Summary

In this chapter, we covered different types of statistical inferences for hypothesis testing, targeting both numerical and categorical data. We introduced inference methods for a single variable, two variables, and multiple variables, using either proportion (for categorical variable) or mean (for numerical variable) as the sample statistic. The hypothesis testing procedure, including both the parametric approach using model-based approximation and the non-parametric approach using bootstrap-based simulations, offers valuable tools such as the confidence interval and p-value. These tools allow us to make a decision about whether we can reject the null hypothesis in favor of the alternative hypothesis. Such a decision also relates to the Type I and Type II errors.

In the next chapter, we will cover one of the most widely used statistical and ML models: linear regression.