Applied Statistics for E and B : Data and Statistics

Course in University about Applied Statistics for economics and business

1. APPLIED STATISTICS FOR
ECONOMICS & BUSINESS
Trang, Ha Thi Thu (Ph.D)
Department of Business Administration
School of Economics and Management (SEM)
Hanoi University of Science and Technology (HUST)
1

2. 
Instructor’s name: Ha ThiThu Trang (Ph.D)

Academic degrees: PhD in Methods & Models for Economics and Finance
(Curriculum in Economic Statistics)

Research Interest:

Spatial Econometrics, Spatial Economics

Economic Development: Environment and Transportation Analysis

Computable General Equilibrium Model, Input-Output Model

Office hours: upon appointment (Lecturer Room: C9 –208)

Email: [email protected]
2

3. 3

4. 1.CourseDescription

Prerequisite:No

Numberofcredits:04credits

Languageofinstruction:English

RequirementforlaptopinstalledstatisticalsoftwareSPSS,Excel.2.CourseObjectives

Themaingoalofthiscourseistointroducethestudentstoachievethebasicknowledge
ofstatisticsanddatapresentation.

Bycompletingthiscourse,thestudentwill:

HaveabasicunderstandingofstatisticsindoingdataanalysisusingSPSS/Excel.

Acquireskillsofmanipulatingandperformingaccuratecalculationsondata.

Identifythevarioustypesofdataanddescribetheseusingappropriatestatistics.

Analyzedataandinterprettheoutputofstatisticalmodeltoanswerquestionsandsolvethe
problems
4

5. Assessment Goals Due Date Weight
Attendance Score During the course10% Individual
Individual AssignmentWeek 5 20% Individual
Group AssignmentWeek 10 20% Group work
Final Exam Examination Period50% Individual
5

6. Textbook

Anderson, David R., Dennis J. Sweeney, Thomas A. Williams,
Jeffrey D. Camm, James J. Cochran (2014), Statistics for
Business and Economics 12th, South-Western Cengage
Learning, USA. Microsoft Excel and (add-ins) Data Analysis
References

Newbold, Paul, William L. Carlson & Betty M. Thorne (2013),
Statistics for Business and Economics, 8th edition, Pearson
Education, USA.

Hoàng Trọng và Chu Nguyễn Mộng Ngọc (2011), Thống kê
ứng dụng trong kinh tế-xã hội, NXB Lao động -Xã hội
6

7. Part 1
•Chapter 1: Data and Statistics
•Chapter 2: Descriptive Statistics: Tabular and Graphical Presentation
•Chapter3: Descriptive Statistics: Numeric measurement
Part 2
•Chapter 4+5+6: Probability Distribution
•Chapter 7: Sampling Distribution
Part 3
•Chapter 8+9: Estimation and Hypothesis Testing
•Chapter 10: Inference about population means and variances
•Chapter 13: Analysis of Variance
•Chapter 14: Simple Linear Regression
7

8. APPLIED STATISTICS FOR
ECONOMICS & BUSINESS
Trang, Ha Thi Thu (Ph.D)
Department of Business Administration
School of Economics and Management (SEM)
Hanoi University of Science and Technology (HUST)
8

9. 9

10. 10
1.1. Applications in Business and Economics
1.2. Data
1.3. Data Sources
1.4. Descriptive Statistics
1.5. Statistical Inference

11. 
Accounting
Publicaccountingfirmsusestatisticalsampling
procedureswhenconductingauditsfortheirclients.

Finance
Financialadvisorsuseavarietyofstatisticalinformation,
includingprice-earningsratiosanddividendyields,to
guidetheirinvestmentrecommendations.

Marketing
Electronicpoint-of-salescannersatretailcheckout
countersarebeingusedtocollectdataforavarietyof
marketingresearchapplications.

Production
Avarietyofstatisticalqualitycontrolchartsareusedto
monitortheoutputofaproductionprocess.

Economics
Economistsusestatisticalinformationinmakingforecasts
aboutthefutureoftheeconomyorsomeaspectofit.
11

12. 12

13. 
Elements, Variables, and Observations

Scales of Measurement

Qualitative and Quantitative Data

Cross-Sectional and Time Series Data
13

14. 
“Data”comesfromLatinVerb“dare”–“togive”.

