301350 VO Quantitative Methoden in der Molekularbiologie (2024W)
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
Details
Sprache: Englisch
Prüfungstermine
- Dienstag 04.02.2025 14:00 - 16:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Dienstag 18.02.2025 14:00 - 16:00 STB/Hörsaal B Campus Vienna Biocenter 5, 1030 Wien
- Dienstag 04.03.2025 14:00 - 16:00 BZB/Seminarraum 3, 6.Ebene 6.105, Dr.-Bohrgasse 9, 1030 Wien
- Dienstag 11.03.2025 14:00 - 16:00 BZB/Seminarraum 2, 6.Ebene 6.507, Dr.-Bohr-Gasse 9, 1030 Wien
- N Dienstag 15.04.2025 13:00 - 17:00 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 Di. 13.00 - 15.00 Uhr
HS A/B, VBC 5, 1030 Wien
- Dienstag 01.10. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Dienstag 08.10. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Dienstag 15.10. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Dienstag 22.10. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Dienstag 29.10. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Dienstag 05.11. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Dienstag 12.11. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Dienstag 19.11. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Dienstag 26.11. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Dienstag 03.12. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Dienstag 10.12. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Dienstag 17.12. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Dienstag 07.01. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Dienstag 14.01. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Dienstag 21.01. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Dienstag 28.01. 13:00 - 15:00 STB/Hörsaal A 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 und Enzymkinetik 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.
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.
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. Let's assume that the professor is 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.
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. Let's assume that the professor is 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.# 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.# Modelling biochemical reaction networks
- Stock-and-flow models.
- Biochemical kinetics: Michaelis-Menten enzyme kinetics models. Competitive and non-competitive inhibition.# 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# 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.# 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.# Modelling biochemical reaction networks
- Stock-and-flow models.
- Biochemical kinetics: Michaelis-Menten enzyme kinetics models. Competitive and non-competitive inhibition.# 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# Bayesian statistics
- Bayes' Theorem
- Bayesian networksHandouts for each of the lectures are available in Moodle.
Literatur
Siehe Handouts in Moodle. Zusätzliche Nachschlagewerke:- 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.
- Crawley, M. J.: The R Book, 2nd ed. John Wiley & Sons, 2013.
- Hastie, T., Tibshirani, R. and Friedman, J.: The Elements of Statistical Learning, 2nd ed. Springer, 2009.
- Crawley, M. J.: The R Book, 2nd ed. John Wiley & Sons, 2013.
Zuordnung im Vorlesungsverzeichnis
BMB 8
Letzte Änderung: Fr 28.03.2025 13:06