Contents:
Classification and tabulation of data can be arranged pictorially to enable fast and easy reading.
Tally Charts: Data may be recorded by this means and also be presented in this way. The data shown is also known as raw data.
Example: Children in year 1:
17
Children in year 2:
13
Children in year 3:
19
Children in year 4:
15
Pictograms: Pictorial or diagrammatical data represented by a pictorial symbol.
Example: Approximate number of people who live in selected cities are:
London: 6million.... Paris: 2
million.... Berlin 3million.... Rome 2
million.... Moscow 8million.
This data may be presented as a pictogram:
Bar Charts: A diagram of columns or bars, the height of the bars determine the value of the particular data in question.
Example of a single bar chart:
When there are two sets of similar information they can be contrasted by displaying both sets on the same graph.
Here a different school may have differing numbers of school children at the same years given. Their numbers are contrasted by using a different shading.
This contrasting of information can slso be shown using sections.
Example of a sectional bar chart, showing the same information as above:
Pie Charts: Are another way of displaying but some limited knowledge of circles and degrees are necessary.
Example: using school children from school 1.
Total number of children = 64.
Total degrees in a circle 360
.
Therefore 17 children = 96
and 13 children = 73
and 19 children = 107
and 15 children = 84
Line Graph: When the quantity is a continuous variable ie. time or temperature.
Data is plotted as a continuous line.
Example: The maximum temperature over a week was;
67
3 on Monday;
65
63
69
65
Histograms: A histogram is a special sort of bar chart. The successive groups of data is linked in a definite numerical order.
Example: a frequancy table was constructed to show the ages of the parents (38) from the year 3 class.
Continuous data histogram; from the above frequency table:
Ages: 20-24 25-29 30-34 35+
Frequency: 6 12 16 4
Histograms may be constructed with unequal widths for each separate data. The area the block takes up is the measure rather than the height. (The frequency density is the frequency of the class interval
width of class interval). Notice that the width includes half a unit extra below and above. Using the data above but changing the boundaries to continuous data:
Ages: 20-22 23-25 26-27 28-30 31-32 33-38 39+
Frequency: 3 3 8 4 6 10 4
Example: Group 20-22, class interval 3, class width 3.
Unequal width histogram from the above frequency table:
Frequency Polygons: Are better than histograms for comparing more than one set of data. Note the centre (intersection) is the value in the centre of the range ie. 20-22 at 21. Using the frequency density from the above histogram for school 1 and school 2 below:
Ages: 20-22 23-25 26-27 28-30 31-32 33-38 39+
School 1 Density: 1 1 4 1
3 2 4
School 2 Density: 1 2 3 1 4 1 3
Frequency polygon for each set of data:
To see Graph click : (26kb).
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