151-0360/01 – Data analysis for diploma thesis (ADDP)

Gurantor departmentDepartment of Mathematical Methods in EconomicsCredits3
Subject guarantorprof. RNDr. Dana Šalounová, Ph.D.Subject version guarantorprof. RNDr. Dana Šalounová, Ph.D.
Study levelundergraduate or graduateRequirementChoice-compulsory
Year2Semesterwinter
Study languageCzech
Year of introduction2010/2011Year of cancellation
Intended for the facultiesEKFIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
FRI02 doc. Ing. Václav Friedrich, Ph.D.
POM68 RNDr. Marek Pomp, Ph.D.
S1A20 prof. RNDr. Dana Šalounová, Ph.D.
SED02 Ing. Petr Seďa, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit 0+2

Subject aims expressed by acquired skills and competences

Students will be able to create and modify datasets in SPSS or R, analyse them, interpret and present outcomes.

Teaching methods

Lectures
Tutorials

Summary

The aim of the subject is to acquaint students with tools of statistical software SPSS or R for analysis both numeric and non-numeric data. The choise of methods is oriented to data processing from marketing and sociological research needed for master's thesis. The subject is based on knowledge from the subject Statistika A and Statisitka B. Used software: IBM SPSS, R, MS Excel.The subject doesn't deal with analysis of time series.

Compulsory literature:

PALLANT, Julie. SPSS survival manual: a step by step guide to data analysing using SPSS. McGraw-Hill/Open University Press, Maidenhead, 2010, ISBN 978-0-33-524239-9.

Recommended literature:

NORUŠIS, Marija. IBM SPSS Statistics 19 Guide to Data Analysis. Pearson 2012, ISBN-10: 0321748417, ISBN-13: 9780321748416.

Way of continuous check of knowledge in the course of semester

Practical excercises.

E-learning

Další požadavky na studenta

Statistics B is needed.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

1. Design of research - sample methods, research hypothesis, sample size. 2. Data collections, creating a data file and entering data in SPSS or R, describing data sets, missing data, modyfying a data file, transforming variables. 3. Descriptive statistics, exploratory analysis, tables, graphs. 4. Principle of table and graphs creating with respect to their presentation. 5. Hypothesis testing on probability distribution. 6. Tests of independence of nominal variables. 7. Tests of independence of continuous variable, correlation, regression. 8. Comparing groups, independent samples, paired samples. 9. One-sided dependence, logistic regression. 10. Output processing, pivoting tables, editing charts and graphs, exporting of outputs to another programs.

Conditions for subject completion

Full-time form (validity from: 2010/2011 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Exercises evaluation Credit 85  85
Mandatory attendence parzicipation: 100 %

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