 # Statistical Methods & Data Analysis

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.

none

### course contents

Part A: Fundamentals of Statistics:
• Introduction to descriptive statistics (graphical representation of data and distributions, 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 cardinally 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.

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.

- Seminar Paper
- Final Exam

German

2

25

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.

1

### name of lecturer(s)

Prof. (FH) Dr. Dr. Mario Situm

FIN 2

### type of course unit

integrated lecture

Compulsory

not applicable

### Contact

Prof. (FH) DDr. Mario Situm, MBA
+43 5372 71819 147
Mario.Situmfh-kufstein.ac.at
Any questions?
+43 5372 71819 500
bewerbungfh-kufstein.ac.at     Infofolder

### Partner 