Basic statistics and regressions

Semester 1 · 29077 · PhD Programme in Management · 0CP · 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.

Teaching Hours: 20
Lab Hours: 0
Mandatory Attendance: Required

Course Topics
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

Teaching format
Frontal lectures with practical in-class computing tutorials

Educational objectives
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.

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

Required readings

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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