Vorlesung
2 SemesterwochenstundenBTU-Vorlesungsverzeichnis ↗- Dienstag06 Okt 2026 bis 26 Jan 202709:15 bis 10:45
- Dienstag06 Okt 2026 bis 26 Jan 202715:30 bis 17:00
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14493Fakultät 5 – Wirtschaft, Recht und Gesellschaft
KI-unterstützte Statistik: Datenanalyse mit ChatGPT & Co
Titel auf Englisch: AI-Assisted Statistics: Exploring Data with ChatGPT & Co
Lernergebnisse
After successfully completing the module, students are able to apply model-based estimation approaches to empirical research questions in business and social science contexts. They can translate substantive theoretical arguments into suitable empirical models, use AI-supported tools for implementation in a controlled and reflective way, and independently assess whether the resulting estimations are appropriate for the research question. Students are able to interpret estimation results beyond a purely technical reading of output tables, evaluate the assumptions and limitations of different modelling choices, and distinguish between descriptive, predictive and causal interpretations. They develop the ability to critically examine potential sources of bias, uncertainty and misspecification, and to communicate empirical findings, including their limitations, in a transparent and responsible manner. The course will also foster critical thinking, enabling students to reflect on the opportunities and limitations of using AI in research, as well as the ethical implications.
Inhalte
- Model-based estimation in empirical research: research questions, theory, data-generating processes, model specification and interpretation, incl. structural models versus reduced-form models
- AI-assisted empirical workflow: using AI for coding, debugging, model implementation, documentation and interpretation checks; limits of AI-generated code and explanations
- OLS beyond simple linear regression: transformations, nonlinear terms, interaction effects, moderation, dummy variables and categorical predictors
- Interpretation of OLS results: coefficients, predicted values, simple slopes, confidence intervals, standard errors and model diagnostics
- Generalized linear models: logit/probit models, count models, link functions, predicted probabilities and average marginal effects
- Structural equation models: path models, latent variables, measurement models, confirmatory factor analysis, mediation, model fit and comparison of alternative models
- Endogeneity and identification (omitted variables, reverse causality, simultaneity, selection problems and unobserved heterogeneity) and strategies for dealing with endogeneity: theoretically justified control variables, instrumental variables, control-function logic, instrument-free correction approaches and sensitivity analyses
- Exploring limitations of estimations: assumptions, robustness checks, outliers, missing data, multicollinearity, model dependence, generalizability and ethical risks
- Communication of results (model equations, estimation tables, marginal-effect plots) and transparent reporting of assumptions and limitations
- Hands-on applications with individualized datasets and AI-supported implementation, applications based on student ddata set, Marketing Mix Attribution models, and success factor research in management
Voraussetzungen
Verpflichtende Voraussetzungen
Keine
Empfohlene Voraussetzungen
- Basic knowledge of statistics, ideally including descriptive statistics, statistical inference and simple linear regression.
- Prior experience with empirical research questions and basic data handling is helpful, but not required.
- Programming knowledge is not required, but students must be willing to work with AI-assisted statistical software workflows, which includes generating programming code.
Prüfungsmodalitäten
- Part 0 (data collection): Participation in a survey, 30 minutes; contributes 5 points to the total score.
- Part 1 (take home): Individual take-home homework with individualised data, pass/fail; passing the homework contributes 10 points to the total score.
- Part 2 (bring own laptop): Individual in-class practice session with individualized data and online submission of results and answers to questions, 60 minutes, 35 points.
- Part 3 (bring own brain and pen): Written exam, consisting of multiple-choice and free-text questions, 50 minutes, 50 points.
Termine im WiSe 26/27
Angeboten in
- Alle StudiengängeAlle Abschlüsse
Wahlpflichtkomplex: Fachübergreifendes Studium
| Studiengang | Abschluss | Wahlpflichtkomplex |
|---|---|---|
| Alle Studiengänge | Alle Abschlüsse | Fachübergreifendes Studium |
Literatur und Materialien
Lecture materials, datasets, and analysis scripts will be provided via Moodle. Literature references will be announced at the beginning of the course.
Hinweise
Module is continuous assessment, hence, register or deregister in teh university exmaniation system before the deadline announced both via the university semester schedule and the Moodle course pages. You cannot register or deregister and we cannot register or deregister you after that deadline
Bewertungen von Studierenden
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