Knowledge and understanding: elements of descriptive statistics, applications of probability; inferential statistics, tools necessary for a fysiotherapist. Ability to apply knowledge and understanding: be able to read and apply notions of Statistics to Physiotherapy trials, with the help of MS EXCEL and/or Jamovi. Making judgements: being able to critically evaluate the results of experiments and first scientific articles with the presence of data collection and analysis. Communication skills: being able to express oneself appropriately on the basic topics of Statistics and on the correct use of software in biomedical-health applications. Learning skills: being able to grasp the salient elements of new topics, in particular on research methodology in the biomedical field. Aim of the course is in particular to give basic notions in order to organizate the degree thesis

basic mathematics and statistics (see the first year Statistics course)

Statistics, a necessary and useful tool for the health professions. Examples and applications. The MS EXCEL spreadsheet (pros and cons); introduction to Jamovi software. Review of Descriptive Statistics: examples and data processing using the MS EXCEL spreadsheet and Jamovi software. Review of using probability calculation with MS EXCEL spreadsheet and with Jamovi software. Review of elements of statistical inference: examples and data processing using the MS EXCEL spreadsheet and Jamovi software. Using Jamovi software for survival analysis: examples and exercises. Critical reading of scientific articles.

G. Norman, D. Streiner (2015), Biostatistica – quello che avreste voluto sapere, Casa Editrice Ambrosiana; D.F. Polit, C.T. Beck (2014), Fondamenti di ricerca infermieristica, Mc Graw Hill H. Motulski (2021), Biostatistica essenziale, Piccin; Crawley M.J. (2005), Statistics: An introduction using R. Wiley;

Review of basic elements of spreadsheets; introduction to Jamovi statistical software. Each topic will see the application, where necessary, of the two softwares. Recalls of DESCRIPTIVE STATISTICS and of Probability: Measurements of numerical synthesis. Measures of central tendency: average, median, mode, and their comparison. Measures of dispersion: range of values, variance, standard deviation, coefficient of variation, quartiles, percentiles. Examples of rehabilitation problems. Recalls on diagnostic tests. Examples. Recalls on relative risk (RR) and on odds ratio (OR). Examples. Basic Elements of Statistical Inference: introduction to the study designs and statistical inference (with examples from scientific articles): object of a study; materials and methods; criteria for inclusion / exclusion and choice of the population; choice of the sample; collection and analysis of sample data; statistical inference; results and discussion. Estimates: confidence intervals (c.i.). Examples Example: c.i. for estimating the mean of a Gaussian population, and a priori estimate of numerosity. Examples. Test Hypothesis - form examples: t Student test for 'paired' and 'independent samples'. The chi-square test, Fisher's exact test. Examples. By rehabilitation examples. first presentation of ANOVA, Kruskal-Wallis, Friedmann and normality tests, ... The logistic regression. Introduction to the survival analysis. Reading of scientific articles.

lectures and exercises on data sets with statistical softwares

if necessary, please contact the teacher: torelli@units.it

written test with 10 open-ended answers (questions on basic elements of the theory) and critical reading of scientific articles.

This course explores topics closely related to one or more goals of the United Nations 2030 Agenda for Sustainable Development (SDGs)