Hello all, This presentation includes the Definition, Functions, Limitations, and Applications of Statistics. Additionally, It consists of Classification of Data, its types and methods of collection of data. Graph Covered:- Bar Graph, Histogram, Frequency curve, Ogives, and Pie Chart.

1. STATISTICS 103
.

2. • Statistics is a form of mathematical analysis that
uses quantified models, representations and
synopses for a given set of experimental data or
real-life studies.
• Statistics studies methodologies to gather, review,
analyse and draw conclusions from data.
WHATISSTATISTICS
a branch of mathematics dealing
with the collection, analysis,
interpretation, and presentation
of masses of numerical data
.
page 2

3. FunctionsofStatistics
Understanding
off nature
It helps in providing a
better understanding
and exact description
of a phenomenon of
nature.
Planning
It helps in the proper
and efficient planning
of a statistical inquiry
in any field of study..
Presenting Data
It helps in presenting
complex data in a
suitable tabular,
diagrammatic and
graphic form for easy
and clear
comprehension of the
data.
Collecting Data
Statistics helps in
collecting appropriate
quantitative data.
Drawing
Inferences
It helps in drawing
valid inferences,
along with a measure
of their reliability
about the population
parameters from the
sample data.
page 3

4. LIMITATIONSOF
STATISTICS
Accuracy
If sufficient care is not exercised in
collecting, analysing and
interpreting the data, statistical
results might be misleading.
Need Expert
Only a person who has an
expert knowledge of
statistics can handle
statistical data efficiently.
Aggregates of
Facts
Statistics are aggregates of facts, so
a single observation is not a statistic.
Statistics deal with groups and
aggregates only.
Limitation in
Data
Statistics cannot be
applied to
heterogeneous data.
page 4

5. Methodstocollectdatainstatistics
Information Age, data is no longer scarce – it’s overpowering.
Simple Surveys In- person
Interviews
Experiments
page 5
Focus
Groups
Observational
data
collections
methods

6. ApplicationsofStatistics
The scope of statistics is confined to two main aspects – the classification and application of statistics.
• Actuarial science is the
discipline that applies
mathematical and statistical
methods to assess risk in the
insurance and finance
industries.
• Environmental statistics is
the application of statistical
methods to environmental
science.Weather, climate, air
and water quality are
included, as are studies of
plant and animal populations.
• Machine learning is the
subfield of computer science
that formulates algorithms in
order to make predictions
from data.
• Business statistics is a
specialty area of statistics
which are applied in the
business setting. It can be
used for quality assurance,
financial analysis, production
and operations, and many
other business areas.
page 6

7. Typesof StatisticalData
1. Numerical Data
These data have meaning as a measurement, such as a
person’s height, weight, IQ, or blood pressure; or they’re
a count
Numerical data can be further broken into two types:
discrete and continuous.
• Discrete data represent items that can be counted;
they take on possible values that can be listed out.The
list of possible values may be fixed (also called finite);
or it may go from 0, 1, 2, on to infinity (making
it countably infinite).
• Continuous data represent measurements; their
possible values cannot be counted and can only be
described using intervals on the real number line.
2. Categorical Data
Categorical data represent characteristics such as a
person’s gender, marital status, hometown, or the types
of movies they like. Categorical data can take on
numerical values
page 7
3. Ordinal
Data mixes numerical and categorical data.The data fall
into categories, but the numbers placed on the
categories have meaning.

8. ClassificationofData
Geographical classification
• When data are classified on
the basis of location or
areas, it is called
geographical classification
• Example: Classification of
production of food grains in
different states in India.
Quantitative classification
• Quantitative classification
refers to the classification of
data according to some
characteristics, which can be
measured such as height,
weight, income, profits etc.
Qualitative classification
• In Qualitative classification,
data are classified on the
basis of some attributes or
quality such as sex, colour of
hair, literacy and religion. In
this type of classification,
the attribute under study
cannot be measured. It can
only be found out whether it
is present or absent in the
units of study.
page 8

9. BarGraph
page 9
X f
10-15 6
15-20 11
20-05 9
25-30 7
30-35 5
35-40 2
0
2
4
6
8
10
12
10 _15 15_20 20_25 25_30 30_35 35_40
Frequency
Frequency

10. X Lower
Limit
f
10-15 10 6
15-20 15 11
20-05 20 9
25-30 25 7
30-35 30 5
35-40 35 2
page 10
HistogramandFrequencyCurve

11. X f Lower
Limit
Upper
Limit
CF
(less
than
ogives)
10-15 6 10 15 6
15-20 11 15 20 17
20-05 9 20 25 26
25-30 7 25 30 33
30-35 5 30 35 38
35-40 2 40 40
page 11
Ogives
0
5
10
15
20
25
30
35
40
45
10 15 20 25 30 35 40
ChartTitle
less than CF (less than)

12. Piechart
20
100
∗ 360 = 720
2018-19
20
100
∗ 360 = 72°
2017-18
40
100
∗ 360 = 144°
2019-20
page 12
Sales
1st Qtr 2nd Qtr 3rd Qtr
Years %
2017-18 20
2018-19 40
2029-20 20

13. THANK
YOU
Aayush Namdev
aayushnamdev20000@gmail.com