https://github.com/lmizner/codecademy_product_defects

Practicing rules of probability, set theory, and distributions
https://github.com/lmizner/codecademy_product_defects

cdf jupyter-notebook numpy pmf ppf python rvs scipy-stats

Last synced: 13 days ago
JSON representation

Practicing rules of probability, set theory, and distributions

• Host: GitHub
• URL: https://github.com/lmizner/codecademy_product_defects
• Owner: lmizner
• Created: 2022-10-08T03:27:56.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-10-08T03:29:21.000Z (over 2 years ago)
• Last Synced: 2024-12-15T10:14:00.273Z (2 months ago)
• Topics: cdf, jupyter-notebook, numpy, pmf, ppf, python, rvs, scipy-stats
• Language: Jupyter Notebook
• Homepage:
• Size: 4.88 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# codecademy_product_defects

### Detecting Product Defects with Probability

You are in charge of monitoring the number of defective products from a specific factory. You’ve been told that the number of defects on a given day follows the Poisson distribution with the rate parameter (lambda) equal to 7. You’re new here, so you want to get a feel for what it means to follow the Poisson(7) distribution. You remember that the Poisson distribution is special because the rate parameter represents the expected value of the distribution, so in this case, the expected value of the Poisson(7) distribution is 7 defects per day.

You will investigate certain attributes of the Poisson(7) distribution to get an intuition for how many defective objects you should expect to see in a given amount of time. You will also practice and apply what you know about the Poisson distribution on a practice data set that you will simulate yourself.