39
1 Copyright © 2010 HJ Shanghai Normal Uni. Copyright © 2010 HJ Shanghai Normal Uni. Slides Prepared by Slides Prepared by Jing Huang Jing Huang Shanghai Normal Univers Shanghai Normal Univers

Slides Prepared by Jing Huang Shanghai Normal University

  • Upload
    nakia

  • View
    59

  • Download
    0

Embed Size (px)

DESCRIPTION

Slides Prepared by Jing Huang Shanghai Normal University. General Information. Instructor: Ph. D. Jing Huang ( 黄静 ) E-mail: [email protected] Tel.: 13816658610 Public Email: [email protected] PW: shnu2011. Course Objectives. - PowerPoint PPT Presentation

Citation preview

Page 1: Slides Prepared by Jing Huang Shanghai Normal University

1 1

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Slides Prepared bySlides Prepared byJing HuangJing Huang

Shanghai Normal UniversityShanghai Normal University

Page 2: Slides Prepared by Jing Huang Shanghai Normal University

2 2

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

General InformationGeneral Information Instructor: Ph. D. Jing Huang (Instructor: Ph. D. Jing Huang ( 黄静黄静 )) E-mail: [email protected]: [email protected] Tel.:Tel.: 13816658610 13816658610

Public Email: Public Email: [email protected]@163.com PW: shnu2011 PW: shnu2011

Course ObjectivesCourse Objectives This course covers a variety of topics in the This course covers a variety of topics in the theorytheory and and methodmethod

of statistics for Business and Economics, including arguments of statistics for Business and Economics, including arguments for purpose of statistical decision making, etc. It aims at not for purpose of statistical decision making, etc. It aims at not only giving undergraduates a brief textbook analysis of only giving undergraduates a brief textbook analysis of statistical issues, but also providing you with vigorous training statistical issues, but also providing you with vigorous training on research in objective appraisal and effective refinement of on research in objective appraisal and effective refinement of statistical data. statistical data.

Page 3: Slides Prepared by Jing Huang Shanghai Normal University

3 3

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Applications in Applications in Business and EconomicsBusiness and Economics

AccountingAccounting

Public accounting firms use statistical sampling Public accounting firms use statistical sampling procedures when conducting audits for their procedures when conducting audits for their clients.clients.

FinanceFinance

Financial analysts use a variety of statistical Financial analysts use a variety of statistical information, including price-earnings ratios and information, including price-earnings ratios and dividend yields, to guide their investment dividend yields, to guide their investment recommendations.recommendations.

MarketingMarketing

Electronic point-of-sale scanners at retail checkout Electronic point-of-sale scanners at retail checkout counters are being used to collect data for a counters are being used to collect data for a variety of marketing research applications.variety of marketing research applications.

Page 4: Slides Prepared by Jing Huang Shanghai Normal University

4 4

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

ProductionProduction

A variety of statistical quality control charts A variety of statistical quality control charts are used to monitor the output of a production are used to monitor the output of a production process.process.

EconomicsEconomics

Economists use statistical information in Economists use statistical information in making forecasts about the future of the making forecasts about the future of the economy or some aspect of it.economy or some aspect of it.

Applications in Applications in Business and EconomicsBusiness and Economics

Page 5: Slides Prepared by Jing Huang Shanghai Normal University

5 5

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Distribution of GradingDistribution of Grading Item Weight (%)Item Weight (%) Assignments 15Assignments 15 Attendance 15Attendance 15 Final Exam 70Final Exam 70

Page 6: Slides Prepared by Jing Huang Shanghai Normal University

6 6

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Chapter 1Chapter 1 Data and Statistics Data and Statistics

DataData Data SourcesData Sources Descriptive StatisticsDescriptive Statistics Statistical InferenceStatistical Inference

Page 7: Slides Prepared by Jing Huang Shanghai Normal University

7 7

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

DataData

Elements, Variables, and ObservationsElements, Variables, and Observations Scales of MeasurementScales of Measurement Qualitative and Quantitative DataQualitative and Quantitative Data Cross-Sectional and Time Series DataCross-Sectional and Time Series Data

Page 8: Slides Prepared by Jing Huang Shanghai Normal University

8 8

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Data and Data SetsData and Data Sets

DataData (数据)(数据) are the facts and figures that are are the facts and figures that are collected, summarized, analyzed, and collected, summarized, analyzed, and interpreted.interpreted.