Dataarethosepiecesofinformationthatanyparticularsituation
givestoanobserver

Dataaremeasurementsorobservationsthatarecollectedasasource
ofinformation.

Dataarethefactsandfiguresthatarecollected,summarized,
analyzed,andinterpreted.

Thedatacollectedinaparticularstudyarereferredtoasthedata
set.
14

15. 
Theelementsaretheentitieson
whichdataarecollected.

Avariableisacharacteristicof
interestfortheelements.

Thesetofmeasurementscollected
foraparticularelementiscalledan
observation.

Thetotalnumberofdatavaluesina
datasetisthenumberofelements
multipliedbythenumberof
variables.
15

16. 
Scalesofmeasurementinclude:

Nominal

Ordinal

Interval

Ratio

Thescaledeterminestheamountofinformationcontainedinthe
data.

Thescaleindicatesthedatasummarizationandstatisticalanalyses
thataremostappropriate.
16

17. 
Nominal

Dataarelabelsornamesusedtoidentifyanattributeoftheelement.

Anonnumericlabeloranumericcodemaybeused.

Example:
o
Studentsatauniversityareclassifiedbytheschoolinwhichtheyare
enrolledusinganonnumericlabelsuchasBusiness,Humanities,
Education,andsoon.
o
Alternatively,anumericcodecouldbeusedfortheschoolvariable:
•
1denotesBusiness,
•
2denotesHumanities,
•
3denotesEducation,andsoon
17

18. 
Ordinal

Thedatahavethepropertiesofnominaldataandtheorderorrankofthe
dataismeaningful.

Anonnumericlabeloranumericcodemaybeused.

Example:
o
Studentsofauniversityareclassifiedbytheirclassstandingusinga
nonnumericlabelsuchasFreshman,Sophomore,Junior,orSenior.
o
Alternatively,anumericcodecouldbeusedfortheclassstanding
variable
•
1denotesFreshman,
•
2denotesSophomore,andsoon).
18

19. 
Interval

Thedatahavethepropertiesofordinaldataandtheintervalbetween
observationsisexpressedintermsofafixedunitofmeasure.

Intervaldataarealwaysnumeric.

Example:
o
MelissahasanSATscoreof1205,whileKevinhasanSATscoreof1090.
Melissascored115pointsmorethanKevin.
19

20. 
Ratio

Thedatahaveallthepropertiesofintervaldataandtheratiooftwo
valuesismeaningful.

Variablessuchasdistance,height,weight,andtimeusetheratioscale.

Thisscalemustcontainazerovaluethatindicatesthatnothingexistsfor
thevariableatthezeropoint.

Example:
o
Melissa’scollegerecordshows36credithoursearned,whileKevin’s
recordshows72credithoursearned.Kevinhastwiceasmanycredit
hoursearnedasMelissa.
20

21. Numerical dataNominal data
ageincome
5575 000
4268 000
..
..
personmarried
1 yes
2 no
3 no
. .
..
computerbrand
1IBM
2Dell
3Compaq
4IBM
..
IBM Dell Compaq othertotal
25118650
50% 22%16%12%100%
With nominal data, all we
can calculate is the
proportion of data that
falls into each category.
exam gradeHD
D
C
P
F
Ordinal data
Food quality
Excellent
Good
Satisfactory
Poor
With ordinal data, all we
can use is computations
involving the ordering
process.
21

22. Which type of data is it?

Sex (Male/Female/Gay/Lesbian)

Eye color (Blue/Brown/Dark brown/Black)

Religion (Hinduism, Buddhism,Islam,Confucianism, Christianity)

Luxury Brands (Gucci, Dior, Prada, LV, Dolce…)

Academic grades (A, B, C)

Clothing size (small, medium, large, extra large)

Attitudes (strongly agree, agree, disagree, strongly disagree).
22

23. 23
Which type of data is?

24. 24

25. 
Datacanbefurtherclassifiedasbeingqualitativeorquantitative.

Thestatisticalanalysisthatisappropriatedependsonwhetherthe
dataforthevariablearequalitativeorquantitative.

Ingeneral,therearemorealternativesforstatisticalanalysiswhen
thedataarequantitative.
25

26. 
Qualitativedataarelabelsornamesusedtoidentifyanattributeof
eachelement.

Qualitativedatauseeitherthenominalorordinalscaleof
measurement.

