cb2200 course outline

7/24/2019 CB2200 Course Outline

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City University of Hong Kong

Information on a Courseoffered by the Department of Management Sciences

with effect from Semester A in 2!" # 2!$

This form is for completion by the Course Co-ordinator/Examiner.The informationprovided on this form will be deemed to be the official record of the details of thecourse. It will be used for several purposes: for the Universitys database, and for

publishing in various University publications including Blackboard, documents forstudents and others as necessary.

lease refer to theExplanatory Notesattached to this form for the precise informationre!uired.

%art I

"ourse Title: Business #tatistics

"ourse "ode: "B$$%%

"ourse &uration: 'ne #emester

"redit Units: (

)evel: B$

*edium of Instruction: +nglish

rere!uisites: il

recursors: il

+!uivalent "ourses: *#$$%% Business #tatistics

+-clusive "ourses: il

%art II

Course Aims

This course aims to facilitate students learning of basic statistical concepts commonlyused in business management decision making, and their application to the real world.The course content is based on realworld e-amples and cases to encourage studentsto develop their attitude and ability to discover and innovate.

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Course Intended &earning 'utcomes (CI&'s)(State what the student is expected to be able to do at the end of the course accordingto a given standard of performance.

!pon successful completion of this course" students should be able to#

*o+ CI&'s ,eighting (if

app-icab-e)

D.C/re-ated

dimension!+ +-plain concepts in numerical descriptivemeasures, sampling distributions, confidenceinterval estimation, hypothesis testing, and simplelinear regression model.

(01 2bility

2+ #elect appropriate statistical methods to analyse

reallife business data, interpret the results and

give recommendations for business decisions.

(01 2bility

0+ 2pply standard statistical software, such as

*icrosoft +-cel, to analyse data arising from real

life business problems.

$%1 2bility

"+ 2ble to demonstrate the attitude to providerecommendations 3 innovations based on

statistical data

/%1 2ttitude

1eaching and &earning Activities (1&As)($hese are indicative of li%ely activities and tas%s designed to facilitate students&achievement of the C')s. *inal details will be provided to students in their firstwee% of attendance in this course.

CI&' *o+ 1&As Hours#wee (if

app-icab-e)!3 23 0 4 " !+ &ectures

ectures: #tatistical analytical techni!ues, relevant

knowledge and concepts are e-plained.

Case studies: "ase studies that illustrate the use of

statistics in the real world are discussed.

!3 2 4 " 2+ 1utoria-s

Class +iscussion: #tudents work in small groups to

discuss the criteria and appropriateness of chosenstatistical measures and methods to realworld

business problems4 e-amples might be to describethe pattern of the data, or to evaluate the 5ob

performance of the staff. The instructor providesinstant feedback based on students responses.

Exercises: #tudents discuss their responses to take

home and inclass e-ercises that are designed toenhance their statistical analytical skills within a

business conte-t. They are re!uired to interpret theresults and give recommendations. #tudents aregiven the opportunity in class to feedback on eachothers work.

0 0+ Computer &aboratory ActivitiesThe instructor demonstrates the use of statistical software

packages to solve realworld business problems. 'utof

class, students are asked to carry out similar analyses withthe use of computer software.

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Assessment 1ass($hese are indicative of li%ely tas%s designed to assess how well the students achievethe C')s. *inal details will be provided to students in their first wee% of attendancein this course.

CI&'

*o+

1ypes of Assessment

1ass (A1s)

Assessment Detai-s ,eighting (if

app-icab-e)!3 2 4 " !+ ,ritten .5amination The e-am is designed to assess students

professional knowledge of selecting andapplying different statistical methods tosolve business problems. "omputer outputmay be given for students interpretationand suggestions.

0%1

!3 2 4 " 2+ Mid/1erm 1est

67eek 8 #aturday,/$:/0pm 9 /:0pm;

The test is designed to assess studentsprofessional knowledge of selecting andapplying different statistical models tosolve business problems.

$%1

23 0 4 " 0+ Case Studies # 6roup

%ro7ects

#tudents work in a group to identify a set

of relevant statistical concepts to realworld case and use them to analyse thecase.

$01

2 4 " "+ In/C-ass Discussionand %erformance

#tudents are re!uired to work on thesee-ercises individually to practise theiranalytical skills in statistical analysiswithin a business conte-t. #tudents presenttheir findings from the e-ercises. #tudentsare given the opportunity in class tofeedback on each others work.

01

8 Students must pass the e5amination and coursewor to pass the course+

%art III

Keyword Sy--abus

!+ %resenting Data and Descriptive Statistics

Types of data. &escriptive statistics including measures of central tendency, variation

and shape.

