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| Semester | Herbstsemester 2024 |
| Angebotsmuster | Jedes Herbstsemester |
| Dozierende | Gonzalo Duran-Pacheco (gonzalo.duran-pacheco@unibas.ch, BeurteilerIn) |
| Inhalt | Basic concepts of experimental design, data types, descriptive methods, statistical measures, normal distribution, parameter estimation and confidence intervals, basic notions and concepts of statistical tests, knowledge of the basic statistical tests, correlation, regression, analysis of variance. Week 1: Descriptive statistics (A. Farnham) Week 2: Normal distribution/parameter estimations (A. Farnham) Week 3: Fundamentals of hypothesis testing (A. Farnham) Week 4: Comparison of means/medians and relative frequencies (A. Farnham) Week 5: Simple linear regression and correlation (A. Farnham) Week 6: Analysis of variance (A. Farnham) Week 7: Lecture Break Week 8: Linear regression models (A. Farnham) Week 9: Design of Experiments (G. Duran) Week 10: Design of Experiments (G. Duran) Week 11: Design of Experiments (G. Duran) Week 12: Design of Experiments (G. Duran) Week 13: Design of Experiments (G. Duran) Week 14: Lecture Break |
| Lernziele | Students know basic descriptive statistical methods. They understand fundamental concepts of inferential statistics and know basic analytic tools, including linear regression modelling and one-way ANOVA. They also know the principles of experimental design. |
| Literatur | Book: Kirkwood, B. R.; Sterne, J. A. C.: Essential Medical Statistics, 2nd edition. Blackwell Science (501Seiten) 2003. |
| Bemerkungen | The course will be based on lecture notes. The statistics program R will be used for the statistics lectures. Students can also use the web platform BOMS (Basics of Medical Statistics) to do some interactive exercises. In addition to the official lectures, there will be exercise sessions. Film and sound recordings during the course are strictly forbidden (recorders may be confiscated) |
| Weblink | www.boms.ch |
| Teilnahmebedingungen | Completed Bachelor degree |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | kein spezifischer Einsatz |
| Intervall | Wochentag | Zeit | Raum |
|---|---|---|---|
| wöchentlich | Montag | 15.15-17.00 | Pharmazentrum, Computerraum Cherry |
| Datum | Zeit | Raum |
|---|---|---|
| Montag 16.09.2024 | 15.15-17.00 Uhr | Pharmazentrum, Computerraum Cherry |
| Montag 23.09.2024 | 15.15-17.00 Uhr | Pharmazentrum, Computerraum Cherry |
| Montag 30.09.2024 | 15.15-17.00 Uhr | Pharmazentrum, Computerraum Cherry |
| Montag 07.10.2024 | 15.15-17.00 Uhr | Pharmazentrum, Computerraum Cherry |
| Montag 14.10.2024 | 15.15-17.00 Uhr | Pharmazentrum, Computerraum Cherry |
| Montag 21.10.2024 | 15.15-17.00 Uhr | Pharmazentrum, Computerraum Cherry |
| Montag 04.11.2024 | 15.15-17.00 Uhr | Pharmazentrum, Computerraum Cherry |
| Montag 11.11.2024 | 15.15-17.00 Uhr | Pharmazentrum, Computerraum Cherry |
| Montag 18.11.2024 | 15.15-17.00 Uhr | Pharmazentrum, Computerraum Cherry |
| Montag 25.11.2024 | 15.15-17.00 Uhr | Pharmazentrum, Computerraum Cherry |
| Montag 02.12.2024 | 15.15-17.00 Uhr | Pharmazentrum, Computerraum Cherry |
| Montag 09.12.2024 | 15.15-17.00 Uhr | Pharmazentrum, Computerraum Cherry |
| Dienstag 07.01.2025 | 09.00-10.00 Uhr | Pharmazentrum, Hörsaal 1 |
| Module |
Modul: General Skills and Experimental Tools (Masterstudium: Drug Sciences) Modul: Vertiefung Medizinische Nanowissenschaften (Masterstudium: Nanowissenschaften) Wahlbereich Master Pharmazie: Empfehlungen (Masterstudium: Pharmazie) |
| Leistungsüberprüfung | Lehrveranst.-begleitend |
| Hinweise zur Leistungsüberprüfung | https://pharma.unibas.ch/en/education/assessments-and-credit-points/continuous-assessments/ |
| An-/Abmeldung zur Leistungsüberprüfung | Anm.: Belegen Lehrveranstaltung; Abm.: stornieren |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | 1-6 0,5 |
| Wiederholtes Belegen | beliebig wiederholbar |
| Zuständige Fakultät | Philosophisch-Naturwissenschaftliche Fakultät, studiendekanat-philnat@unibas.ch |
| Anbietende Organisationseinheit | Departement Pharmazeutische Wissenschaften |