News
- There will be no lecture on Tuesday, May 10.
Course outline
- Introduction to statistics. Data preprocessing
- Exploratory data analysis (descriptive statistics, data visualization)
- Brief review of probability theory
- Probability distributions. Central limit theorem
- Point estimates (maximum likelihood method, method of moments)
- Confidence intervals, testing of statistical hypotheses
- Model selection. Normality testing
- Testing of statistical hypotheses II (two sample tests)
- ANOVA, tests of independence, contingency tables
- Nonparametric tests
- Linear regression model (revision and generalization)
- Final remarks (revision, bootstrap, Monte Carlo simulations, visualization)
Prerequisities
- Basic knowledge of mathematical analysis: functions, limits of sequences and functions, derivations and integral for real and multidimensional functions.
- Basic knowledge of linear algebra: matrices and determinants, eigenvalues and eigenvectors.
- Basic knowledge of probability theory: probability, random variables and vectors, limit theorems.
- Basic knowledge of linear regression models.
Review (self study) of probability theory
- Sigma algebra, probability measure and its properties, Kolmogorov definition of probability, conditional probability, independent events.
- Random variables and vectors, their distributions, properties and connections. Discrete and continuous random variables and random vectors, their distributions. Numerical characteristics of random variables and random vectors. Independent random variables.
- Law of large numbers, central limit theorem.
-
Chyba: Odkazovaný objekt neexistuje nebo nemáte právo jej číst.https://is.muni.cz/el/fi/jaro2022/MV013/um/literature/All_of_Statistics__A_Concise_Course_in_Statistical_Inference__Springer_Texts_in_Statistics____PDFDrive__.pdf
- Chyba: Odkazovaný objekt neexistuje nebo nemáte právo jej číst.https://is.muni.cz/el/fi/jaro2022/MV013/um/literature/casella_berger_statistical_inference1.pdf
- If the students do not have sufficient knowledge the above terms, they have to self-study it.
- Week 3 - 4 : A brief overview of the above terms.
- From that on, we will assume that all the students know it, even with all details not mentioned in the lectures.
Assessment methods
- 4 homework assignments (from each you can get up to 10 points).
- Final written exam - open notes (you can get up to 60 points).
• Grading:
| 100-90 | A |
| 89-80 | B |
| 79-70 | C |
| 69-60 | D |
| 59-50 | E |
| 49-0 | F |
The final exam
- The exam type: written with open notes.
- Time for the exam: 100 minutes.
- During the exam, you will be asked to prove your identity (showing your ID).
- If anybody has recorded disability in Information System and needs special treatment, let me know before the exam (in advance). Subsequent demands will not be taken into account.
- The exam language is English.
- The exam will be written. You will write down your solution on separate sheets of paper.
- You may use any materials available, but communication with others is prohibited.
- You may use R for computations, but all the relevant results state in your solution (you will not submit your R-code).
- Google or Wikipedia solutions will not be accepted.
- Unreadable solutions will be ignored.
- Sample exam:
Chyba: Odkazovaný objekt neexistuje nebo nemáte právo jej číst.https://is.muni.cz/el/fi/jaro2022/MV013/um/MV013-sample-exam.pdf
Homework assignments
- The assignment will be published centrally on Wednesday afternoon/evening (4 times a semester, not regularly).
- Deadline: the next Wednesday 12:00.
- Solution (pdf and source code) upload to "Homework vaults" in the Information system.
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