Course detail

Analysis of statistical data files

FAST-CA07Acad. year: 2013/2014

Survey analysis of one- and two-dimensional populations. Parametric problems with one and two random samples. Non-parametric problems. Analysis of variance in a one-factor model. Analysis of relationships. Regression analysis. Use of the EXCEL program.

Language of instruction

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

doc. RNDr. Jiří Novotný, CSc.

Department

Institute of Mathematics and Descriptive Geometry (MAT)

Learning outcomes of the course unit

Knowledge of basics on descriptive statistics of one- and two-dimensional populations. Knowledge of statistical tests and basics on analysis of variance, analysis of relationships and regression analysis. Knowledge of using the statistical programs.

Prerequisites

Basics of linear algebra, calculus, probability theory, and statistics as taught in the basic mathematics courses.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations - lecture, seminar.

Assesment methods and criteria linked to learning outcomes

Submission of solutions to problems assigned by the teacher for home work. Unless properly excused, students must attend all the workshops. The result of the semester examination is given by the sum of maximum of 80 points obtained for a written test and a maximum of 20 points from the seminar.

Course curriculum

1.Basics of descriptive statistics. Using EXCEL and STATISTICA.
2.Functional characteristics of one- and two-dimensional data.
3.Numeric characteristics of data.
4.Numeric characteristics of data. Diagnostic graphs.
5. Basics of mathematical statistics. Parametric problems with one random sample.
6.Parametric problems with two random smaples. Comparing variances and means.
7.Non-parametric tests. Goodness-of-fit tests.
8.Goodness-of-fit tests. Kolmogorov-Smirnov test.
9.Analysis of variance in a one-factor model.
10.Analysis of variance in several-factor models.
11.Analysis of relationships.
12.Regression analysis.
13.Regression analysis.

The time schedule of the seminars follows that of lectures.

Work placements

Not applicable.

Aims

To acquaint the students with methods used to process statistical data, calculate point and interval estimates and test statistical hypotheses. Teach them how to perform and use regression analysis, tell whether the components of a random vector are independent or not. Next the students should be able to use analysis of variance to plan experiments.

Specification of controlled education, way of implementation and compensation for absences

Extent and forms are specified by guarantor’s regulation updated for every academic year.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

MELOUN, Milan a MILITKÝ, Jiří: Statistické zpracování experimentálních dat. Praha. Academia, 2004. (CS)
BUDÍKOVÁ, Marie, KRÁLOVÁ, Maria a MAROŠ, Bohumil: Průvodce základními statistickými metodami. Praha: GRADA, 2011. ISBN 978-80-247-3243-5. (CS)

Recommended literature

WALPOLE, Ronald E. a MYERS, Raymond H.: Probability and Statistics for Engineers and Scientists. New York: Macmillan Publishing Company, 1990. ISBN 0-02-946910-4. (EN)
ANDĚL, Jiří: Statistické metody. Praha: MATFYZPRESS, 1998. ISBN 80-85863-27-8. (CS)
ŠŤASTNÝ, Zdeněk: Matematické a statistické výpočty v Microsoft Excelu. Praha: Computer Press, 1999. (CS)
KOUTKOVÁ, Helena a MOLL, Ivo: Základy pravděpodobnosti. Brno: AN CERM, 2008. ISBN 978-80-7207-574-7. (CS)

Classification of course in study plans

  • Programme N-K-C-SI Master's

    branch S , 1 year of study, summer semester, compulsory

  • Programme N-P-C-SI Master's

    branch S , 1 year of study, summer semester, compulsory

  • Programme N-P-E-SI Master's

    branch S , 1 year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. RNDr. Jiří Novotný, CSc.

Syllabus

1. Basics of descriptive statistics.
2. Functional characteristics of one- and two-dimensional data.
3. Numeric characteristics of data.
4. Diagnostic graphs.
5. Parametric problems with one random sample.
6. Parametric problems with two random samples.
7. Non-parametric tests.
8. Goodness-of-fit tests. Kolmogorov-Smirnov test.
9. Analysis of variance in a one-factor model.
10. Analysis of variance in several-factor models.
11. Analysis of relationships.
12. Regression analysis I.
13. Regression analysis II.

Exercise

13 hod., compulsory

Teacher / Lecturer

doc. RNDr. Jiří Novotný, CSc.

Syllabus

1. Graphical methods of data files representation I.
2. Graphical methods of data files representation II.
3. Computational methods of data processing I.
4. Computational methods of data processing II.
5. Summary of survey analysis of one-dimensional populations.
6. Two-dimensional data files.
7. Linear regression.
8. Nonlinear regression.
9. Linear forecasting.
10. Multiple correlation and regression.
11. Statistical induction.
12. Interval estimates.
13. Tests of statistical hypotheses. Seminar evaluation.