Lehrveranstaltungsprüfung
301350 VO Quantitative Methoden in der Molekularbiologie (2025W)
Labels
WANN?
Montag
16.03.2026
14:00 - 16:00
BZB/Seminarraum 1/2, 6.Ebene 6.501/6.504, Dr.-Bohr-Gasse 9, 1030 Wien
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mi 01.10.2025 09:00 bis So 15.03.2026 09:00
- Abmeldung bis So 15.03.2026 09:00
Prüfer*innen
Information
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.
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"):
\[ G =
\begin{cases}
5- \lceil 8 (S - 0.5) \rceil & \text{if } 0.5 \leq S \leq 1 \\
5 & \text{if } 0 \leq S < 0.5
\end{cases}
\]
Letzte Änderung: Mo 27.07.2026 09:27