MEDICAL STATISTICS
cod. 1011311

Academic year 2023/24
1° year of course - First semester
Professor
Matteo Charles MALVEZZI
Academic discipline
Statistica medica (MED/01)
Field
Scienze propedeutiche
Type of training activity
Basic
16 hours
of face-to-face activities
2 credits
hub: -
course unit
in ITALIAN

Integrated course unit module: INFORMATION PROCESSING

Learning objectives

This course aims to provide a solid understanding of fundamental statistical concepts. Through the exploration of descriptive statistics, measurement theory, correlation, concordance, inference, and hypothesis testing, students will develop the skills needed for data analysis in the medical field. Practical implementation of statistical concepts will be emphasized using free/open-source analysis software. The course will also cover data collection techniques and introduce the basics of databases to ensure accurate and reliable data. Through critical thinking and problem-solving exercises, students will learn to apply statistical methods to real-world scenarios. Effective interpretation and communication of statistical findings will be emphasized, fostering skills essential for evidence-based decision making in medical research and practice. Collaborative group exercises and projects will enhance teamwork skills. This course provides a strong foundation in applied statistics.

Prerequisites

Knowledge of basic maths.

Course unit content

Introduction to statistics and illustration of the fundamentals of: Descriptive statistics, theory of measurement, correlation, concordance, introduction to inference, hypothesis testing.
Use of free/open-source analysis software for the implementation and practical illustration of theoretical concepts, along with data collection techniques and introduction to the basics of databases.

Full programme

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Bibliography

Course materials in the form of presentations and handouts will be provided during the course.

Teaching methods

Lectures and computer aided practicals.

Assessment methods and criteria

The course will be evaluated on a data analysis exercise to be presented both as a technical report and as an oral presentation.

Other information

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2030 agenda goals for sustainable development

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