## Learning objectives

Knowledge and understanding of basic Mathematics, principal statistical indices and some simple discrete probability notions.

Applying knowledge and understanding to basic mathematical models.

Making judgements w.r.t. the output information coming from a table of data and w.r.t. the identification and the study of Bernoulli and Gauss distributions.

Communication skills related to the ability of explaining simple mathematical issues and related obtained results.

Learning skills developed in order to allow the student to deal in autonomy with the next stages of the university course requiring simple mathematical notions.

## Prerequisites

Operations in the set of real numbers. Logarithmic and exponential calculus.

## Course unit content

Lectures on Statistics and Probability applied to the biomedical sciences.

First lessons cover topics of general interest related to the foundations of Mathematics and Logic such as operations in numerical sets and predicate calculus.

The second part regards the discussion of the fundamental contents of Statistics: data collection, averages and dispersion indicators. The third part is devoted to combinatorics, probability calculus, Gauss and Bernoulli distributions.

## Full programme

Elementary theory of sets - Elements of Logic - Numerical sets, operations and properties - Applications: proportions and percentages - Applications: equivalences - Floating point representation of real numbers - Operations in scientific notation - Truncation and rounding approximations - Significant digits - Outline of functions - Real functions of real variable - Qualitative properties - Polynomial, exponential and logarithmic functions -

Elements of descriptive statistics: data collection, classification and graphical representation - Frequency distributions - Avarages, mode, median, squared error,

variance, standard deviation, index of variation - Gauss distribution - Two characters distribution - Combinatorics - Events and discrete probability calculus - Elements of probability theory - Bernoulli distribution.

## Bibliography

C. Sbordone, F. Sbordone: “Matematica per le Scienze della Vita”, EdiSES, (2014)

M. Abate: “Matematica e Statistica, Le basi per le scienze della vita”, McGraw-Hill, (2009)

V. Villani: “Matematica per le discipline bio-mediche”, Quarta edizione, McGraw-Hill, (2007)

A.Aimi: "Appunti di Matematica di base applicata alle scienze biomediche", (obtained from the slides used during lectures, a.y. 2019/20)

## Teaching methods

During lectures, pdf slides will be screened on the topics of the course, which will be always supported by examples and classroom exercises performed by the teacher or by the students under the supervision of the teacher. Their purpose is to provide the opportunity for each student to be able to measure himself in independently obtaining the solutions to problems presented in theoretical form during lectures.

Slides will be loaded at the end of each lecture on Elly E-Learning Platform.

## Assessment methods and criteria

• Written final examination on problems to be solved in 2 hours

• Pass mark: 18/30; Maximum score: 33/30 ("cum laude")

• Since it is an integrated teaching together with Physics and Computer Science, the final score will be the arithmetic average of the three marks, which have to be anyway all equal or greater than 18/30.

• Cetified DSA students will be allowed to use supplementary time during the examination and/or formulas to be chosen in accordance with the teacher

## Other information

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## 2030 agenda goals for sustainable development

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