Content, progress and pedagogy of the module

The module builds on the modules 'Applied mathematics for business economists' and 'Applied statistics for business economists'. The module guides the student from descriptive statistics to statistical inference, where the student becomes able to perform business economic analyses by means of statistical models, hereunder conducting of hypothesis tests and regression analyses using relevant software. Students will work with real-world datasets and learn to leverage Large Language Models (LLMs) as analytical tools throughout their investigative process. This includes: selecting a statistical model, processing and cleaning data for the model, testing compliance with the assumptions behind the use of the model, critical interpretation of results and predictions based on the model, and utilizing LLMs to enhance data exploration, hypothesis generation, and result interpretation.

Learning objectives

Knowledge

The objective is that the student after the module possesses the necessary knowledge on:

• probability theory and hypothesis testing, including empirical and theoretical probability distribution.
• various statistical methods and models, including assumptions which must be met in order to apply these.
• the application of statistics in business economic and industrial context, including an interpretation of the results from statistical models.
• the capabilities and limitations of LLMs in quantitative analysis, including appropriate use cases and potential biases.
• strategies for working with real-world, unstructured data including data cleaning, validation, and preprocessing techniques.

Skills

The objective is that the student after the module possesses the necessary skills in:

• applying hypothesis testing in connection with analysis of business economic data using relevant software.
• argueing in favour of the choice of statistical model for implementation of a given business economic analysis, including conducting practical tests to fulfil the conditions (including the Gauss Markov Assumptions) for the application of the model.
• applying a given statistical model, including an interpretation of the results and predictions based on the model.
• effectively utilizing LLMs to support data exploration, generate analytical insights, and assist in model selection and interpretation.
• cleaning, transforming, and preparing messy datasets for statistical analysis.
• critically evaluating and validating LLM-generated suggestions and interpretations in the context of quantitative analysis.

Competences

The objective is that the student after the module possesses the necessary competences in:

• evaluating the applicability of statistics and quantitative methods within business economics.
• independently conducting a critical interpretation and evaluation of own or others' application of quantitative methods within business economics, including in scientific literature.
• communicating the results of a statistical analysis to persons with no specific statistical knowledge.
• integrating traditional statistical methods with modern AI-assisted analytical approaches while maintaining methodological rigor.
• adapting analytical approaches when working with imperfect, incomplete, or complex real-world data scenarios.

Type of instruction

For information see §17.

Exam

Exams