Lesson Notes By Weeks and Term v4 - SHS 1
STATISTICAL REASONING AND ITS APPLICATION IN REAL LIFE
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STATISTICAL REASONING AND ITS APPLICATION IN REAL LIFE
Download the Lessonotes Mobile Ghana app for faster lesson access on Android and iPhone.
Subject: Mathematics
Class: SHS 1
Term: 2nd Term
Week: 14
Grade code: 1.4.3.LI.2
Strand code: 4
Sub-strand code: 1
Content standard code: 1.4.1.CS.2
Indicator code: 1.4.3.LI.2
Theme: MAKING SENSE OF AND USING DATA
Subtheme: STATISTICAL REASONING AND ITS APPLICATION IN REAL LIFE
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In our daily lives in Ghana, we are constantly surrounded by data. From the prices of foodstuffs at Makola Market to the performance of the Black Stars, and even our own class test scores, data helps us understand the world. Statistics is the tool we use to collect, organise, analyse, and interpret this data to make informed decisions. This lesson focuses on "measures of central tendency" (mean, median, and mode), which are single values that attempt to describe the centre of a set of data. We will revise these concepts from JHS and then learn how to apply them to more complex, grouped data, which is essential for analysing large amounts of information.
This topic builds on your JHS knowledge and extends it to handle larger datasets. Part A: Revision of Measures of Central Tendency for Ungrouped Data (JHS Recap) Ungrouped data is a list of individual values. Mean (or Arithmetic Average): The sum of all values divided by the number of values. It is sensitive to extreme values (outliers). Median: The middle value of a dataset that has been arranged in order of magnitude (either ascending or descending). If there are two middle values, the median is their average. It is not affected by outliers. Mode: The value that appears most frequently in a dataset. A dataset can have one mode (unimodal), two modes (bimodal), or more than two (multimodal). It is the only measure that can be used for non-numerical (categorical) data.