Basic statistics and regressions

Semester 1 · 29077 · Doktoratsstudium in Management · 0KP · EN

This course introduces core statistical methods with a focus on inference and regression modeling, tailored to applications in management and business decision-making. Students learn how to estimate, test, and model relationships using data, with practical implementation in R.

Vorlesungsstunden: 20
Laboratoriumsstunden: 0
Anwesenheitpflicht: Required

Themen der Lehrveranstaltung
Part I: Statistical Inference 1. Sampling Distributions and the Logic of Inference 2. Confidence Intervals 3. Hypothesis Testing Part II: Regression Modeling 4. Simple and Multiple Linear Regression 5. Statistical Inference in Regression 6. Extenting the linear regression model

Unterrichtsform
Frontal lectures with practical in-class computing tutorials

Bildungsziele
The first part covers statistical inference (estimation, confidence intervals, hypothesis testing); the second focuses on linear regression techniques for analyzing economic and managerial data. The course equips students with the tools to conduct empirical research and supports further study in econometrics and data-driven management.

Art der Prüfung
Assessment is based on two short data analysis projects. The first focuses on statistical inference; the second applies linear regression to a business dataset.

Pflichtliteratur

Lecture slides and R computing handouts. In addition selected readings for following texbooks will be assigned in class:

Hogg, R. V., Tanis, E. A., & Zimmerman, D. L. (2019). Probability and Statistical Inference (10th ed.).

Pearson.James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R (2nd ed.). Springer. Available free online: https://www.statle

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