Difference between revisions of "Category:Graphs, charts, diagrams"

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Graphs are to visualize quantitative data and relationship between variables using a system of coordinates. They are powerful in getting the message across, but the same data can be displayed in many ways, with a variety of visual effects. Examples include line graphs, histograms, and bar graphs. These graphical tools help us to see magnitude, trends, differences and similarities in the data. They are a key aspect in scientific communication for any audience. There is no general advice about when it is appropriate to use a graph rather than a table. Graphs offer the opportunity to show more data, and thus are most suited for data that cannot be easily displayed in a table <Ref name="Altman">Altman DG. Practical statistics for medical research. London: Chapman & Hall; 1991. p. 43.</ref>. This is often the case when there is a trend or comparison to be shown <Ref>McLennan W. 1331.0 Statistics - a powerful edge! 2nd ed. Australian Bureau of Statistics; 1998. p. 103.</ref>. Some displays, such as histograms, are in essence graphical <Ref name="Altman"/>
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Graphical displays should:
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* show the data
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* induce the viewer to think about the substance rather than about methodology, graphic design, the technology of graphic production, or something else
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* avoid distorting what the data is telling
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* present many numbers in a small place
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* make large data sets coherent
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* encourage the eye to compare different pieces of data
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* reveal the data at several levels of detail, from a broad overview to the fine structure
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* serve a reasonably clear purpose: descriptions, exploration, tabulation, or decoration
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* be closely integrated with the statistical and verbal descriptions of the data set <Ref>Tufte ER. The visual display of quantitative information. 2nd ed. Connecticut: Graphics Press; 2009. p. 13</ref>
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In this part of the FEMWIKI, the use of [[line graphs]], [[histograms]], [[frequency polygons]], [[bar graphs]], [[Pie charts|pie graphs]], and [[other types of data display]] are discussed.
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=References=
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<References/>
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==FEM PAGE CONTRIBUTORS 2007==
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;Editor
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:Agnes Hajdu
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;Original Author
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:Alain Moren
 +
;Contributors
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:Lisa Lazareck
 +
:Maarten Hoek
 +
:Agnes Hajdu
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[[Category:Informing Action / Improving Knowledge]]
 
[[Category:Informing Action / Improving Knowledge]]

Latest revision as of 08:11, 29 March 2023

Graphs are to visualize quantitative data and relationship between variables using a system of coordinates. They are powerful in getting the message across, but the same data can be displayed in many ways, with a variety of visual effects. Examples include line graphs, histograms, and bar graphs. These graphical tools help us to see magnitude, trends, differences and similarities in the data. They are a key aspect in scientific communication for any audience. There is no general advice about when it is appropriate to use a graph rather than a table. Graphs offer the opportunity to show more data, and thus are most suited for data that cannot be easily displayed in a table [1]. This is often the case when there is a trend or comparison to be shown [2]. Some displays, such as histograms, are in essence graphical [1]

Graphical displays should:

  • show the data
  • induce the viewer to think about the substance rather than about methodology, graphic design, the technology of graphic production, or something else
  • avoid distorting what the data is telling
  • present many numbers in a small place
  • make large data sets coherent
  • encourage the eye to compare different pieces of data
  • reveal the data at several levels of detail, from a broad overview to the fine structure
  • serve a reasonably clear purpose: descriptions, exploration, tabulation, or decoration
  • be closely integrated with the statistical and verbal descriptions of the data set [3]

In this part of the FEMWIKI, the use of line graphs, histograms, frequency polygons, bar graphs, pie graphs, and other types of data display are discussed.

References

  1. Jump up to: 1.0 1.1 Altman DG. Practical statistics for medical research. London: Chapman & Hall; 1991. p. 43.
  2. Jump up McLennan W. 1331.0 Statistics - a powerful edge! 2nd ed. Australian Bureau of Statistics; 1998. p. 103.
  3. Jump up Tufte ER. The visual display of quantitative information. 2nd ed. Connecticut: Graphics Press; 2009. p. 13

FEM PAGE CONTRIBUTORS 2007

Editor
Agnes Hajdu
Original Author
Alain Moren
Contributors
Lisa Lazareck
Maarten Hoek
Agnes Hajdu

Pages in category "Graphs, charts, diagrams"

The following 6 pages are in this category, out of 6 total.