Study programme

Statistics (146038)

ECTS 3.00
Teaching hours 30
Lectures 20
Practicum 10
Lecturer
Assist. Prof. Biserka Kolarec, PhD
Associate teacher for exercises
Assist. Prof. Biserka Kolarec, PhD
Grading
Sufficient (2) 60-69 %
Good (3) 70-79 %
Very good (4) 80-89 %
Excellent (5) 90-100%

Course coordinator

Assist. Prof. Biserka Kolarec, PhD
Assist. Prof. Biserka Kolarec, PhD

Course description

The course includes the elements of statistical inference and metodologies for data analysis: comparing two and more populations, analysis of variance, simple and multiple regresion, time series analysis and factor analysis to the data gained from the environmental studies.

Type of course

General competences

- raising the level of statistical literacy
- acquiring knowledge and skills necessary to understand, analyze and solve problems arising in the course of practical work in environmental studies
- developing the ability to critically assess and interpret statistical data and to avoid common pitfalls
- using statistical software with confidence

Types of instruction

  • Auditory Exercises
  • Lectures
    mastering statistical methods through concrete problems from environmental studies
  • Other forms of group or individual learning
  • Practicum
    on computers using relevant software packages

Learning outcomes

Learning outcome Evaluation methods
ability to compare two or more populations, construct confidence intervals and test hypothesis concerning them Homework, practical work, exam
do analysis of variance using ANOVA test project
do modeling with simple and multiple regression including using the obtained models for prediction homework, individual work
get familiar with time series models, determination of linear trend in time series analysis and seasonal and cyclical effects, using models for prediction homework, project task, exam
understanding the basic principles of factor analysis homework, exam

Working methods

Teachers' obligations

1. Course planning
2. Selection and creation of teaching materials
3. Evaluation of course, teaching materials and curriculum
4. Construct tests
5. Grade students on the basis of their achievement

Students' obligations

1. Attend lectures regularly
2. Do homeworks and participate actively during lectures
3. Write tests and win at least 25% of points on each test to get the signature
4. Do individual projects

Methods of grading

Evaluation elements Maximum points or Share in evaluation Grade rating scale Grade Direct teaching hours Total number of average student workload ECTS
1st exam 50 % 14 20 1
2nr exam 25 % 8 20 1
3rd exam 25 % 8 20 1
activity >10 %
Total 100 % 30 60 3

Weekly class schedule

  1. Short repetition L - normal distribution, interval estimations, testing hypothesis.
  2. Comparing two populations L - types of samples, interval estimations and hypothesis testing about the difference between two population means
  3. Comparing two populations L -interval estimations and hypothesis testing about the difference between two population proportions.
  4. Comparing two populations E - excercises on comparing two populations.
  5. Analysis of variance L - analysis of variance: F-distribution, ANOVA test, conditions on applicability.
  6. Analysis of variance E - the use of ANOVA test.
  7. Linear regression L - simple linear regression, least squre line, linear correlation.
  8. Linear regression L+E - multiple regression.
  9. Linear regression E - excercises on regression.
  10. Time series analysis L - components, models.
  11. Time series analysis L - determination of linear trend, seasonal and cyclical effects.
  12. Time series analysis E - exercises on concrete dana.
  13. Factor analysis L - Method of common factors, standardizing variables.
  14. Factor analysis L - correlation matrix, identifying factors, common and individual factors.
  15. Factor analysis E - excercises on factor analysis.

Obligatory literature

  1. M. Dekking, C. Kraaikamp, H. P. Lopuhaa, L. E. Meester: Modern introduction to probability and statistics: understanding why and how, Springer Verlag, London, 2005.
  2. Richard A. Johnson, Dean W. Wichern: Applied Multivariate Statistical Analysis(6th Edition), Pearson Prentice Hall, 2007.
  3. G. van Belle: Statistical Rules of Thumb, Willey-Interscience, 2002.
  4. P.S. Mann: Statistics for Business and Economics, J. Wiley, N. Y., 1995.

Recommended literature

  1. P. Kline: An Easy Guide to Factor Analysis, Routledge, London and New York, 2008.
  2. D. R. Nielsen, O. Wendroth: Spatial and Temporal Statistics, Catena Verlag, 2003.

Similar course at related universities

  • Matematik und Statistik, BOKU
  • Statistik, University of Hohenheim