Statistical inference for numerical data
In this section, we will switch to look at statistical inference using numerical data. We will cover two approaches. The first approach relies on the bootstrapping procedure and permutes the original dataset to create additional artificial datasets, which can then be used to derive the confidence intervals. The second approach uses a theoretical assumption on the distribution of the bootstrapped samples and relies on the t-distribution to achieve the same result. We will learn how to perform a t-test, derive a confidence interval, and conduct an analysis of variance (ANOVA).
As discussed earlier, bootstrapping is a non-parametric resampling method that allows us to estimate the sampling distribution of a particular statistic, such as the mean, median, or proportion, as in the previous section. This is achieved by repeatedly drawing random samples with replacement from the original data. By doing so, we can calculate confidence intervals and...
