DATA ANALYSIS AND DECISION MAKING

Course Description

Effective leadership requires not only the ability to understand and interpret statistical data, but also how to collect and analyze it efficiently. The course "Data Analysis and Decision Making" covers important foundations of descriptive and inferential statistics, regression analysis, and decision theory. These topics are explained on the basis of a large number of examples. The main objectives are to introduce the basic ideas of methods and analysis, their practical application, and the interpretation of results, and how the analysis of statistical data influences decision making processes.

Learning Objectives

By the end of the course the student will be able to

  • distinguish different types of data
  • describe data by measures of central tendency [(arithmetic) mean, median, mode, quartile and percentile] and dispersion [range, (semi) interquartile range, variance, standard deviation, coefficient of variation, skewness, kurtosis]
  • interpret different visualizations of different types of data [bar chart, component or stacked bar chart, multiple bar chart, pie chart, histogram, cumulative frequency curve, scatter plot, Box(-and-whisker)-plot, line graph]
  • understand different probability distributions [continuous (normal, standard normal), discrete (binomial, Poisson)]
  • understand the concept of estimation and related key words [sample (size), population, confidence interval]
  • test different hypotheses using the appropriate parametric tests [one sample Z-test for the population mean, one sample t-test for the population mean, two sample Z-test for the population mean, two sample Z-test for the population proportion, pooled-variance t-test, separate-variance t-test, two sample t-test: paired sample for means, F-test for variance]
  • test different hypotheses using chi-square and non-parametric tests [chi-square test of association, Sign test, Wilcoxon signed rank sum (or matched pairs) test, Mann-Whitney U test for two independent samples]
  • test different kinds of hypotheses using factorial tests [single-factor (one-way) ANOVA, two-factor (two-way) ANOVA without replication, two-way ANOVA with no (or one-factor with) repeated measures, Friedman’s test, two-factor (two-way) ANOVA with replication, two-way ANOVA with equal replication, Kruskal-Wallis test]
  • evaluate the association between different variables [covariance, Pearson’s (product moment) correlation coefficient, Spearman’s rank correlation coefficient]
  • determine the relationship (causal influence) between independent and dependent variables [simple (multiple) linear regression]

Teaching Methods

The final grade will be determined by:
20%:
(1) Verbal contribution during the core-module (discussion and questions concerning the analytical methods, making real life suggestions for other artificial examples or related to the data sets offered in the core module…)
(2) Ability to carry out different tests using Excel
80%:
The group assignments will be evaluated according to:

  • the correctness of the realized empirical examples
  • the variety of different used statistical methods
  • the implementation style of the different used statistical methods
  • the usability and reasonability of the research questions asked and answered
  • the information content of the whole report
  • the clarity of interpretation of the results
  • the feasibility of the real life suggestions
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