This document provides an overview of different types of charts for visualizing data, including pie charts, bar charts, histograms, step charts, and ogives. It discusses when each chart is most appropriate based on the type of data (nominal, ordinal, discrete metric, continuous metric). For each chart type, it provides examples and discusses how to properly construct and interpret the charts. It also covers concepts like bimodal distributions and the normal distribution as they relate to interpreting chart shapes.

1. Statistics “Three”
Mohamed Ahmed Hefny, MD.

2. Strong people do not
put others down …
they lift them up
Michael P. Watson

3. Describing data with charts

4. Learning objectives
1. Choose the most appropriate chart for a given data
type.
2. Draw pie charts; and simple, clustered and stacked,
bar charts.
3. Draw histograms.
4. Draw step charts and ogives.
5. Draw time series charts.
6. Interpret and explain what a chart reveals.

5. Charting nominal and ordinal data

6. The pie chart
Each segment (slice) of a pie chart should be proportional to the
frequency of the category it represents.
• Disadvantage of a pie chart is that it can only represent one
variable (A separate pie chart for each variable).
• Moreover a pie chart can lose clarity if it is used to represent more
than four or five categories.
Pie chart: Prevalence of eye
color, percentage by eye color
34%
52%
14%
Brown Blue Green

7. The simple bar chart
• An alternative to the pie chart for nominal data.
• Frequency on the vertical axis and category on the horizontal axis.
• The simple bar chart is appropriate if only one variable is to be
shown.
• Bars should all be the same width and spaces between bars.
• These spaces emphasize the categorical nature of the data.
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Brown Blue Green

8. The clustered bar chart
If you have more than one group you can use the clustered bar chart.
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Brown Blue Green
Boys Girls

9. The stacked bar chart
• Appropriate if you want to compare the total number of subjects in
each group (total number of boys and girls for example),
• Somehow inappropriate if you want to compare category sizes
between groups, e.g. Brown eyes in girls with brown eyes in boys.
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Boys Girls
Brown Blue Green

10. Charting discrete metric data

11. Bar charts can be used to graph discrete metric data in
the same way as with ordinal data
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Bar chart used to represent discrete metric data on numbers of measles cases
in 10 schools

12. The histogram
• A continuous metric variable can take a very large number of values,
so it is usually impractical to plot them without first grouping the
values.
• The grouped data is plotted using a frequency histogram, which has
frequency plotted on the vertical axis and group size on the
horizontal axis.
• A histogram looks like a bar chart but without any gaps between
adjacent bars (continuous nature of variable).
• If the groups in the frequency table are all of the same width, then
the bars in the histogram will also all be of the same width.
• One limitation of the histogram is that it can represent only one
variable at a time (like the pie chart), and this can make comparisons
between two histograms difficult, because, if you try to plot more
than one histogram on the same axes, invariably parts of one chart
will overlap the other.

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Histogram of the grouped birth weight data

14. The cumulative frequency curve or ogive
With continuous metric data, there is assumed to be a smooth continuum of
values, so you can chart cumulative frequency with a correspondingly smooth
curve, known as a cumulative frequency curve , or ogive.
The ogive can be very useful if you want to estimate the cumulative frequency
for any value on the horizontal axis, which is not one of the original group
values.
For example, suppose you want to know what percentage of infants had a
birthweight of 3600g or less. By drawing a line vertically upwards from a value
of 3600 g on the horizontal axis to the ogive, and then horizontally to the
vertical axis, you can see that about 76 per cent of the infants weighed 3600 g
or less. You can of course ask such questions in reverse. (See next chart)
The orgive can represent one or several variables

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2700-2999 3000-3299 3300-3599 3600-3899 3900-4199 4200-4499
%ofCumulativeFrequency
% of Cumulative frequency
The relative cumulative frequency curve (or ogive) for the
percentage cumulative birth weight

16. Charting time-based data – the time series chart
If the data you have collected are from measurements made at regular intervals
of time (minutes, weeks, years, etc.), you can present the data with a time
series chart. Usually these charts are used with metric data, but may also be
appropriate for ordinal data. Time is always plotted on the horizontal axis, and
data values on the vertical axis.
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InfectionRate
Year (10 years interval)
Females
Male

17. Pie Chart Bar Chart Histogram (if
grouped)
Step Chart Ogive
Nominal Yes Yes No No No
Ordinal No Yes No Yes (Cumulative) No
Metric Discrete No Yes Yes Yes (Cumulative) Yes (Cumulative)
Metric
Continuous
No No Yes No Yes (Cumulative)
Choosing an appropriate chart

18. Symmetric or mound-shaped distributions
In this type you can see that the distribution is reasonably symmetric and mound
shaped, and has only one peak.
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19. Bimodal distributions
A bimodal distribution is one with two distinct humps. These are less common
than the shapes described previously, and are sometimes the result of two
separate distributions, which have not been disentangled.
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20. Normal-ness
There is one particular symmetric bell-shaped distribution, known as the
Normal distribution, which has a special place in the heart of statisticians.
Many human clinical features are distributed normally, and the Normal
distribution has a very important role to play.
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21. Thank You