Ogives are cumulative histograms that help visualize data distribution, showing how many values lie above or below a specific threshold. The document details steps for plotting both 'less than' and 'more than' type ogives, outlining the importance of ogives in summarizing large datasets and analyzing data characteristics. However, it also notes several limitations, including complications in preparation and minimal descriptive power regarding central tendencies or distributions.

2. OGIVES

4. Cumulative histogram also
known as ogives , are graph
that can be used to determine
how many data values lie above
or below a particular value in a
data set .

6. OGIVE

7. How to plot an ogive curve?

8. Following steps are necessary to plot
less than type ogive curve.
1. Start from the upper limit of the
class intervals and then add class
frequency to the cumulative
frequency distribution.
2. Take upper in the x-axis direction
cumulative frequencies along the y-
axis direction.

9. 3.Plot the points ( x , y ) , where
‘x’ is the upper limit of the class
and ‘y’ is the corresponding
cumulative frequency.
4.Join the points by a smooth
curve.

10. Following steps are necessary to plot a more than
type ogive curve ;
1. Starts from the lower limit of the class intervals
and total frequency is subtracted from the
frequency to get the cumulative frequency
distribution .
2. In the graph , consider the lower limit x - axis
direction and cumulative frequencies along y -
axis direction .

11. 3. Plot the points x, y, where ‘x’ is the upper
limit of the class and y is the
corresponding cumulative frequency.
4. Joins the points by a smooth curve.

13. Ogive can
•Visually approximate.
•Summarize a large data set in
visual form.

14. Delineate each interval in the
frequency distribution.
Clarify rates of change between
classes better than other graph.
Provide visual check of
accuracy or reasonableness
of calculations.

15. •Be easily understood due to
widespread use in business and
media.
•Show the number of proportion
or of data point above / below
a particular value.
•Become more smooth as data
points or classes added.

17. Ogive can:
•Fail to reflect all data points in a data set.
•Be somewhat complicated to prepare.
•Reveal little about central
tendency, dispersion , skew
or kurtosis.

18. • Often requires additional written or verbal
explanation.
•Be inadequate to describe to attribute,
behaviour, or condition of
interest.
•Fail to reveal key assumptions.

20. For the data given below , construct a
less than cumulative frequency table
and plot its ogive .
MAR
KS
0-10 10-
20
20-
30
30-
40
40-
50
50-
60
60-
70
70-
80
80-
90
90-
100
FREQ
UENC
Y
3 5 6 7 8 9 10 12 6 4

22. MARKS FREQUENCY LESS THAN
CUMULATIVE
FREQUENCY
0-10 3 3
10-20 5 8
20-30 6 14
30-40 7 21
40-50 8 29
50-60 9 38
60-70 10 48
70-80 12 60
80-90 6 66
90-100 4 70

24. MA
RKS
0-5 5-10 10
-15
15-
20
20-
25
25-
30
30-
35
35-
40
40-
45
45-
50
FRE
QUE
NCY
3 5 7 8 10 11 14 19 15 13
For the data give below, construct
more than cumulative frequency
and plot its ogive

26. MARKS FREQUE
NCY
CUMULATIVE
FREQUENCY
0-5 3 95
5-10 5 95 - 3 = 92
10-15 7 92 - 5 = 87
15-20 8 87 - 7 = 80
20-25 10 80 - 8 = 72
25-30 11 72 - 10 = 62
30-35 14 62 - 11 = 51
35-40 19 51 - 14 = 37
40-45 15 37 - 19 = 18
45-50 13 18 - 15 = 3

28. https://www.statisticshowto.data
sciencecentral.com/ogive-graph
/https://www.statisticshowto.dat
asciencecentral.com/ogive-graph/
http://www.mathcaptain.com/sta
tistics/ogive.html
www.preciousheart.net/chaplain
cy/Auditor_Manual/11grphd.pdf