2+ 9asic %robabi-ity and %robabi-ity Distributions

robability distribution for a discrete random variable. Binomial distribution. ormal

distribution. #ampling distributions of mean and proportion. "entral limit theorem.

0+ Statistica- Inference

+stimation of population parameters the mean and proportion. "onfidence interval

estimation. #tatistical hypotheses. Type I and Type II errors. The significance level

and re5ection region. The pvalue. Testing hypotheses about the mean and proportion.

&etermining sample si


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1e5tboo

)evine, &.*., =rehbiel, T.". and Berenson, *.).,usiness Statistics# *irst Course,

)atest +dition, earson +ducation )imited.

:eferences

>effrey '. Bennett, 7illiam ). Briggs, *ario ?. Triola, Statistical easoning for

Everyday ife, 3e, $%/, 7esley

)iu, =. I., To =. *., Spea%ing of Statistics, $%/, earson +ducation )td

ewbold, ., "arlson, 7.). and Thorne, B. Statistics for ,usiness and Economic.

rentice @all

*iddleton, *.A.+ata nalysis !sing icrosoft Excel. Thomson, Brooks3"ole.

'n-ine :esources

#tatistics lossary

http:33www.stats.gla.ac.uk3steps3glossary 3inde-.html

#tatistical Universe

http:33www.lib.umich.edu3govdocs3statuniv.html

#TI"I 9 2 very interesting online statistics course

http:33www.stat.berkeley.edu3Cstark3#ticiui3Te-t3inde-.htm

@yper#tat 'nline #tatistics Te-tbook

http:33davidmlane.com3hyperstat3
http://www.stats.gla.ac.uk/steps/glossary%20/index.htmlhttp://www.lib.umich.edu/govdocs/statuniv.htmlhttp://www.stat.berkeley.edu/~stark/SticiGui/Text/index.htmhttp://davidmlane.com/hyperstat/http://www.stats.gla.ac.uk/steps/glossary%20/index.htmlhttp://www.lib.umich.edu/govdocs/statuniv.htmlhttp://www.stat.berkeley.edu/~stark/SticiGui/Text/index.htmhttp://davidmlane.com/hyperstat/


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C-ass Schedu-e1'%IC *'+ ';,..KS

1opic !< Introduction to StatisticsSession 0.1# Statistics# *undamental for ,usinessSession 0.2# ,asic 3ocabulary of StatisticsSession 1.4# isuses and Common Errors in 3isuali5ing +ata

Session 0.6# +ata and 3ariables Types of Dariables

Session 6.0# Central $endency

The *ean

The *edian

The *ode

Session 6.1# 3ariation and Shape

The Aange

The Dariance and the #tandard &eviation

#hape

Session 6.2# Numerical +escriptive easures for a 7opulation

The opulation *ean The opulation Dariance and #tandard &eviation

Session 6.6#Exploring Numerical +ata

Euartiles

The Inter!uartile Aange

The ?iveumber #ummary

The Bo-plot

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1opic 2< 9asic %robabi-ity 4 9inomia- DistributionSession 2.0# ,asic 7robability Concepts

+vents and #ample #paces

"ontingency Tables

#imple robability

>oint robability

eneral 2ddition Aule

Session 2.1#Conditional 7robability

"omputing "onditional robabilities

&ecision Trees

Independence

*ultiplication Aules

*arginal robability Using the eneral *ultiplication Aule

Session 8.0#$he 7robability +istribution for a +iscrete andom 3ariable

+-pected Dalue of a &iscrete Aandom Dariable

Dariance and #tandard &eviation of a &iscrete Aandom Dariable

Session 8.1# ,inomial +istribution

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1opic 0< *orma- DistributionSession 6.2# Numerical +escriptive easures for a 7opulation

The +mpirical Aule

Session 9.1# $he Normal +istribution

"omputing ormal robabilities

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1opic "< Samp-ing DistributionSession :.2# Sampling +istribution of the ean

#tandard +rror of the *ean

#ampling from ormally &istributed opulations

#ampling from onnormally &istributed opulations 9 The "entral )imit Theorem

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1'%IC *'+ ';,..KS

1opic $< Confidence Interva- .stimationSession 4.0# Confidence 'nterval Estimation for the ean (; %nownSession 4.1# Confidence 'nterval Estimation for the ean (; un%nown

#tudents t &istribution

roperties of the t &istribution The "oncept of &egrees of ?reedom

The "onfidence Interval #tatement

Session 4.2# +etermining Sample Si5e

#ample #i

cb2200 course outline

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