Achtung! Das Lehrangebot ist noch nicht vollständig und wird bis Semesterbeginn laufend ergänzt.
301350 VO Quantitative Methoden in der Molekularbiologie (2025W)
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An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
Details
Sprache: Englisch
Prüfungstermine
- Montag 09.02.2026 14:00 - 16:00 BZB/Seminarraum 1/2, 6.Ebene 6.501/6.504, Dr.-Bohr-Gasse 9, 1030 Wien
- Montag 02.03.2026 14:00 - 16:00 BZB/Seminarraum 1/2, 6.Ebene 6.501/6.504, Dr.-Bohr-Gasse 9, 1030 Wien
- Montag 16.03.2026 14:00 - 16:00 BZB/Seminarraum 1/2, 6.Ebene 6.501/6.504, Dr.-Bohr-Gasse 9, 1030 Wien
- Montag 20.04.2026 14:00 - 16:00 BZB/Seminarraum 1/2, 6.Ebene 6.501/6.504, Dr.-Bohr-Gasse 9, 1030 Wien
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
jeweils Mo. 14.00 - 16.00 Uhr
HS A/B, VBC 5, 1030 Wien
Achtung! Am 24.11. findet die VO ausnahmsweise im Hörsaal IMBA, Dr-Bohr-Gasse 3 (1030 Wien) statt!
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Montag
06.10.
14:00 - 16:00
STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
STB/Hörsaal B Campus Vienna Biocenter 5, 1030 Wien -
Montag
13.10.
14:00 - 16:00
STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
STB/Hörsaal B Campus Vienna Biocenter 5, 1030 Wien -
Montag
20.10.
14:00 - 16:00
STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
STB/Hörsaal B Campus Vienna Biocenter 5, 1030 Wien -
Montag
27.10.
14:00 - 16:00
STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
STB/Hörsaal B Campus Vienna Biocenter 5, 1030 Wien -
Montag
03.11.
14:00 - 16:00
STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
STB/Hörsaal B Campus Vienna Biocenter 5, 1030 Wien -
Montag
10.11.
14:00 - 16:00
STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
STB/Hörsaal B Campus Vienna Biocenter 5, 1030 Wien -
Montag
17.11.
14:00 - 16:00
STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
STB/Hörsaal B Campus Vienna Biocenter 5, 1030 Wien - Montag 24.11. 14:00 - 16:00 Hörsaal IMBA 1030 Wien, Dr.-Bohr-Gasse 3
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Montag
01.12.
14:00 - 16:00
STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
STB/Hörsaal B Campus Vienna Biocenter 5, 1030 Wien -
Montag
15.12.
14:00 - 16:00
STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
STB/Hörsaal B Campus Vienna Biocenter 5, 1030 Wien -
Montag
12.01.
14:00 - 16:00
STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
STB/Hörsaal B Campus Vienna Biocenter 5, 1030 Wien -
Montag
19.01.
14:00 - 16:00
STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
STB/Hörsaal B Campus Vienna Biocenter 5, 1030 Wien -
Montag
26.01.
14:00 - 16:00
STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
STB/Hörsaal B Campus Vienna Biocenter 5, 1030 Wien
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Das Hauptziel dieser Lehrveranstaltung ist, die Angst vor der Mathematik wegzunehmen. Wir werden lernen, wie man biologische Fragestellungen mit mathematischen und statistischen Methoden effizient beantworten kann. Das Hauptaugenmerk dabei liegt auf "Warum?", und weniger auf "Wie?".Wir werden zuerst die Grundlagen der Wahrscheinlichkeitsrechnung diskutieren, und danach uns in die angewandte Biostatistik vertiefen, fokussierend auf Tests von statistischen Hypothesen und lineare Methoden der statistischen Modellierung. Wir werden Beispiele aus der klinischen Forschung benutzen.Erfolgreiche Absolventen dieses Kurses werden in der Lage sein, statistische Methoden in der Datenanalyse richtig anzuwenden.Es ist empfohlen, auch die Übung 301351-1 UE zu belegen. Erfahrungsgemäss erreichen Studenten, die auch die Übungen mitgemacht haben, bessere Prüfungsergebnisse.
