Further reading
The following are some resources that you can use to become more familiar with Jupyter:
- Jupyter Notebook Basics: https://nbviewer.jupyter.org/github/jupyter/notebook/blob/master/docs/source/examples/Notebook/Notebook%20Basics.ipynb
- JupyterLab introduction: https://blog.jupyter.org/jupyterlab-is-ready-for-users-5a6f039b8906
- Learning Markdown to make your Jupyter Notebooks presentation-ready: https://medium.com/ibm-data-science-experience/markdown-for-jupyter-notebooks-cheatsheet-386c05aeebed
- 28 Jupyter Notebook Tips, Tricks, and Shortcuts: https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/
Some resources for learning more advanced concepts of statistics (that we won't cover here) and carefully applying them are as follows:
- A Gentle Introduction to Normality Tests in Python: https://machinelearningmastery.com/a-gentle-introduction-to-normality-tests-in-python/
- How Hypothesis Tests Work: Confidence Intervals and Confidence Levels: https://statisticsbyjim.com/hypothesis-testing/hypothesis-tests-confidence-intervals-levels/
- Intro to Inferential Statistics (Making Predictions with Data) on Udacity: https://www.udacity.com/course/intro-to-inferential-statistics--ud201
- Lesson 4: Confidence Intervals (Penn State Elementary Statistics): https://online.stat.psu.edu/stat200/lesson/4
- Seeing Theory: A visual introduction to probability and statistics: https://seeing-theory.brown.edu/index.html
- Statistics Done Wrong: The Woefully Complete Guide by Alex Reinhart: https://www.statisticsdonewrong.com/
- Survey Sampling Methods: https://stattrek.com/survey-research/sampling-methods.aspx
