The basic information and lecture of statistics.

1. Course Code: MAT 202
Course Title: STATISTIC
Class Day: TIME:
Lecture / Week No. 1
Instructor Name:
Department of Faculty of Management Sciences

2. Contents
1. Statistics
2. Applications of Statistics in Business and
Economics
3. Descriptive Statistics
4. Inferential Statistics
5. Data
6. Data Sources

3. Chapter1
Introduction toStatistics

4. Statistics
• Statistics is the discipline that concerns the collection,
organization, analysis, interpretation and presentation of data.
In applying statistics to a scientific, industrial, or social problem,
it is conventional to begin with a statistical population to be
studied.
Reference No.1

5. Applications of Statistics in theState
• For the effective functioning of the State, Statistics is indispensable. Different
department and authorities require various facts and figures on different matters.
They use this data to frame policies and guidelines in order to perform
smoothly.
• Traditionally, people used statistics to collect data pertaining to manpower,
crimes, wealth, income, etc. for the formation of suitable military and fiscal
policies.
• Over the years, with the change in the nature of functions of the State from
maintaining law and order to promoting human welfare, the scope of the
application of statistics has changed too.
• Today, the State authorities collect statistics through their agencies on multiple
aspects like population, agriculture, defense, national income, oceanography,
natural resources, space research, etc.
• Further, nearly all ministries at the Central as well as State level, rely heavily on
statistics for their smooth functioning. Also, the availability of statistical
information enables
the government to frame policies and guidelines to improve the overall working
of the system.

6. Applications of Statistics inEconomics
• Economics is about allocating limited resources among unlimited ends in the most
optimal manner. Statistics offers information to answer some basic questions in
economics –
• What to produce?
• How to produce?
• For whom to produce?
• Statistical information helps to understand the economic problems and formulation of
economic policies. Traditionally, the application of statistics was limited since the
economic theories were based on deductive logic. Also, most statistical techniques were
not developed enough for application in all disciplines.
• However, today, with computers and information technology, statistical data and
advanced techniques of statistical analysis are a boon to many.
• In economics, many scholars have now shifted their stand from deductive logic to
inductive logic in order to explain any economic proposition. This inductive logic requires
the observation of economic behavior of a large number of units. Hence, it needs strong
statistical support in the form of data and techniques.

7. Applications of Statistics inBusiness
According to Chao, “Statistics is a method of decision-
making
in the face of uncertainty on the basis of numerical data
and
calculated risks.” Hence, statistics provides
information to businesses which help them in
making critical decisions.
• Accounting
Public accounting firms use statistical sampling
procedures when conducting audits for their clients.
• Finance
Financial advisors use a variety of statistical
information,
including price-earnings ratios and dividend yields, to
guide their investment recommendations.
• Marketing
Electronic point-of-sale scanners at retail checkout
counters are being used to collect data for a
variety of marketing research applications.

8. Contd…
• Production
A variety of statistical quality control charts are
used to monitor the output of a production
process.
• Economics
Economists use statistical information in making
forecasts about the future of the economy or some
aspect of it.
• Industry
Statistics helps in the field of Quality Control.

9. statistical methods used in dataanalysis
• Two main statistical methods are used in data
analysis:
• descriptive statistics
• Inferential Statistics

10. Descriptive statistics
• Descriptive statistics are graphical representations of data in
tabular, graphical, and numerical methods in order to
summarize data.
• A graphical representation of data is a useful method of analysis.
Examples of this visual representation are histograms, bar
graphs and pie graphs, to name a few. Using these methods,
the data is described by compiling it into a graph, table or other
visual representation.
• This provides a quick method to make comparisons between
different data sets and to spot the smallest and largest values
and trends or changes over a period of time.
Reference No.2

11. Descriptive statistics are most often concerned with two
sets of properties of a distribution (sample or
population):
• 1. central tendency (or location) seeks to
characterize the distribution's central or typical
value, Use the mean or the median to locate the
center of the dataset. This measure tells you where
most values fall.
• 2. dispersion (or variability) characterizes the extent
to which members of the distribution depart from its
center and each other. You can use the range or
standard deviation to measure the dispersion. A low
dispersion indicates that the values cluster more
tightly around the center. Higher dispersion signifies
that data points fall further away from the center. We
can also graph the frequency distribution.
Reference No.2
Descriptive Statistics