Art der Leistungskontrolle und erlaubte Hilfsmittel
Exam type: single-correct-answer (SCA) type multiple choice test. 4 possible answers per question.
Scoring: 1 point for a correct answer, 0 points for incorrect answers or for no answers at all. Final score is the sum of the question scores.
Format: physical, on paper.
Language: English.
Resources: "closed-book", no external information resources allowed. [If you disagree with this policy, you can complain to Sam Altman and his cronies.]
Tools: hand-held calculator allowed. No laptop or smartphone.Example test question:A professor prepares a SCA multiple-choice test consisting of 16 questions. For each question there are 4 possible answers of which one is correct. Correct answers are worth 1 point, incorrect ones are worth 0. Unfortunately the professor is totally incompetent and he hasn't taught anything so his 30 students just pick the answers "randomly". Which probability distribution describes the total scores of these poor students?
a. Poisson with mean parameter lambda = 7.5
b. Normal with mean parameter = 7.5 and standard deviation parameter 0.25
c. Binomial with size parameter n = 16 and success probability parameter p=0.25
d. Binomial with size parameter n = 30 and success probability parameter p=0.25
Scoring: 1 point for a correct answer, 0 points for incorrect answers or for no answers at all. Final score is the sum of the question scores.
Format: physical, on paper.
Language: English.
Resources: "closed-book", no external information resources allowed. [If you disagree with this policy, you can complain to Sam Altman and his cronies.]
Tools: hand-held calculator allowed. No laptop or smartphone.Example test question:A professor prepares a SCA multiple-choice test consisting of 16 questions. For each question there are 4 possible answers of which one is correct. Correct answers are worth 1 point, incorrect ones are worth 0. Unfortunately the professor is totally incompetent and he hasn't taught anything so his 30 students just pick the answers "randomly". Which probability distribution describes the total scores of these poor students?
a. Poisson with mean parameter lambda = 7.5
b. Normal with mean parameter = 7.5 and standard deviation parameter 0.25
c. Binomial with size parameter n = 16 and success probability parameter p=0.25
d. Binomial with size parameter n = 30 and success probability parameter p=0.25
Mindestanforderungen und Beurteilungsmaßstab
Die Absolventinnen und Absolventen sind in der Lage, ausgehend von biologischen Datensätzen, biologische Fragestellungen eigenständig mit einfachen mathematischen Modellen zu bearbeiten und mit statistischen Methoden zu beantworten.Beurteilungsmaßstab der schriftlichen Klausur:
<=50%: 5
<62.5%: 4
<75%: 3
<87.5%: 2
>=87.5%: 1Mathematische Formel in LaTeX (S: "score", 0 <= S <= 1, G: "grade"):
\[ G =
\begin{cases}
5- \lceil 8 (S - 0.5) \rceil & \text{if } 0.5 \leq S \leq 1 \\
5 & \text{if } 0 \leq S < 0.5
\end{cases}
\]
<=50%: 5
<62.5%: 4
<75%: 3
<87.5%: 2
>=87.5%: 1Mathematische Formel in LaTeX (S: "score", 0 <= S <= 1, G: "grade"):
\[ G =
\begin{cases}
5- \lceil 8 (S - 0.5) \rceil & \text{if } 0.5 \leq S \leq 1 \\
5 & \text{if } 0 \leq S < 0.5
\end{cases}
\]
Prüfungsstoff
# Probability theory
- Foundations of probability theory: basic identities (sum rule, product rule). Independent variates, conditional probability.
- Discrete probability distributions: Uniform, Bernoulli, Binomial, Poisson, Negative Binomial.
- Continuous probability distributions: Uniform, Exponential, Gamma etc.
- Central Limit Theorem and the Normal distribution. Chi-square distribution.# Basic statistics
- Sampling theory: obtaining information about a population via sampling. Sample characteristics (location, dispersion, skewness).
