Sampling and sampling distributions.
Distribution of the sample mean. The central limit theorem. Distribution of the sample proportion.
Point estimation. Estimators and their properties.
Confidence intervals. Confidence intervals for the mean and for the proportions.
Confidence interval for a proportion
Theory of statistical hypothesis testing. Test for a mean, test for a proportion.
Comparison of means.
Newbold, Carlson, Thorne, Statistica. Nona edizione. Pearson (2021). Foreign students can refer to the original version of this book. For a topic not covered by the textbook, documentation will be provided in Aulaweb by the teacher.
Ricevimento: Tuesday 16.30-18.00 Teacher's office For Imperia: I am available after lessons during the first semester, and throughout the year on Teams by appointment via email
https://corsi.unige.it/en/corsi/11758/studenti-orario
The timetable for this course is available here: EasyAcademy
The partial exam for the open badge consists of a written/practical test with multiple-choice questions and some exercises to be solved using Excel.
The multiple-choice questions are designed to assess the knowledge and understanding of the topics covered in class, while the exercises are intended to evaluate the student's ability to apply the methods learned.
Working students and students with DSA certification, disability or other special educational needs are advised to contact the teacher at the beginning of the course to agree on teaching and exam methods that, in compliance with the teaching objectives, take into account individual learning methods.
