About this module
We can now read distributions as models rather than decorative curves.
Counts, positive amounts, waiting times, and simulated outcomes each have different support and assumptions.
Our goal is not to memorize a catalogue of names, but to understand which mechanism a distribution represents and where it can fail.
Lessons
- Lesson 61
PMF, PDF, CDF, Survival, and Quantile Functions
Turning a loss model into probabilities and thresholds
2:38 - Lesson 62
Bernoulli and Binomial Distributions
Counting failures across a fixed number of comparable attempts
2:37 - Lesson 63
Poisson Distribution and Event Counts
Modeling event counts over a stated exposure interval
2:35 - Lesson 64
Uniform Distribution and Random Sampling
Scaling random draws for simulation without claiming uniform market behavior
2:43 - Lesson 65
Normal Distribution and Standard Normal
Standardizing a processing-time model and reading density correctly
2:34 - Lesson 66
Lognormal Distribution and Positive Quantities
Modeling positive amounts in log space
2:37 - Lesson 67
Student-t Distribution and Heavy Tails
Allowing heavier tails without assuming all moments exist
2:38 - Lesson 68
Exponential, Gamma, and Weibull Waiting-Time Models
Waiting for an event, several stages, or an aging failure process
2:36 - Lesson 69
Mixture Distributions, Multimodality, and Fat Tails
Separating customer groups instead of forcing one average distribution
2:41 - Lesson 70
Random Sampling and Monte Carlo Intuition
Estimating a quantity by simulation and checking the uncertainty
2:45