Module Overview
An outline of the module's learning topics covering probability fundamentals, sample spaces, theoretical versus experimental probability, and core probability rules, used by instructors to understand the module's scope and by learners to preview what will be studied.
Readings & Interactive Content
EGN3443 Module 3 – Probability Distributions
A landing page that links learners to the Module 3 PowerPoint slides and YouTube lecture video on probability distributions, serving as a navigation hub for accessing supplementary instructional resources.
A reading page covering foundational probability concepts including axioms, rules, sample spaces, events, and related theorems, used by learners as the primary reference text for understanding and reviewing core probability theory for the module.
An interactive dart-throwing simulation in which learners apply normal distribution properties to assign five throwers (each with a known μ and σ for radial miss distance) to five scored targets, practicing expected-value calculations and normal distribution reasoning to maximize their team's total score.
An interactive simulation in which learners practice optimal-stopping (secretary-problem) decision-making by accepting or rejecting random monetary offers ($1–$100) across three lines of ten strangers, competing against a computer using the same lines, to build intuition about probability, sequential decision-making, and stopping rules.
Topic Detail Readings
This reading introduces probability as a formal system for quantifying uncertainty, explaining its foundational concepts and relevance to engineering, data science, and everyday decision-making; learners use it to build conceptual grounding before tackling calculations.
This reading defines sample spaces and events as the mathematical foundation of probability, explaining how to identify all possible outcomes and structure them before assigning likelihoods; learners use it to set up problems correctly before applying any probability rules.
Theoretical vs. Experimental Probability
This reading contrasts theoretical probability (based on logical reasoning and equally likely outcomes) with experimental probability (based on observed data and relative frequency), helping learners understand when and how to apply each approach.
This reading covers the core rules of probability—including addition, multiplication, and complement rules—explaining how to combine and calculate probabilities correctly; learners use it as a reference for solving multi-event probability problems.
Interpreting and Applying Probability in Context
This reading focuses on interpreting and communicating probability values in real-world contexts, moving beyond calculation to explain what a probability means and how to apply it in decision-making; learners and instructors use it to connect mathematical results to practical reasoning.
Reference Files
Thirty Strangers – Interactive Probability Game
Thirty Strangers is a self-contained interactive browser game in which players repeatedly decide whether to 'take' or 'pass' on strangers encountered across multiple lines, competing against an AI opponent under a scoring system. The game simulates repeated probabilistic decision-making across many rounds, producing experimental outcomes that accumulate over gameplay. It includes configurable parameters such as number of lines, people per line, and AI difficulty thresholds.
The Oche – Probability Dart-Throwing Simulation
The Oche is an interactive, browser-based simulation in which learners assign dart throwers to boards and observe probability outcomes through repeated virtual throws. The tool models skill distributions and generates experimental trial data visually, allowing users to compare expected versus observed results. It includes roster management, board assignment mechanics, and real-time outcome tracking.
Assessment – Quiz
A 45-question quiz bank containing applied probability problems set in engineering contexts such as defect rates, resistor tolerances, and component selection, used by instructors to assess learners' ability to calculate probabilities including binomial and combinatorial scenarios.
Assignments
A programming assignment in which learners write a Python or R Monte Carlo simulation to estimate the probability of drawing exactly 21 from a three-card hand using a standard deck, applying computational methods to reinforce probability concepts.
A graded discussion assignment in which learners use an AI tool to generate and evaluate examples of statistically independent, mutually exclusive, and dependent events, then post and peer-review responses to deepen conceptual understanding of event relationships.