Chapter 13
Project 4.1: Visual Analysis Techniques
When doing exploratory data analysis (EDA), one common practice is to use graphical techniques to help understand the nature of data distribution. The US National Institute of Standards and Technology (NIST) has an Engineering Statistics Handbook that strongly emphasizes the need for graphic techniques. See https://doi.org/10.18434/M32189.
This chapter will create some additional Jupyter notebooks to present a few techniques for displaying univariate and multivariate distributions.
In this chapter, we’ll focus on some important skills for creating diagrams for the cleaned data:
Additional Jupyter Notebook techniques
Using PyPlot to present data
Unit testing for Jupyter Notebook functions
This chapter has one project, to build the start of a more complete analysis notebook. A notebook can be saved and exported as a PDF file, allowing an analyst to share preliminary results for early conversations. In the next chapter, we...