Mathematical Statistics
General data
Course ID:  2400PP2STa  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 Englishlanguage course offering of the Faculty of Economics Mandatory courses for IIyear, 1st cycle students of Economics  collective courses Specialization courses for IIyear, 1st cycle students of Economics of Enterprise Specialization courses for IIyear, 1st cycle students of Finance and Accounting Specialization courses for IIyear, 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 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, CramerRao inequality 6. Confidence intervals 7. Testing statistical hypotheses: test of significance, NeymanPearson 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: 
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:  20200217  20200802 
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 
Copyright by University of Warsaw.