The data collected in a particular study are The data collected in a particular study are referred to as the referred to as the data setdata set. . (数据集)(数据集)

Page 9: Slides Prepared by Jing Huang Shanghai Normal University

9 9

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Page 10: Slides Prepared by Jing Huang Shanghai Normal University

10 10

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Elements, Variables, and ObservationsElements, Variables, and Observations

The The elementselements (元素)(元素) are the entities on are the entities on which data are collected.which data are collected.

A A variablevariable (变量)(变量) is a characteristic of is a characteristic of interest for the elements.interest for the elements.

The set of measurements collected for a The set of measurements collected for a particular element is called an particular element is called an observation observation ((观测值)观测值) ..

The total number of data values in a data set The total number of data values in a data set is the number of elements multiplied by the is the number of elements multiplied by the number of variables.number of variables.

Page 11: Slides Prepared by Jing Huang Shanghai Normal University

11 11

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Data, Data Sets, Data, Data Sets, Elements, Variables, and ObservationsElements, Variables, and Observations

ElementElementss

VariableVariabless

Data SetData Set DatumDatum

ObservatioObservationn

StockStock Annual Earn/ Annual Earn/

CompanyCompany Exchange Sales($M) Sh. Exchange Sales($M) Sh.($)($)

DataramDataram AMEXAMEX 73.1073.10 0.86 0.86

EnergySouthEnergySouth OTC OTC 74.0074.00 1.67 1.67

KeystoneKeystone NYSE NYSE 365.70 365.70 0.86 0.86

LandCareLandCare NYSE NYSE 111.40 111.40 0.330.33

PsychemedicsPsychemedics AMEXAMEX 17.6017.60 0.13 0.13

Page 12: Slides Prepared by Jing Huang Shanghai Normal University

12 12

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Scales of MeasurementScales of Measurement

Scales of measurementScales of measurement include: include:

• NominalNominal (名义尺度)(名义尺度)• OrdinalOrdinal (序数尺度)(序数尺度)• IntervalInterval (区间尺度)(区间尺度)• RatioRatio (比例尺度)(比例尺度)

The scale determines the amount of The scale determines the amount of information contained in the data.information contained in the data.

The scale indicates the data summarization The scale indicates the data summarization and statistical analyses that are most and statistical analyses that are most appropriate.appropriate.

Page 13: Slides Prepared by Jing Huang Shanghai Normal University

13 13

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Scales of MeasurementScales of Measurement

NominalNominal

• Data are Data are labels or nameslabels or names used to identify an used to identify an attribute of the element.attribute of the element.

• A A nonnumeric labelnonnumeric label or a or a numeric codenumeric code may may be used.be used.

Page 14: Slides Prepared by Jing Huang Shanghai Normal University

14 14

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Scales of MeasurementScales of Measurement

NominalNominal

• Example:Example:

Students of a university are classified by Students of a university are classified by the school in which they are enrolled the school in which they are enrolled using a nonnumeric label such as using a nonnumeric label such as Business, Humanities, Education, and so Business, Humanities, Education, and so on.on.

Alternatively, a numeric code could be Alternatively, a numeric code could be used for the school variable (e.g. 1 used for the school variable (e.g. 1 denotes Business, 2 denotes Humanities, denotes Business, 2 denotes Humanities, 3 denotes Education, and so on).3 denotes Education, and so on).

Page 15: Slides Prepared by Jing Huang Shanghai Normal University

15 15

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Scales of MeasurementScales of Measurement

OrdinalOrdinal

• The data have the properties of nominal The data have the properties of nominal data and the data and the order or rank of the data is order or rank of the data is meaningfulmeaningful..

• A A nonnumeric labelnonnumeric label or a or a numeric codenumeric code may may be used.be used.

Page 16: Slides Prepared by Jing Huang Shanghai Normal University

16 16

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Scales of MeasurementScales of Measurement

OrdinalOrdinal

• Example:Example:

Students of a university are classified by Students of a university are classified by their class standing using a nonnumeric their class standing using a nonnumeric label such as Freshman, Sophomore, label such as Freshman, Sophomore, Junior, or Senior.Junior, or Senior.

Alternatively, a numeric code could be Alternatively, a numeric code could be used for the class standing variable (e.g. used for the class standing variable (e.g. 1 denotes Freshman, 2 denotes 1 denotes Freshman, 2 denotes Sophomore, and so on).Sophomore, and so on).

