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Introduction to Graphs
Page 2


Introduction to Graphs
Gra ph
The graphs are the visual
representation of the
data collected in
numerical form so that it
could be easily
understood. Graphs are
generally used for
comparisons between
two or more data.
Page 3


Introduction to Graphs
Gra ph
The graphs are the visual
representation of the
data collected in
numerical form so that it
could be easily
understood. Graphs are
generally used for
comparisons between
two or more data.
Side-by-side bar gRAPH
Side-by-side bar graphs
compare two or more
categories across
different groups or
conditions. They make it
easier to identify
patterns, differences, and
relationships between the
groups.
Fish Ca ts Do gs
0
5
1 0
15
20
Page 4


Introduction to Graphs
Gra ph
The graphs are the visual
representation of the
data collected in
numerical form so that it
could be easily
understood. Graphs are
generally used for
comparisons between
two or more data.
Side-by-side bar gRAPH
Side-by-side bar graphs
compare two or more
categories across
different groups or
conditions. They make it
easier to identify
patterns, differences, and
relationships between the
groups.
Fish Ca ts Do gs
0
5
1 0
15
20
Pie Chart
Pie charts are useful for
displaying the relative
proportions or
percentages of different
categories within a
dataset. They are
effective in showing the
composition of a whole.
Page 5


Introduction to Graphs
Gra ph
The graphs are the visual
representation of the
data collected in
numerical form so that it
could be easily
understood. Graphs are
generally used for
comparisons between
two or more data.
Side-by-side bar gRAPH
Side-by-side bar graphs
compare two or more
categories across
different groups or
conditions. They make it
easier to identify
patterns, differences, and
relationships between the
groups.
Fish Ca ts Do gs
0
5
1 0
15
20
Pie Chart
Pie charts are useful for
displaying the relative
proportions or
percentages of different
categories within a
dataset. They are
effective in showing the
composition of a whole.
Hi sto gr am
Histograms are best used
to visualise the frequency
distribution of continuous
data. They are
particularly useful for
identifying patterns and
distributions within
datasets.
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