- The distribution of the sample mean. Confidence intervals.
- Basic principles of hypothesis testing. "Student"'s t-test.
- Type I and Type II errors. P-value distributions. Power calculations.
- Distribution tests, parametric and non-parametric tests, counting statistics, contingency tables, correlation tests.# Linear models I: Regression
- Single, weighted and multivariable linear regression.
- Orthogonal regression, Principal Components Analysis.
- Linearization techniques. Orthogonal polynomial regression.# Linear models II: Analysis of variance
- One-way ANOVA: prerequisites, omnibus F-test, post hoc tests.
- Power calculations.
- The relationship between ANOVA and linear regression.
- Combination of effects: two-way ANOVA.
- Analysis of covariance# Nonlinear regression
- Least-squares nonlinear parameter estimation
- Data-driven smoothing methods# Bayesian statistics
- Bayes' Theorem
- Bayesian networksHandouts for each of the lectures are available in Moodle.
- Foundations of probability theory: basic identities (sum rule, product rule). Independent variates, conditional probability.
- Discrete probability distributions: Uniform, Bernoulli, Binomial, Poisson, Negative Binomial.
- Continuous probability distributions: Uniform, Exponential, Gamma etc.
- Central Limit Theorem and the Normal distribution. Chi-square distribution.# Basic statistics
- Sampling theory: obtaining information about a population via sampling. Sample characteristics (location, dispersion, skewness).
- The distribution of the sample mean. Confidence intervals.
- Basic principles of hypothesis testing. "Student"'s t-test.
- Type I and Type II errors. P-value distributions. Power calculations.
- Distribution tests, parametric and non-parametric tests, counting statistics, contingency tables, correlation tests.# Linear models I: Regression
- Single, weighted and multivariable linear regression.
- Orthogonal regression, Principal Components Analysis.
- Linearization techniques. Orthogonal polynomial regression.# Linear models II: Analysis of variance
- One-way ANOVA: prerequisites, omnibus F-test, post hoc tests.
- Power calculations.
- The relationship between ANOVA and linear regression.
- Combination of effects: two-way ANOVA.
- Analysis of covariance# Nonlinear regression
- Least-squares nonlinear parameter estimation
- Data-driven smoothing methods# Bayesian statistics
- Bayes' Theorem
- Bayesian networksHandouts for each of the lectures are available in Moodle.
Literatur
Siehe Handouts in Moodle. Zusätzliche Nachschlagewerke auf Englisch:- Venables, W.N. and Ripley, B.D.: Modern Applied Statistics with S-Plus. Springer, 1994.
- Hastie, T., Tibshirani, R. and Friedman, J.: The Elements of Statistical Learning, 2nd ed. Springer, 2009.
- Bishop, C. M.: Pattern Recognition and Machine Learning. Springer, 2006.
- https://www.stewartschultz.com/statistics/books/ (frei verfügbare PDFs von verschiedenen Statistikbüchern)
- Crawley, M. J.: The R Book, 2nd ed. John Wiley & Sons, 2013.Deutsche Bücher:
- Rudolf, M., Kuhlisch, W.: Biostatistik. Eine Einführung für Biowissenschaftler. Pearson Studium, 2008.
- Hastie, T., Tibshirani, R. and Friedman, J.: The Elements of Statistical Learning, 2nd ed. Springer, 2009.
- Bishop, C. M.: Pattern Recognition and Machine Learning. Springer, 2006.
- https://www.stewartschultz.com/statistics/books/ (frei verfügbare PDFs von verschiedenen Statistikbüchern)
- Crawley, M. J.: The R Book, 2nd ed. John Wiley & Sons, 2013.Deutsche Bücher:
- Rudolf, M., Kuhlisch, W.: Biostatistik. Eine Einführung für Biowissenschaftler. Pearson Studium, 2008.
Zuordnung im Vorlesungsverzeichnis
BMB 8
Letzte Änderung: Mi 17.12.2025 11:27