Page 17: Slides Prepared by Jing Huang Shanghai Normal University

17 17

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Scales of MeasurementScales of Measurement

Interval Interval (区间尺度)(区间尺度)• The data have the properties of ordinal data The data have the properties of ordinal data

and the interval between observations is and the interval between observations is expressed in terms of a fixed unit of expressed in terms of a fixed unit of measure.measure.

• Interval data are Interval data are always numericalways numeric..

Page 18: Slides Prepared by Jing Huang Shanghai Normal University

18 18

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Scales of MeasurementScales of Measurement

IntervalInterval

• Example:Example:

Melissa has an SAT score of 1205, while Melissa has an SAT score of 1205, while Kevin has an SAT score of 1090. Melissa Kevin has an SAT score of 1090. Melissa scored 115 points more than Kevin.scored 115 points more than Kevin.

Page 19: Slides Prepared by Jing Huang Shanghai Normal University

19 19

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Scales of MeasurementScales of Measurement

RatioRatio ((比例尺度比例尺度))• The data have all the properties of interval The data have all the properties of interval

data and the ratio of two values is data and the ratio of two values is meaningful.meaningful.

• Variables such as distance, height, weight, Variables such as distance, height, weight, and time use the ratio scale.and time use the ratio scale.

• This This scale must contain a zero valuescale must contain a zero value that that indicates that nothing exists for the variable indicates that nothing exists for the variable at the zero point.at the zero point.

Page 20: Slides Prepared by Jing Huang Shanghai Normal University

20 20

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Scales of MeasurementScales of Measurement

RatioRatio

• Example:Example:

Melissa’s college record shows 36 credit Melissa’s college record shows 36 credit hours earned, while Kevin’s record shows hours earned, while Kevin’s record shows 72 credit hours earned. Kevin has twice 72 credit hours earned. Kevin has twice as many credit hours earned as Melissa.as many credit hours earned as Melissa.

Page 21: Slides Prepared by Jing Huang Shanghai Normal University

21 21

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Qualitative and Quantitative DataQualitative and Quantitative Data

Data can be further classified as being Data can be further classified as being qualitative or quantitative.qualitative or quantitative.

The statistical analysis that is appropriate The statistical analysis that is appropriate depends on whether the data for the variable depends on whether the data for the variable are qualitative or quantitative.are qualitative or quantitative.

In general, there are more alternatives for In general, there are more alternatives for statistical analysis when the data are statistical analysis when the data are quantitative.quantitative.

Page 22: Slides Prepared by Jing Huang Shanghai Normal University

22 22

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Qualitative DataQualitative Data

Qualitative dataQualitative data (定性数据)(定性数据) are labels or are labels or names used to identify an attribute of each names used to identify an attribute of each element.element.

Qualitative data use either the Qualitative data use either the nominal or nominal or ordinalordinal scale of measurement. scale of measurement.

Qualitative data can be either numeric or Qualitative data can be either numeric or nonnumericnonnumeric..

The statistical analysis for qualitative data are The statistical analysis for qualitative data are rather limited.rather limited.

Page 23: Slides Prepared by Jing Huang Shanghai Normal University

23 23

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Quantitative DataQuantitative Data

Quantitative dataQuantitative data (定量数据)(定量数据) indicate either indicate either how many or how much.how many or how much.

• Quantitative data that measure how many Quantitative data that measure how many are are discretediscrete..

• Quantitative data that measure how much Quantitative data that measure how much are are continuouscontinuous because there is no because there is no separation between the possible values for separation between the possible values for the data..the data..

Quantitative data are always Quantitative data are always numericnumeric.. Ordinary arithmetic operations are meaningful Ordinary arithmetic operations are meaningful

only with quantitative data.only with quantitative data.

Page 24: Slides Prepared by Jing Huang Shanghai Normal University

24 24

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Cross-Sectional and Time Series DataCross-Sectional and Time Series Data

Cross-sectional dataCross-sectional data (截面数据)(截面数据) are collected are collected at the same or approximately the same point at the same or approximately the same point in time.in time.

• Example: Example: 表表 1.11.1 Time series dataTime series data (时间序列数据)(时间序列数据) are collected are collected

over several time periods.over several time periods.

• Example:Example:

Page 25: Slides Prepared by Jing Huang Shanghai Normal University

25 25

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Data SourcesData Sources

Existing SourcesExisting Sources

• Data needed for a particular application Data needed for a particular application might already exist might already exist within a firmwithin a firm. Detailed . Detailed information is often kept on customers, information is often kept on customers, suppliers, and employees for example.suppliers, and employees for example.

