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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 Organizational unit: Faculty of Economic Sciences Course groups: (in Polish) Przedmioty 4EU+ (z oferty jednostek dydaktycznych) (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 Prerequisites (description): Online (moodle) Short description: The objective of the course is to introduce the basic topics of mathematical statistics, in the extent it may be of use for economists (in statistical analysis, in econometrics). The aim of the course is to familiarize the students with the basic concepts and methods of statistical inference, in order to allow the students to conduct basic analyses and properly interpret the results of such analyses. The emphasis is placed on the understanding of concepts and connecting mathematical models with real-life phenomena (mainly in economics). Assessment: exam consists of problems to solve, held online Prerequisite: Probability Calculus Full description: 1. Introduction: paradigm of Mathematical Staistics; mathematical models and empirical inference. 2. Probability distributions and their empirical counterparts 3. Statistical models: families of probability distributions, parametric and nonparametric models 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 Bibliography: 1. Michel Lavine, Statistical Thought, available online: www.stat.duke.edu/~michael/book.html Learning outcomes: KNOWLEDGE 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. SKILLS 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. COMMON SKILLS The student knows the applications of theories and methods of mathematical statistics in economics and related sciences Assessment methods and assessment criteria: Discussions assessment: The class grade is based on the sum of points obtained from: three tests (max 20 points each), homework assignments (max 20 points) and class activity (max 20 points). A student needs to have at least 50 points and at most two absences to pass classes. Lecture assessment: the final grade is based on the weighted average: 1/3 class grade + 2/3 final exam grade, with the final exam grade based on the results of an online exam. The exam will consist of 8 problems to solve; the answers will need to be marked in an online test and scans of problem solutions will need to be submitted.

## Classes in period "Summer semester 2020/21" (past)

 Time span: 2021-02-22 - 2021-06-13  Choosen plan division: this week course term  see course schedule Type of class: Class, 30 hours more information Lecture, 30 hours more information Coordinators: Anna Janicka Group instructors: Anna Janicka Students list: (inaccessible to you) Examination: Course - Examination Class - Grading Lecture - Examination Mode: Remote learning

## Classes in period "Summer semester 2021/22" (future)

 Time span: 2022-02-21 - 2022-06-15  Choosen plan division: this week course term  see course schedule Type of class: Class, 30 hours more information Lecture, 30 hours more information Coordinators: (unknown) Group instructors: (unknown) Students list: (inaccessible to you) Examination: Course - Examination Class - Grading Lecture - Examination
Course descriptions are protected by copyright.