STATLAB Academy has expanded again. Module 7: Continuous Probability Distributions is now available, extending the probability pathway developed across Modules 5 and 6.

This new module helps learners move from counting discrete outcomes to modelling measurements such as waiting times, service durations, product weights, processing times, and equipment lifetimes.

Students explore:

• continuous random variables;
• probability density functions and cumulative distribution functions;
• uniform distributions;
• normal and standard normal distributions;
• z-scores and percentiles;
• inverse normal calculations;
• normal approximations to binomial and Poisson models;
• continuity correction;
• exponential waiting-time models;
• mean time between events and failures;
• warranty and reliability decisions;
• optional triangular distributions for business scenario analysis.

The module combines clear explanations with interactive probability visualizations, original business examples, quizzes, synthetic datasets, live R exercises, and an applied capstone. Learners can calculate probabilities, explore shaded areas under distribution curves, compare exact and approximate results, and interpret statistical output in practical language.

Module 7 uses R functions such as:

pnorm()
qnorm()
rnorm()
punif()
qunif()
runif()
pexp()
qexp()
rexp()

Together, Modules 5, 6, and 7 create a connected probability-learning sequence:

Module 5 introduces probability rules and reasoning.
Module 6 applies those foundations to discrete probability distributions.
Module 7 extends the analysis to continuous distributions and waiting-time models.

The source curriculum for Module 7 covers continuous variables, uniform and normal distributions, standardization, normal approximations, exponential waiting times, and triangular what-if models.

Visit STATLAB Academy, create an account, and begin learning:

More modules, interactive tools, datasets, and R Labs will continue to be added gradually.