Qualitativedatacanbeeithernumericornonnumeric.

Thestatisticalanalysisforqualitativedataareratherlimited.
26

27. 
Quantitativedataindicateeitherhowmanyorhowmuch.

Quantitativedatathatmeasurehowmanyarediscrete.

Quantitativedatathatmeasurehowmucharecontinuousbecause
thereisnoseparationbetweenthepossiblevaluesforthedata.

Quantitativedataarealwaysnumeric.

Ordinaryarithmeticoperationsaremeaningfulonlywithquantitative
data.
27

28. 28

29. 
Cross-sectional data are collected
at the same or approximately the
same point in time. 
Time series data are collected over
several time periods.
29

30. 30

31. 31

32. 32

33. 
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.

Internet

The Internet has become an important source of data.

Most government agencies, like the Bureau of the Census (www.census.gov), make their
data available through a web site.

More and more companies are creating web sites and providing public access to them.

A number of companies now specialize in making information available over the Internet.
33

34. Statistical Studies

Statistical studies can be classified as either experimental or observational.

In experimental studies, the variables of interest are first identified. Then one or
more factors are controlled so that data can be obtained about how the factors
influence the variables.

In observational (nonexperimental) studies, no attempt is made to control or
influence the variables of interest.

A survey is perhaps the most common type of observational study.
34

35. DATA ACQUISITION
CONSIDERATIONS
35
•Searching for information can be time
consuming.
•Information might no longer be useful by
the time it is available.
Time Requirement
•Organizations often charge for information
even when it is not their primary business
activity.
Cost of Acquisition
•Using any data that happens to be available
or that were acquired with little care can
lead to poor and misleading information.
Data Errors

36. 
Statistics is defined as the art and science of collecting, analyzing,
presenting, and interpreting data.

In this course, I emphasize the use of statistics for business data
analysis

Two major branches:

Descriptive statistics

Inferential statistics.
36

37. 
Descriptive statistics are the tabular, graphical, and numerical methods used to
summarize data.
37

38. 
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 are listed below:
38

39. 39
Tabular Summary
(Frequencies and Percent Frequencies)
Graphical Summary
(Histogram)

40. 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).
40

41. Statisticalinferenceisthe
processofusingdata
obtainedfromasmall
groupofelements(the
sample)tomakeestimates
andtesthypothesesabout
thecharacteristicsofa
largergroupofelements
(thepopulation).
41

42. 
Apopulationisthecollectionofall
outcomes,responses,measurements,or
countsthatareofinterest.

Asampleisasubset,orpart,ofa
population.

Tocollectunbiaseddata,aresearcher
mustensurethatthesampleis
representativeofthepopulation.
42

43. Example:

In a recent survey, 1500 adults in the United States were asked if they thought there
was solid evidence of global warming. 855 of the adults said yes.

Identify the population and the sample.
43

44. Example:

In a recent survey, 1500 adults in the United States were asked if they thought there
was solid evidence of global warming. 855 of the adults said yes.

Identify the population and the sample.
44
Solution:
Population:theresponsesofalladultsinthe
UnitedStates
Sample:theresponsesofthe1500adultsinthe
UnitedStatesinthesurvey.Thesampledataset
consistsof855yes’sand645no’s.

45. ThemanagerofHudsonAutowouldliketohaveabetter
understandingofthecostofpartsusedintheenginetune-ups
performedintheshop.
Sheexamines50customerinvoicesfortune-ups.Thecostsof
partsarelistedbelow:
45

46. 
Process of Statistical Inference
1. Population
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.
46

47. SELF-TEST
TheU.S.DepartmentofEnergyprovidesfuel
economyinformationforavarietyofmotor
vehicles.Asampleof10automobilesisshown
inTable1.6(FuelEconomywebsite,February
22,2008).Datashowthesizeoftheautomobile
(compact,midsize,orlarge),thenumberof
cylindersintheengine,thecitydrivingmiles
pergallon,thehighwaydrivingmilesper
gallon,andtherecommendedfuel(diesel,
premium,orregular).
a.Howmanyelementsareinthisdataset?
b.Howmanyvariablesareinthisdataset?
c.Whichvariablesarecategoricalandwhich
variablesarequantitative?
d.Whattypeofmeasurementscaleisusedfor
eachofthevariables?
47

48. 48

49. 49