
IB Math AI SL 4.10 – Spearman’s Correlation Rank & Pearson’s Product Moment
This lesson introduces students to two key measures of association between variables: Spearman’s Rank Correlation Coefficient (rₛ) and Pearson’s Product-Moment Correlation Coefficient ®. Students learn that Spearman’s rank correlation measures how well a monotonic relationship (not necessarily linear) fits the data, making it useful for ranked or non-linear datasets, while Pearson’s correlation measures the strength and direction of a linear relationship between two continuous variables.
Visuals illustrate cases of positive, negative, and zero correlation, helping students interpret numerical correlation values from -1 to +1. Through guided examples and real-world datasets, such as comparing students’ math and reading scores, students learn how to rank data, compute Spearman’s rₛ, and interpret the strength and direction of relationships. The lesson also discusses the limitations of both coefficients, emphasizing the impact of outliers and the importance of using each method appropriately depending on data type and structure.
Fully aligned with IB Math AI SL Topic 4.10 – Spearman’s Correlation Rank and Pearson’s Product Moment, this slide deck builds analytical skills for statistical interpretation and lays the groundwork for further correlation and regression analysis.
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