E2PythonAdvancedAbout 150 min to complete
Probability Distributions
Descriptive statistics summarise the data you have. Probability distributions are mathematical models for the data you might get — they describe how likely each possible value is.
Notebook
About 150 minutes to complete
Enrol to read the notebook and download it to run.
What this module covers
Descriptive statistics summarise the data you have. Probability distributions are mathematical models for the data you might get — they describe how likely each possible value is.
By the end of it
- Explain random variables and the difference between discrete and continuous distributions
- Read a PMF/PDF (how likely) and a CDF (cumulative), and use the PPF (quantiles/inverse CDF)
- Work with key discrete distributions — Bernoulli, Binomial, Poisson — and continuous ones — Uniform, Normal, Exponential
- Use the scipy.stats interface (.pdf, .cdf, .ppf, .rvs, .mean, .std) fluently
- Apply the normal distribution and the 68–95–99.7 rule; convert to/from z-scores
- Demonstrate the Central Limit Theorem by simulation and explain why it makes the normal distribution ubiquitous
- Check whether data is approximately normal with a histogram overlay and a Q–Q plot, and fit a distribution to data