Decision Making Statistics

MS04-001-G

Undergraduate course providing the fondamental mathematical intuition to understand, describe, and analyse data in finance, economics, and management.

Welcome

This course introduces inferential statistics for 2nd-year business school students. It covers the mathematical foundations needed to move from descriptive statistics to data-driven decision making under uncertainty.

Course Structure

The course is organized in 15 sessions across three main parts:

# Session Topics Covered
1 Sets & Combinatorics Sets, combinatorial analysis
2 Probability Calculation of probabilities
3 Discrete Random Variables Discrete distributions
4 Continuous Random Variables Continuous distributions
5 Practical Work β€” Distributions Applying distributions in Excel
6 Data Description Numerical summaries, reminders
7 Estimation Confidence intervals
8 Conformity Tests Hypothesis testing β€” conformity
9 Comparison Tests Hypothesis testing β€” comparison
10 Practical Work β€” Inference Inference in Excel
11 ANOVA Test Analysis of variance
12 Chi-Square Test Goodness-of-fit, independence
13 Linear Correlation Test Correlation coefficient test
14 Practical Work β€” Tests Advanced tests in Excel
15 Mock Exam Revision & mock exam

Part I β€” Probability Foundations (1–5) Β· Part II β€” Statistical Inference (6–10) Β· Part III β€” Advanced Tests (11–15).

Learning Objectives

By the end of this course, students will be able to:

  1. Apply combinatorial analysis and probability rules to real-world problems
  2. Work with discrete and continuous probability distributions (Binomial, Poisson, Normal)
  3. Compute and interpret confidence intervals for means and proportions
  4. Formulate, perform, and interpret statistical hypothesis tests
  5. Choose the appropriate test for a given business problem
  6. Use Excel to automate statistical computations

Course Materials

Each session folder contains:

  • Animation material β€” step-by-step lecture slides
  • Summary β€” key formulas and concepts
  • Exercises β€” problems to practice during and after the session
  • Answered exercises β€” complete solutions