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Statistical Methods & Data Analysis

level of course unit

Bachelor

Learning outcomes of course unit

The students
• Possess basic knowledge of quantitative methods in economics and basic knowledge of statistical methods and procedures for describing and analyzing economic data.
• Are able to evaluate and perform descriptive statistics (empirical distribution, mean values, measures of dispersion), probability calculations, one• and two-dimensional random variables, theoretical distributions, random samples and sample distributions as well as estimation procedures (confidence intervals) and test procedures (parameter tests, analysis of variance, distribution tests) and regression analyses (linear single and multiple regression).
• Are able to structure and compile larger data sets.

prerequisites and co-requisites

none

course contents

Part A: Fundamentals of Statistics:
• Introduction to descriptive statistics (graphical representation of data and dis-tributions, calculations of statistical central and scatter measures, test for normal distribution of data) and data interpretation
• Introduction to closing statistics (difference test for nominal, ordinal and cardi-nally scaled data)
• Introduction to correlation and factor analysis

Part B: Structure of a data set and variable declaration:
• Structure and structure of a data set for statistical analysis using software
• Determination and development of variables (dependent, independent, dummy, interaction) and scaling (nominal, ordinal, interval, cardinal)

Part C: Fundamentals of regression analysis:
• Introduction to linear regression (basic model, estimation methods, integration of non-linear variables, statistical significance, assessment measures of estimation quality) incl. interpretation of results

The (theoretical) contents will be expanded by practical examples including soft-ware support.

recommended or required reading

Bamberg, G., Baur, F., & Krapp, M. (2017). Statistik: Eine Einführung für Wirtschafts- und Sozialwissenschaftler. Berlin: Walter de Gruyter.
Cleff, T. (2015). Deskriptive Statistik und Explorative Datenanalyse: Eine computergestützte Einführung mit Excel, SPSS und STATA. Wiesbaden: Springer Verlag.
Kohn, W., & Öztürk, R. (2017). Statistik für Ökonomen: Datenanalyse mit R und SPSS. Wiesbaden: Springer Verlag.
Leohnhart, R. (2017). Lehrbuch Statistik: Einstieg und Vertiefung. Bern: Hogrefe Verlag.
Steland, A. (2016). Basiswissen Statistik: Kompaktkurs für Anwender aus Wirtschaft, Information und Technik. Berlin-Heidelberg: Springer Verlag.
Zwerenz, K. (2015). Statistik: Einführung in die computergestützte Datenanalyse. Berlin: Walter de Gruyter.

assessment methods and criteria

- Seminar Paper
- Final Exam

language of instruction

German

number of ECTS credits allocated

2

course-hours-per-week (chw)

2

planned learning activities and teaching methods

25 % of the event is covered by eLearning. A combination between online phases (inductive method for the independent acquisition of knowledge and the practice of tasks) and presence phases (deductive method, in which assistance is given in the learning process and knowledge is imparted via frontal lectures) is used.

semester/trimester when the course unit is delivered

1

name of lecturer(s)

Prof. (FH) Dr. Dr. Mario Situm

course unit code

FIN 2

type of course unit

integrated lecture

mode of delivery

Compulsory

work placement(s)

not applicable