12. 91 78 93 57 75 52 99 80 97 62
71 69 72 89 66 75 79 75 72 76
104 74 62 68 97 105 77 65 80 109
85 97 88 68 83 68 71 69 67 74
62 82 98 101 79 105 79 69 62 73
Example: Hudson Auto Repair
The manager of Hudson Auto would like to have
a better understanding of the cost of parts used in the
engine tune-ups performed in the shop. She examines
50 customer invoices for tune-ups. The costs of parts,
rounded to the nearest dollar, are listed below.

13. Examp•
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y
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aa
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id
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Percent Frequencie
s)
Percen
t
Frequenc
y
Parts
Cost
($)
50-59
Frequenc
y 2 4
60-
69
1
3
2
6
1
6
7
7
3
2
1
4
1
4
70-79
80-89
90-99
100-
109
5
1
0
Total
5
0
10
0

14. Example: Hudson AutoRepair
• Graphical Summary
(Histogram)
Parts
Cost
($)
1
6
1
4
1
2
1
0
8
6
4
2
1
8
Frequen
cy
50 60 70 80 90 100 110

15. Example: Hudson AutoRepair
• Numerical Descriptive Statistics
• The most common numerical descriptive statistic is the average (or
mean).
• Hudson’s average cost of parts, based on the 50 tune-ups studied,
is $79 (found by summing the 50 cost values and then dividing by
50).

16. Inferential Statistics
Statistical inference is the process of using data obtained from a small group of elements (the sample)
to make estimates and test hypotheses about the characteristics of a larger group of elements (the
population).
Inferentialstatistics takes datafrom a sample and makes inferences about the larger populationfrom
which the sample was drawn.Becausethe goal of inferentialstatisticsis to drawconclusions from
a sample and generalize them to a population, we need to have confidence that our sample
accuratelyreflectsthe population.This requirementaffectsour process.At a broad level,we must
do thefollowing:
• Define the populationwe are studying.
• Draw arepresentativesample from that population.
• Use analyses thatincorporatethe samplingerror
.
Reference No.3

17. Example: Hudson AutoRepair
• Process of
Statist1i.caPloIpnufleartieonnce
consists of all
tune-ups. Average
cost of parts is
unknown.
2. A sample of 50
engine tune-ups
is examined.
3. The sample data
provide a sample
average cost of
$79 per tune-up.
4. The value of the
sample average is
used
to make an estimate of
the population average.

18. Data and DataSets
• Data are characteristics or information, usually
numerical, that are collected through
observation. In a more technical sense, data
is a set of values of qualitative or quantitative
variables about one or more persons or
objects, while a datum (singular of data) is a
single value of a single variable.
• The data collected in a particular study are
referred to as the data set.

19. Definition of terms
Elements, Variables, andObservations
• The elements are the entities on which data are collected.
• A variable is a characteristic of interest for the elements.
• The set of measurements collected for a particular element is
called an observation.
• The total number of data values in a data set is the
number of elements multiplied by the number of
variables.

20. Data, Data Sets,
Elements, Variables, and Observations
Elements
Variable
s
Company
Stock Annual Earn/
Exchange Sales($M) Sh.($)
AMEX 73.10 0.86
OTC 74.00 1.67
NYSE 365.70 0.86
NYSE 111.40 0.33
Dataram
EnergySouth
Keystone
LandCare
Psychemedic
s
AMEX 17.60 0.13
Data Set Datum

21. Scales ofMeasurement
• Scales of measurement include:
• Nominal
• Ordinal
• Interval
• Ratio
• The scale determines the amount of information contained
in the data.
• The scale indicates the data summarization and statistical
analyses that are most appropriate.