Page 26: Slides Prepared by Jing Huang Shanghai Normal University

26 26

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Data SourcesData Sources

Existing SourcesExisting Sources

• Substantial amounts of business and Substantial amounts of business and economic data are available from economic data are available from organizations that specialize in collecting organizations that specialize in collecting and maintaining dataand maintaining data..

• Government agenciesGovernment agencies are another are another important source of data.important source of data.

• Data are also available from a variety of Data are also available from a variety of industry associationsindustry associations and and special-interest special-interest organizationsorganizations..

Page 27: Slides Prepared by Jing Huang Shanghai Normal University

27 27

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Data SourcesData Sources

InternetInternet

• The The InternetInternet has become an important has become an important source of data.source of data.

• Most government agencies, like the Bureau Most government agencies, like the Bureau of the Census (www.census.gov), make their of the Census (www.census.gov), make their data available through a web site.data available through a web site.

• More and more companies are creating web More and more companies are creating web sites and providing public access to them.sites and providing public access to them.

• A number of companies now specialize in A number of companies now specialize in making information available over the making information available over the Internet.Internet.

Page 28: Slides Prepared by Jing Huang Shanghai Normal University

28 28

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Statistical StudiesStatistical Studies• Statistical studies can be classified as either Statistical studies can be classified as either

experimental or observational.experimental or observational.• In In experimental studiesexperimental studies (实验型研究)(实验型研究) the the

variables of interest are first identified. variables of interest are first identified. Then one or more factors are controlled so Then one or more factors are controlled so that data can be obtained about how the that data can be obtained about how the factors influence the variables.factors influence the variables.

• In In observationalobservational (nonexperimental) (nonexperimental) studiesstudies (观测型研究)(观测型研究) no attempt is made to control no attempt is made to control or influence the variables of interest; an or influence the variables of interest; an example is a example is a surveysurvey..

Data SourcesData Sources

Page 29: Slides Prepared by Jing Huang Shanghai Normal University

29 29

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Data Acquisition ConsiderationsData Acquisition Considerations

Time RequirementTime Requirement

• Searching for information can be time Searching for information can be time consuming.consuming.

• Information might no longer be useful by the Information might no longer be useful by the time it is available.time it is available.

Cost of AcquisitionCost of Acquisition

• OrganizationsOrganizations often charge for information even often charge for information even when it is not their primary business activity.when it is not their primary business activity.

Data ErrorsData Errors

• Blindly using any data that happen to be Blindly using any data that happen to be available or that were acquired with little care available or that were acquired with little care can lead to poor and misleading information.can lead to poor and misleading information.

Page 30: Slides Prepared by Jing Huang Shanghai Normal University

30 30

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Descriptive StatisticsDescriptive Statistics

Descriptive statisticsDescriptive statistics are the tabular, are the tabular, graphical, and numerical methods used to graphical, and numerical methods used to summarizesummarize data. data.

Page 31: Slides Prepared by Jing Huang Shanghai Normal University

31 31

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

91 78 93 57 75 52 99 80 97 6271 69 72 89 66 75 79 75 72 76104 74 62 68 97 105 77 65 80 10985 97 88 68 83 68 71 69 67 7462 82 98 101 79 105 79 69 62 73

91 78 93 57 75 52 99 80 97 6271 69 72 89 66 75 79 75 72 76104 74 62 68 97 105 77 65 80 10985 97 88 68 83 68 71 69 67 7462 82 98 101 79 105 79 69 62 73

Example: Hudson Auto RepairExample: Hudson Auto Repair

The manager of Hudson Auto would like to The manager of Hudson Auto would like to havehave

a better understanding of the cost of parts used a better understanding of the cost of parts used in thein the

engine tune-ups performed in the shop. She engine tune-ups performed in the shop. She examinesexamines

50 customer invoices for tune-ups. The costs of 50 customer invoices for tune-ups. The costs of parts,parts,

rounded to the nearest dollar, are listed below.rounded to the nearest dollar, are listed below.

