COURSE PROGRAMME

1. Information about the programme
1.1University / University “Alexandru Ioan Cuza” of Iaşi
1.2 Faculty / FacultyofMathematics
1.3 Department / Mathematics
1.4 Domain / Mathematics
1.5Cycle / Master
1.6Programme / Qualification / AppliedMathematics(in English)
2. Information about the course
2.1Course Name / Applied Statistics
2.2Course taught by / Iulian Stoleriu
2.3Seminary / laboratory taught by / Iulian Stoleriu
2.4Year / 2 / 2.5 Semester / 3 / 2.6Type of evaluation / E / 2.7Course type / OB

* OB – Obligatory / OP–Optionally / F – Facultative

3. Total Hours(estimated per semester and activities)
3.1Number of hours per week / 4 / 3.2 course / 2 / 3.3.seminary/laboratory / 2
3.4Total number of hours / 56 / 3.5 course / 28 / 3.6.seminary/laboratory / 28
Distribution / hours
Individual study using textbooks, course notes, bibliography items, etc. / 30
Supplimentary study (library, on-line platforms, etc.) / 26
Individual study for seminary/laboratory,homeworks, projects, etc. / 20
Tutoring / 14
Examination / 4
Other activities......
3.7Total hours of individual activity / 90
3.8Total hours per semester / 150
3.9Credit points / 6
4. Pre-requisites
4.1 Curriculum / Probability Theory, Statistical Mathematics, Calculus
4.2 Competencies / Scientific computing with MATLAB
5. Conditions(if necessary)
5.1 Course / Amphitheatre, laboratory
5.2 Seminary / Laboratory / MATLAB application
6. Specific competencies acquired
Professional competencies / C1 Manipulating notions, methodsand mathematical models, specific techniques and technologiesinscientific calculus andapplications in economyand informatics - 1 credit
C2 Data processing, analysis and interpretation using mathematical, statistical and informatics tools
- 2 credits
C3 Being able to develop, test and validate algorithms; implementation in high level programming languages- 2 credits
C4 Being able to constructand apply mathematical modelsfor analyzing and simulating some phenomena and processes- 2 credits
C5 Being able to develop, analyze and test computer systems and specific programming languages; being able to use them for solving problems in applied mathematics
C6 Being able to analyze and interpret some economic processes and phenomena
Transversal competencies / CT1 Having a responsible attitude towards scientific research and teaching, being able to fully develop the personal potential in the professional career, respecting the principles of a rigorous and efficient work in order to fulfill complex tasks, respecting the ethical norms and principles in the professional activity
CT2 Being able to work efficiently in a team and to coordinate and efficiently lead a team or an inter-disciplinary group
CT3 Being able to make a selection of information resources and to use them efficiently, in Romanian or other language of international circulation, in order to develop the professional activity and adapt it to the demands of a dynamical society- 1 credit
7. Course objectives
7.1. General objective /
  • Students will be familiarized to the terminology of Statistics and will be able to use computing tools is solving adequate statistical problems
  • Students will be able to use notions from Statistics to solve some interdisciplinary problems

7.2. Specific objectives / After successfully completing this course, the students will be able to:
  • Identify different types of statistical data;
  • Group and plot various statistical data;
  • Determine some numerical and functional characteristics of data;
  • Do inference on distribution parameters or on the distribution of observed data;
  • Identify any correlation among data and determine the correlation relation

