Summary
In this chapter, we discussed installing and setting up the Python environment to run the Statsmodels API and other requisite open-source packages. We also discussed populations versus samples and the requirements to gain inference from samples. Finally, we explained several different common sampling methods used in statistical and machine learning models.
In the next chapter, we will begin a discussion on statistical distributions and their implications for building statistical models. In Chapter 3, Hypothesis Testing, we will begin discussing hypothesis testing in depth, expanding on the concepts discussed in the Observational study section of this chapter. We will also discuss power analysis, which is a useful tool for determining the sample size based on existing sample data parameters and the desired levels of statistical significance.