Page 32: Slides Prepared by Jing Huang Shanghai Normal University

32 32

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Example: Hudson Auto RepairExample: Hudson Auto Repair

Tabular Summary (Frequencies and Percent Tabular Summary (Frequencies and Percent Frequencies)Frequencies)

PartsParts Percent Percent Cost ($)Cost ($) FrequencyFrequency

FrequencyFrequency

50-5950-59 2 2 4 4

60-6960-69 1313 2626

70-7970-79 1616 3232

80-8980-89 7 7 1414

90-9990-99 7 7 1414

100-109100-109 5 5 1010

Total 50Total 50 100 100

Page 33: Slides Prepared by Jing Huang Shanghai Normal University

33 33

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Example: Hudson Auto RepairExample: Hudson Auto Repair

Graphical Summary (Histogram)Graphical Summary (Histogram)

PartsCost ($)PartsCost ($)

22

44

66

88

1010

1212

1414

1616

1818

Fre

qu

en

cy

Fre

qu

en

cy

50 60 70 80 90 100 11050 60 70 80 90 100 110

Page 34: Slides Prepared by Jing Huang Shanghai Normal University

34 34

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Example: Hudson Auto RepairExample: Hudson Auto Repair

Numerical Descriptive StatisticsNumerical Descriptive Statistics• The most common numerical descriptive The most common numerical descriptive

statistic is the statistic is the averageaverage (or (or meanmean). ). • Hudson’s average cost of parts, based on Hudson’s average cost of parts, based on

the 50 tune-ups studied, is $79 (found by the 50 tune-ups studied, is $79 (found by summing the 50 cost values and then summing the 50 cost values and then dividing by 50).dividing by 50).

Page 35: Slides Prepared by Jing Huang Shanghai Normal University

35 35

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Statistical InferenceStatistical Inference

Statistical inferenceStatistical inference (统计推断) (统计推断) is the is the process of using data obtained from a small process of using data obtained from a small group of elements (the group of elements (the samplesample) to make ) to make estimates and test hypotheses about the estimates and test hypotheses about the characteristics of a larger group of elements characteristics of a larger group of elements (the (the populationpopulation).).

AA populationpopulation (总体)(总体) is the set of all is the set of all elements of interest in a particular study.elements of interest in a particular study.

AA sample sample (样本) (样本) is a subset of the is a subset of the population.population.

Page 36: Slides Prepared by Jing Huang Shanghai Normal University

36 36

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

Example: Hudson Auto RepairExample: Hudson Auto Repair

Process of Statistical InferenceProcess of Statistical Inference

1. Population 1. Population consists of allconsists of all

tune-ups. Averagetune-ups. Averagecost of parts iscost of parts is

unknownunknown.

2. A sample of 502. A sample of 50engine tune-ups engine tune-ups

is examined.is examined.

3. The sample data 3. The sample data provide a sampleprovide a sampleaverage cost ofaverage cost of

$79 per tune-up.$79 per tune-up.

4. The value of the 4. The value of the sample average is usedsample average is usedto make an estimate ofto make an estimate of the population average.the population average.

Page 37: Slides Prepared by Jing Huang Shanghai Normal University

37 37

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

SummarySummary

StatisticsStatistics is the art and science of collecting, is the art and science of collecting, presenting and interpreting data.presenting and interpreting data.

DataData are the facts and figures that are are the facts and figures that are collected, analyzed, presented and collected, analyzed, presented and interpreted. interpreted.

Four scales of measurement are available for Four scales of measurement are available for obtaining data on a particular variable: obtaining data on a particular variable: nominal, ordinal, intervalnominal, ordinal, interval and and ratioratio..

Page 38: Slides Prepared by Jing Huang Shanghai Normal University

38 38

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

SummarySummary

For purpose of statistical analysis, data can be For purpose of statistical analysis, data can be classified as qualitative or quantitative. classified as qualitative or quantitative. Qualitative Qualitative datadata are labels or names used to are labels or names used to identify an attribute of each element. identify an attribute of each element. QuantitativeQuantitative data data are numeric values that are numeric values that indicate how much or how many.indicate how much or how many.

Descriptive statisticsDescriptive statistics are the tabular, are the tabular, graphical, and numerical methods used to graphical, and numerical methods used to summarize data. summarize data. Statistical inferenceStatistical inference is the is the process of using data obtained from a sample process of using data obtained from a sample to make estimates or test hypotheses about to make estimates or test hypotheses about the characteristics of a population.the characteristics of a population.

Page 39: Slides Prepared by Jing Huang Shanghai Normal University

39 39

Copyright © 2010 HJ Shanghai Normal Uni.Copyright © 2010 HJ Shanghai Normal Uni.

End of Chapter 1End of Chapter 1