8. Contents
8.1 / Course / Teaching methods / Remarks
(number oh hours, references)
1. / Brief review on Mathematical Statistics. Population, variables, samples, parameters, statistics, laws of Probability theory / Blackboard presentation / 2h
2. / Descriptive statistics. Sampling data, organization and graphical representation of data / Blackboard presentation / 2h
3. / Statistics and their distributions. Sampling from a normal population / Blackboard presentation / 2h
4. / Parameter estimation (general considerations, maximum likelihood method, method of moments, minimum of the χ2) / Blackboard presentation / 2h
5. / Confidence intervals (one population, two populations) / Blackboard presentation / 2h
6. / Inferential statistics (parametric tests for one or two populations) / Blackboard presentation / 2h
7. / Inferential statistics (distribution tests, contingency tests) / Blackboard presentation / 2h
8. / Inferential statistics, non-parametric tests (sign test, runs test, tests for paired data, Wald-Wolfowitz test) / Blackboard presentation / 2h
9. / Inferential statistics, non-parametric tests (signed-rank test, rank-sum test) / Blackboard presentation / 2h
10. / Randomization tests / Blackboard presentation / 2h
11. / Corelation / Blackboard presentation / 2h
12. / Simple linear regression / Blackboard presentation / 2h
13. / Multiple regression. / Blackboard presentation / 2h
14. / ANOVA (one-way and two-way) / Blackboard presentation / 2h
Bibliography
Main references:
1) I. Stoleriu, Statistica prin MATLAB, Editura MatrixRom, Bucuresti, 2010.
2) J.L. Devore, K.N. Berk, Modern Mathematical Statistics with Applications, second edition, Springer, 2012.
3) D. Wackerly, W.Mendenhall, R.L. Scheaffer, Mathematical Statistics With Applications, Duxbury Press, 7th edition, 2007.
4) M.R. Spiegel, L.J. Stephens, Schaum's Outline of Statistics, McGraw-Hill, 2007.
Other references:
5) Gh. Mihoc, N. Micu: Teoria probabilităţilor si statistică matematică, Bucuresti, 1980
6) E.Nenciu: Lectii de statistica matematica, Universitatea A.I.Cuza, Iasi, 1976.
..
8.2 / Seminary / Laboratory / Teaching methods / Remarks
(number oh hours, references)
1. / Graphical representation of data,
Random experiments with MATLAB / Exercises solved on the blackboard
and PC simulations / 2h
2. / Descriptive statistics with MATLAB / Exercises solved on the blackboard and PC simulations / 2h
3. / Parameter estimation / Exercises solved on the blackboard and PC simulations / 2h
4. / Sampling from a normal population
Distribution of the sample mean and sample variance / Exercises solved on the blackboard and PC simulations / 2h
5. / Confidence intervals with MATLAB (one and two populations) / Exercises solved on the blackboard and PC simulations / 2h
6. / Hypothesis testing with MATLAB (parametric tests for one or two samples) / Exercises solved on the blackboard and PC simulations / 2h
7. / Hypothesis testing with MATLAB (distribution tests, contingency tests) / Exercises solved on the blackboard and PC simulations / 2h
8. / Hypothesis testing with MATLAB
(non-parametric tests) / Exercises solved on the blackboard and PC simulations / 2h
9. / Hypothesis testing with MATLAB
(signed-rank test, rank-sum test) / Exercises solved on the blackboard and PC simulations / 2h
10. / Randomization tests (permutation tests, bootstrapping) / Exercises solved on the blackboard and PC simulations / 2h
11. / Tests for correlation coefficient / Exercises solved on the blackboard and PC simulations / 2h
12. / Simple non-parametric regression / Exercises solved on the blackboard and PC simulations / 2h
13. / Multiple Regression with MATLAB / Exercises solved on the blackboard and PC simulations / 2h
14. / ANOVA with MATLAB / Exercises solved on the blackboard and PC simulations / 2h
References
1) I. Stoleriu, Statistica prin MATLAB, Editura MatrixRom, Bucuresti, 2010.
2) J. L. Devore, K.N. Berk, Modern Mathematical Statistics with Applications, Duxbury, 2007.
3) D. Wackerly, W.Mendenhall, R. L. Scheaffer, Mathematical Statistics With Applications, Duxbury
Press, 7th edition, 2007.
4) M.R. Spiegel, L.J. Stephens, Schaum's Outline of Statistics, McGraw-Hill, 2007.
5) Gh. Mihoc, N. Micu: Teoria probabilităţilor si statistică matematică, Bucuresti, 1980
9. Coordination of the contents with the expectations of the community representatives, professional associations and relevant employersin the corresponding domain
Students could use the aquired information from this course in any area that employs statistical data
10. Assessment and examination
Activity / 10.1 Criteria / 10.2 Modes / 10.3 Weight in the final grade (%)
10.4Course / Final examination / 90%
10.5 Seminary/Laboratory / Class activity/homework / 10%
10.6Minimal requirements minimum grade 5
1. Basic knowledge of various statistical notions and the ability to apply them in solving simple problems
2. Theability to use MATLAB functions for solving statistical problems.
3. Interpretation of the results.
Date / Course coordinator / Seminary coordinator
2/10/2017 / Dr. Iulian Stoleriu / Dr. Iulian Stoleriu
Aproval date in the department / Head of the departament
Prof. Dr. Ioan Bucătaru