|
|
|
||
|
Foundations of statistical methods. Recommended for bachelor's program in General Mathematics, specialization
Stochastics.
Last update: G_M (16.05.2012)
|
|
||
|
The students will become familiar with basic methods for statistical data analysis. Last update: G_M (16.05.2012)
|
|
||
|
Requirements for earning the course credit:
1. Successful completion of the written exam (at least 60 points out of 100), with exactly one opportunity to retake the exam.
2. Satisfactory completion (at least 60% of the possible points) of each of the two homework assignments. Two homework assignments will be assigned, for which a total of 100 points can be earned (40 points for the first assignment, 60 for the second). A total score of at least 60 points for both assignments combined is considered a satisfactory completion.
Passing this course is a prerequisite for taking the exam. Last update: Komárek Arnošt, prof. RNDr., Ph.D. (31.08.2026)
|
|
||
|
Mukhopadhyay, N. (2000). Probability and statistical inference. CRC Press Last update: Omelka Marek, doc. Ing., Ph.D. (28.10.2019)
|
|
||
|
Lecture+exercises. Last update: T_KPMS (11.05.2012)
|
|
||
|
The exam will cover the entire scope of the course. Students must know all essential definitions, theorems, and statements (including assumptions), understand their interrelationships, and be able to explain, at least in general terms, their justifications (proofs). Furthermore, students are required to be able to select an appropriate method for the statistical analysis of a real-world problem and to discuss the advantages and disadvantages of various alternative solutions (if any).
The exam consists of a written and an oral portion. The written portion precedes the oral portion, and failure to pass it results in the entire exam being graded as “fail,” and the student does not proceed to the oral portion. Failure to pass the oral portion of the exam means that both parts of the exam—the written and the oral—must be retaken at the next exam session. The exam grade is determined based on the scores from both the written and oral sections.
The written section consists of several questions based on the material covered in lectures and corresponding to what was practiced in class.
The requirements for the oral portion of the exam correspond to the course syllabus to the extent that the material was presented in lectures. Last update: Komárek Arnošt, prof. RNDr., Ph.D. (31.08.2026)
|
|
||
|
1. Random sample. Distribution of sample mean and variance. Order statistics.
2. Point and interval estimates - basic principles. Empirical estimates, sample moments and quantiles.
3. Hypothesis testing principles.
4. One-sample and paired methods for quantitative data.
5. Two-sample methods for quantitative data.
6. One-sample and two-sample methods for binary adata.
7. Multinomial distributions and contingency tables.
8. Multi-sample methods for quantitative data. Analysis of variance. Multiple comparison principles.
9. Correlation analysis. Last update: Omelka Marek, doc. Ing., Ph.D. (22.09.2019)
|
|
||
|
Basics of probability and statistics - e.g. NMSA202. Last update: Zichová Jitka, RNDr., Dr. (12.05.2025)
|