This document contains comprehensive notes from Lecture 15 of the CO2412 course on Computational Thinking. The lecture focuses on the importance of organizing and presenting data graphically, which is essential for making raw data more interpretable and actionable. The notes cover various technique...
Organizing and Presenting Data Graphically
1. Importance of Data Organization
o Raw data is often difficult to interpret and analyze directly,
necessitating the need for proper organization into tables or graphs.
o Techniques covered include bar charts, pie charts, ordered arrays,
stem-and-leaf displays, frequency distributions, histograms,
polygons, contingency tables, and scatter diagrams.
Variables in Data
1. Definition of a Variable
o A variable is a characteristic that can change over time or vary
between individuals or objects.
o Examples: Hair color, white blood cell count, or the time to failure
of a computer component.
2. Types of Variables
o Quantitative: Variables that are numeric and can be measured.
o Qualitative: Variables that describe qualities or categories.
o Discrete: Variables that take specific values.
o Continuous: Variables that can take any value within a range.
Tables and Charts for Categorical Data
1. Categorical Data
o Categorical data represents categories or groups.
o Common graphical representations include pie charts and bar
charts.
2. Graphing Categorical Data
o Pie Charts: Show the percentage distribution of categories.
o Bar Charts: Represent the frequency or percentage of categories
through the height of bars.
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