22. Scales ofMeasurement
• Nominal
• Data are labels or names used to identify an
attribute of the element.
• A nonnumeric label or a numeric code may be
used.
• Example:
Students of a university are classified by the school in
which they are enrolled using a nonnumeric label such
as Business, Humanities, Education, and so on.
Alternatively, a numeric code could be used for the
school variable (e.g. 1 denotes Business, 2 denotes
Humanities, 3 denotes Education, and so on).

23. Scales ofMeasurement
• Ordinal
• The data have the properties of nominal data and
the order or rank of the data is meaningful.
• A nonnumeric label or a numeric code may be
used.
• Example:
Students of a university are classified by their class
standing using a nonnumeric label such as
Freshman, Sophomore, Junior, or Senior.
Alternatively, a numeric code could be used for the
class standing variable (e.g. 1 denotes Freshman,
2 denotes Sophomore, and so on).

24. Scales ofMeasurement
• Interval
• The data have the properties of ordinal data and the interval
between observations is expressed in terms of a fixed unit of
measure.
• Interval data are always numeric.
• Example:
Melissa has an SAT score of 1205, while Kevin has an SAT score of 1090.
Melissa scored 115 points more than Kevin.

25. Scales ofMeasurement
• Ratio
• The data have all the properties of interval data and
the ratio of two values is meaningful.
• Variables such as distance, height, weight, and
time use the ratio scale.
.
• Example:
Melissa’s college record shows 36 credit hours earned,
while Kevin’s record shows 72 credit hours earned.
Kevin has twice as many credit hours earned as
Melissa.

26. Qualitative and QuantitativeData
• Data can be further classified as being
qualitative or quantitative.
• The statistical analysis that is appropriate
depends on whether the data for the variable
are qualitative or quantitative.

27. QualitativeData
• Qualitative data are labels or names used to identify an
attribute of each element.
• Qualitative data use either the nominal or ordinal
scale of measurement.
• Qualitative data can be either numeric or nonnumeric.
• The statistical analysis for qualitative data are rather limited.

28. Quantitative Data
• Quantitative data indicate either how many or how much.
• Quantitative data are always numeric.
• Ordinary arithmetic operations are meaningful only with
quantitative data.

29. Cross-Sectional and Time SeriesData
• Cross-sectional data are collected at the
same or approximately the same point in
time.
• Example: data detailing the number of building
permits issued in June 2000 in each of the
counties of Texas
• Time series data are collected over several
time periods.
• Example: data detailing the number of building
permits issued in Travis County, Texas in each of
the last 36 months

30. Types of data based onSources
• Primary data: Data collected by the investigator
himself/ herself for a specific purpose.
• Examples: Data collected by a student for his/her
thesis or research project. ...
• Secondary data: Data collected by someone else for
some other purpose , from existing Sources
• Data needed for a particular application might already exist
within a firm. Detailed information is often kept on
customers, suppliers, and employees for example.
• Substantial amounts of business and economic data are
available from organizations that specialize in collecting
and maintaining data.
• Government agencies are another important source of data.
• Data are also available from a variety of industry
associations and special-interest organizations.
• The Internet has become an important source of data

31. Data Acquisition Considerations
• Time Requirement
• Searching for information can be time consuming.
• Information might no longer be useful by the time
it is available.
• Cost of Acquisition
• Organizations often charge for information even
when it is not their primary business activity.
• Data Errors
• Using any data that happens to be available or
that were acquired with little care can lead to
poor and misleading information.

32. Topic:
Reference No.
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2
3
4
4
4
4
Statistics
Applications of Statistics in Business and
Economics Descriptive Statistics
Inferential
Statistics Data
Elements, Variables, and
Observations Data Types
Data Acquisition Considerations

33. References / Resources
• 1 https://www.toppr.com/guides/business-
economics- cs/descriptive-statistics/application-of-
statistics/
• 2.https://statisticsbyjim.com/basics/descriptive-
inferential- statistics/
• 3. https://statisticsbyjim.com/basics/descriptive-
inferential- statistics/
• 4.https://en.wikipedia.org/wiki/Data
• https://en.wikipedia.org/wiki/Statistics