BiteSize Statistics for Business: Hypothesis Testing for Two Samples is the second course in the BiteSize Stats for Business specialization. It extends single-sample inference to the comparisons that drive most real business decisions: does group A differ from group B — in a mean, a proportion, a category, or across more than two groups at once?

Across five modules, learners progress from two-sample Z- and t-tests for independent groups (including full A/B testing pipelines), through the paired t-test for before/after and matched designs, to the two-proportion Z-test, the chi-square test of independence with Cramér's V, and finally one-way ANOVA with Tukey's HSD post-hoc test. Every core lesson pairs a focused reading with a hands-on interactive notebook built around a realistic business scenario, and every module includes a fully worked demo case study and one or more ungraded practice projects applying the module's tools to a new scenario.

BiteSize Statistics for Business: Correlation and Regression Analysis is the third and final course in the BiteSize Stats for Business specialization. It moves from testing whether two groups differ to modeling how two or more variables relate — quantifying association, fitting a predictive line, and rigorously checking whether that line can be trusted.

Across five modules, learners progress from scatterplots and the Pearson correlation coefficient (with significance testing and common pitfalls), through simple linear regression and OLS estimation, to inference for the regression slope, model-fit diagnostics (R², residual plots, influential points), and finally multiple regression with multicollinearity checks. Every core lesson pairs a focused reading with a hands-on interactive notebook built around a realistic business scenario, and every module includes a fully worked demo case study and one or more student-led practice projects applying the module's tools to a new dataset.