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 daily lives in Ghana, we are surrounded by data. We hear about the average rainfall, the average price of plantain in Madina market, or the average score in a WASSCE subject. These "averages" (measures of central tendency) tell us about the centre of the data, but they don't tell the whole story. For example, two classes might have the same average test score of 65%. But in one class, all students scored between 60% and 70%, while in the other, scores ranged from 20% to 100%. The data is spread out very differently! This lesson introduces Measures of Dispersion, which are statistical tools that help us understand and describe this "spread" or "variability" in a set of data.
Part 1: What is Dispersion?
Dispersion is a measure of how spread out or scattered a set of data is from its average value. Low Dispersion: The data points are clustered closely together around the mean or median. This indicates consistency or similarity. High Dispersion: The data points are spread far apart. This indicates a wide variation.
Think about two basket weavers. They both weave an average of 10 baskets a day. Weaver A: Weaves 9, 10, 11, 10, 10 baskets over 5 days. (Low dispersion) Weaver B: Weaves 5, 15, 12, 3, 15 baskets over 5 days. (High dispersion) Although their average is the same, Weaver A is far more consistent. Measures of dispersion help us quantify this difference. Part 2: Measures of Dispersion for Ungrouped (Raw) Data
These are used when you have a list of individual data points.