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Mathematical Statistics

General data

Course ID: 2400-PP2STa Erasmus code / ISCED: 14.3 / (0311) Economics
Course title: Mathematical Statistics Name in Polish: Mathematical Statistics
Department: Faculty of Economic Sciences
Course groups: (in Polish) Przedmioty obowiązkowe dla II r. studiów licencjackich - Ekonomia Międzynarodowa
(in Polish) Przedmioty obowiązkowe dla II r.licencjackich: Ekonomia, specjalność: MSEMen
English-language course offering of the Faculty of Economics
Mandatory courses for II-year, 1st cycle students of Economics - collective courses
Specialization courses for II-year, 1st cycle students of Economics of Enterprise
Specialization courses for II-year, 1st cycle students of Finance and Accounting
Specialization courses for II-year, 1st cycle students of Information Technology and Econometrics
ECTS credit allocation (and other scores): 5.00
view allocation of credits
Language: English
Type of course:

obligatory courses

Short description:

The aim of the course is to acquire working knowledge of basic notions and methods of

Mathematical Statistics, to the degree sufficient to properly interpret statistical analyses and

also to perform simple analyses. The emphasis is on understanding the methods and relations

between mathematical models and real phenomena, mostly in economics. At the end of the

course, final EXAM (solving several problems). Prerequisite: course of Probability.

Full description:

1. Introduction: paradigm of Mathematical Staistics; mathematical models and empirical


2. Probability distributions and their empirical counterparts

3. Statistical models: families of probability distributions, parametric and nonparametric


4. Methods of estimation: the method of moments, maximum likelihood

5. Properties of estimators: bias, mean square error, Mimimum variance unbiased estimators,

Cramer-Rao inequality

6. Confidence intervals

7. Testing statistical hypotheses: test of significance, Neyman-Pearson lemma, most powerful

tests, typical parametric

and nonparametric tests

8.Selected special topics: e.g. Bayesian approach or introduction to multivariate analysis


1. Michel Lavine, Statistical Thought, available online:

Learning outcomes:


The student knows and understands selected concepts of probability calculus and mathematical statistics, the most important of which is a random variable, distribution of a random variable, basic characteristics of the distribution of a random variable and types of random variables. Knows the theory of statistical inference, point estimation, interval estimation, the theory of verification of statistical hypotheses. The student knows parametric and nonparametric models for verification of hypotheses regarding theoretical distribution.


The student is able to use the tools of mathematical statistics. He can use selected statistical procedures. Student is able to describe models in formal statistical language. The student is able to use analytical methods to correctly formulate and solve tasks in the field of mathematical statistics. The student is able to construct an unbiased and effective parameter estimator using the chosen method. The student is able to estimate the parameter using the confidence interval. He can verify the hypothesis regarding theoretical distribution.


The student knows the applications of theories and methods of mathematical statistics in economics and related sciences

Assessment methods and assessment criteria:

The student counts exercises (100%) based on 2 tests (60%), unannounced small tests (20%) and homework (20%), and the subject ends with a written exam. The final grade is 1/3 of the exercise grade + 2/3 of the exam grade.

Classes in period "Summer semester 2019/20" (past)

Time span: 2020-02-17 - 2020-08-02
Choosen plan division:

see course schedule
Type of class: Class, 30 hours more information
Lecture, 30 hours more information
Coordinators: Anna Janicka
Group instructors: Adam Czerwiński, Anna Janicka
Students list: (inaccessible to you)
Examination: Course - Examination
Class - Grading
Lecture - Examination
Course descriptions are protected by copyright.
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