Course Exam
301350 VO Quantitative methods in molekularbiology (2024W)
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WHEN?
Please try to arrive around 13:45 so that you have time to make yourselves comfortable etc. The exam will start at 14:00 the latest, but if everyone is present and ready we might even start a few minutes earlier.
The exam will take max. 120 minutes (2 hours).
There will be 40 questions in a single-correct-answer-multiple-choice test. "Closed book" = no sources allowed. Definitely no laptops/smartphones please. You may bring a hand-held calculator with you if you wish (there might be some simple calculations among the questions). You will need a black or blue pen to fill out the answer form.
The exam language is in English. If you don't fully understand a question or the answer options, please ask Zach or me, we will help.
Food & drink are allowed as usual.Thank you, see you tomorrow!
András
The exam will take max. 120 minutes (2 hours).
There will be 40 questions in a single-correct-answer-multiple-choice test. "Closed book" = no sources allowed. Definitely no laptops/smartphones please. You may bring a hand-held calculator with you if you wish (there might be some simple calculations among the questions). You will need a black or blue pen to fill out the answer form.
The exam language is in English. If you don't fully understand a question or the answer options, please ask Zach or me, we will help.
Food & drink are allowed as usual.Thank you, see you tomorrow!
András
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Tu 01.10.2024 09:00 to Mo 03.03.2025 09:00
- Deregistration possible until Mo 03.03.2025 09:00
Examiners
Information
Examination topics
# 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.
Assessment and permitted materials
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
Minimum requirements and assessment criteria
Participants will be able to investigate biological questions using molecular biologic data sets using basic mathematical models and statistical methods.Evaluation scale of written exam:
<=50%: 5
<62.5%: 4
<75%: 3
<87.5%: 2
>=87.5%: 1Mathematical formula 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%: 1Mathematical formula in LaTeX (S: "score", 0 <= S <= 1, G: "grade"):
\[ G =
\begin{cases}
5- \lceil 8 (S - 0.5) \rceil & \text{if } 0.5 \leq S \leq 1 \\
5 & \text{if } 0 \leq S < 0.5
\end{cases}
\]
Last modified: Fr 28.03.2025 13:06