Unit MEDICAL STATISTICS, COMPUTER SCIENCE AND MATEMATICS
- Course
- Pharmacy
- Study-unit Code
- A002507
- Location
- PERUGIA
- Curriculum
- In all curricula
- Teacher
- Massimo Moretti
- CFU
- 9
- Course Regulation
- Coorte 2022
- Offered
- 2022/23
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa integrata
MATHEMATICS
| Code | 55106206 |
|---|---|
| Location | PERUGIA |
| CFU | 3 |
| Teacher | Antonio Boccuto |
| Teachers |
|
| Hours |
|
| Learning activities | Affine/integrativa |
| Area | Attività formative affini o integrative |
| Academic discipline | MAT/05 |
| Type of study-unit | Obbligatorio (Required) |
| Language of instruction | ITALIAN It is possible to do the examination also in English. |
| Contents | Elements of analytic geometry, trigonometry, inequalities, sets, real numbers, elementary functions, injective, surjective and bijective functions. Percentuals, equivalences. Limits, derivatives and study of functions. Integrals and applications to probability and statistics. Definite and indefinite integral. Fundamental theorems. Improper integrals. Gamma function. Gaussian distribution. Probability density. Combinatorics and probability. Conditioned probability. Elements of descriptive statistics: mean, median, mode, variance, mean square error, covariance, correlation coefficient, regression line. |
| Reference texts | Material given by the teacher. |
| Educational objectives | THE TRANSMISSION OF KNOWLEDGE!!! The aim of the course is to introduce, form and illustrate the basic mathematics to well understand natural phaenomena and several applications to various branches of sciences, taking into account the poetry and the art hidden in Mathematics, to arouse the curiosity of the students. It is required that the student handles fluently the fundamental tools and have the basic notions of Linear Algebra and Statistics, which are useful for successive studies in several sciences, in order to investigate some fundamental aspects of them. It is requested also that the student is able to work in team, but also in autonomy. |
| Prerequisites | To better understand the topics covered in the course the student should be familiar with notions like decomposition of simple algebraic expressions, set theory (union, intersection, complement, difference, Venn diagram, algebra of sets), resolution of linear and quadratic equations and inequalities, investigation on polynomials. |
| Teaching methods | The frequence is not officially obligatory, but warmly suggested, AS WELL AS A DAILY SERIOUS AND RESPONSIBLE STUDY. The course is split into theoretical lessons and practical lessons, which will be given by means of a projector and/or a computer, in these latter several exercises are carried out in class. The course is organized by means of these kinds of lectures and supplementary didactical activities, which include the tutorial service and in which the students are followed individually by the teacher. |
| Other information | The hours dedicated to exercises are FUNDAMETAL!!! |
| Learning verification modality | The exam consists in a series of tests, to establish together with the students. The test is oral, and sometimes by some written tests can be added (BUT NOT NECESSARILY). The teacher will ask ALL THE SUBJECTS IN DETAIL, INCLUDED SOME EXERCISES WHICH MUST BE DONE AND EXPLAINED IMMEDIATELY. |
| Extended program | Elements of analytic geometry, trigonometry, inequalities, sets, real numbers, elementary functions, injective, surjective and bijective functions. Percentuals, equivalences. Limits, derivatives and study of functions. Integrals and applications to probability and statistics. Definite and indefinite integral. Fundamental theorems. Improper integrals. Gamma function. Gaussian distribution. Probability density. Combinatorics and probability. Conditioned probability. Elements of descriptive statistics: mean, median, mode, variance, mean square error, covariance, correlation coefficient, regression line. |
MEDICAL STATISTICS, COMPUTER SCIENCE
| Code | A002508 |
|---|---|
| Location | PERUGIA |
| CFU | 6 |
| Teacher | Massimo Moretti |
| Teachers |
|
| Hours |
|
| Learning activities | Base |
| Area | Discipline matematiche, fisiche, informatiche e statistiche |
| Academic discipline | MED/01 |
| Type of study-unit | Obbligatorio (Required) |
| Language of instruction | Italian. |
| Contents | General principles of statistics and traditional epidemiology. Risk analysis. |
| Reference texts | (Pt.1) Statistics: Lecture notes are available on UniStudium. (Pt.2) Epidemiology: Pontello M. & Auxilia F. "Igiene, Medicina Preventiva e salute Globale" - Piccin, Padova. (Chapters: 3, 4, e 5; the book is also adopted for the course of Hygiene). Or: Lantieri P.B., Risso D., Ravera G. "Statistica Medica per le Professioni Sanitarie" - McGraw-Hill, 2004. |
| Educational objectives | The learning experiences should help students in achieving attitudes and practices on common techniques used in statistics and epidemiology. |
| Prerequisites | None. |
| Teaching methods | Face-to-face lessons. |
| Other information | |
| Learning verification modality | Written exam (closed-ended questions). |
| Extended program | Essentials of Medical Statistics: Numerical/categorical variables. Analysis of numerical outcomes: mean/median, standard deviation, standard error. The normal distribution. Inferential statistics. Comparison of two means: hypothesis tests and p-values. Student's t-test, ANOVA, Chi-square test. Epidemiologic Methods: Epidemiology: definition and scope. Rates of morbidity and mortality. Mortality tables. Population pyramids. Study design: Observational and experimental epidemiology. Ecological studies. Cross-sectional studies. Measures of disease frequency: prevalence and incidence. Analytical epidemiology: cohort studies and case-control studies. Causal and/or noncausal association. Evaluation of relative risk (RR) and odds-ratio (OR); attributable risk. Experimental epidemiology: preventive trials, randomized controlled trials. Evidence-based medicine: systematic reviews and meta-analysis. Risk Analysis: IARC classification for carcinogenicity. |