Lesson Notes By Weeks and Term v4 - SHS 1
ORGANISING, REPRESENTING AND INTERPRETING DATA
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ORGANISING, REPRESENTING AND INTERPRETING DATA
Download the Lessonotes Mobile Ghana app for faster lesson access on Android and iPhone.
Subject: Additional Mathematics
Class: SHS 1
Term: 2nd Term
Week: 17
Grade code: 1.4.1.LI.8
Strand code: 4
Sub-strand code: 1
Content standard code: 1.4.1.CS.1
Indicator code: 1.4.1.LI.8
Theme: HANDLING DATA
Subtheme: ORGANISING, REPRESENTING AND INTERPRETING DATA
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In our previous lessons, we learned about measures of central tendency (mean, median, mode), which give us a single value to represent the "centre" of a dataset. However, this single value doesn't tell the whole story. For instance, two JHS classes might both have an average BECE mock score of 60, but in one class, most students score between 55 and 65, while in the other, scores range wildly from 20 to 100. Which class is more consistent? To answer this, we need measures of dispersion. This lesson introduces measures of dispersion, which tell us how spread out or varied our data is.
Concept 1: What is Dispersion?
Dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed. It tells us how much the individual data points differ from the average value (like the mean). Low Dispersion: Data points are clustered closely around the mean. This indicates high consistency or predictability. High Dispersion: Data points are spread out over a wider range. This indicates low consistency or high variability.
Example: Consider the scores of two students, Kofi and Ama, in five mathematics tests: Kofi's scores: 68, 70, 70, 71, 71 (Mean = 70) Ama's scores: 50, 60, 70, 80, 90 (Mean = 70)
Both have the same mean score, but Kofi's performance is very consistent (low dispersion), while Ama's is highly variable (high dispersion). Measures of dispersion help us quantify this difference.