# APPLIED PHYSICS AND ELEMENTS OF MEDICAL STATISTICS I cod. 1006168

1° year of course - First semester
Professor
- Ludovica LEO - Giuseppe PEDRAZZI
Fisica applicata (a beni culturali, ambientali, biologia e medicina) (FIS/07)
Field
Scienze propedeutiche
Type of training activity
Basic
14 hours
of face-to-face activities
2 credits
hub:
course unit
in

Integrated course unit module: PROPAEDEUTIC SCIENCES

## Learning objectives

The module of Medical Statistics is designed to introduce the student to
the basics of statistical thinking and its application in practice. The topics
are geared to concrete problems of analysis and research and deal in
particular with situations and cases drawn from the medical literature.
Starting from the multitude of information from which we are faced daily,
the course aims to give students the statistical tools needed to describe
and analyze the data, extract useful information and make informed
decisions. Special emphasis will be put on statistical reasoning,
interpretation and decision-making process. We will insist more on the
conceptual understanding that the mechanical calculation, especially in
light of the wide range of software available for analysis. The theory will
be made explicit by means of practical exercises and teaching cases,
therefore, the ultimate goal of the course is that the student learn "how
to do" as well as knowing.

None

## Course unit content

The first part of the course will introduce the basics of statistical planning
and experimental design.
Principles of probability and combinatorial analysis needed later in the
course will be introduced, as well as the major probability distributions.
This includes the binomial distribution, the Poisson distribution, the
Normal and standard Normal distribution.

The second part of the course will address the methods of descriptive
statistics. It will be shown how to recognize the type of data and how to
summarize them in appropriate indicators.
The student will learn how to calculate measures of location (mean,
median, mode), variability (variance, standard deviation), the coefficient
of variation (CV), quantiles and their use.
Overview of special charts (mosaic plot, box-percentile plot, parallelviolin
plot, etc).

In the final part of the course the general principles of statistical
inference will be introduced.
The student will face the concepts of sampling distribution, type I and II error, power of a statistical test and operating curve.
The following methods will then be explained:
parametric tests - Student's t test, ANOVA 1 and 2 classification criteria.
non-parametric tests: - Wilcoxon test, Mann-Whitney, Kruskal-Wallis,
Friedman test, median test, chi-square test, Fisher's exact test.
Elements of correlation and linear regression.
The course will conclude with an introduction to Machine Learning and related examples of application in the scientific field.

## Full programme

Introduction: medical statistics and related disciplines. Logic and
statistical planning. Overview of combinatorial analysis: permutations,
arrangements, combinations. Applications. Overview of probability
calculations: simple and compound probability, Bayes theorem.
Odds. Odds ratios. Likelihood ratios. applications.
Probability distributions : binomial distribution, Poisson distribution,
normal and standard normal distribution. Tables and their use.
Summarising data. Units of measure. Measurements of position, order
and variation. Indices of central tendency, mean median, mode.
Indices of variability, variance, standard deviation, CV. Percentiles and
their use.
General principles of statistical inference. Sampling distribution.
Hypothesis and hypothesis testing. Type 1 and type 2 error. Power of a
test and operating curve.
Power analysis and sample size determination.
Parametric test : Student t-test, ANOVA with 1 and 2 classification
criteria.
Non-parametric test: Wilcoxon test, Mann-Whitney test, Kruskal-Wallis
test, Friedman test, median test, Chi-square test, Fisher exact test.
Linear regression and correlation. Multiple regression. Logistic regression.

Computer exercises with the software R, Jasp, Jamovi, and SPSS.
Introduction to Machine Learning: Classification and Regression; theoretical overview of the main supervised learning algorithms (logistic regression, linear regression, decision trees, and neural networks); examples of application in the scientific field.

## Bibliography

M.M Triola, M.F. Triola : Fondamenti di Statistica, Ed. Pearson
W.W. Daniel : Biostatistica – Ed. Edises
A. Field. J. Miles, Z. Field : Discovering Statistics Using R, Ed. SAGE

## Teaching methods

Lectures will be held on-site in compliance with safety standards, provided that further instructions on the ongoing health emergency are not implemented. Supporting material will be available on the specific, student-reserved platform (Elly) and will include slide presentations, audio-video aids or video-recording of the lectures.

During classroom lectures, the topics contained in the program of the module will be illustrated and commented.
At the end of each topic classroom exercises explaining the application of the theory in practice will follow. The formal procedure and the step by
step execution of the necessary calculations will be described. Both manual solution and computer calculation will be shown.

The students will be particularly encouraged to use the open source statistical systems R, Jasp and Jamovi and the package IBM-SPSS.

## Assessment methods and criteria

The achievement of the objectives of the module will be assessed through a written examination, mainly consisting in open questions on
the topics of the course. This will allow to ascertain the knowledge and the understanding of both the theoretical bases and their consequences.
The written examination will include the resolution of problems, to assess the achievement of the ability to apply the acquired knowledge to a
simulated biological or medical situation.

Consultation of the didactic material will be allowed.

Students with disabilities, SLD, BSE must first contact Centro Accoglienza ed Inclusione (CAI) (https://cai.unipr.it/) for support.

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