Business Analytics for Managers

202 Pages·2011·4.59 MB·English

by  Wolfgang Jank (auth.)

Use R!
SeriesEditors:
RobertGentleman KurtHornik GiovanniParmigiani
Forfurthervolumes:
http://www.springer.com/series/6991

Wolfgang Jank
Business Analytics
for Managers
123
WolfgangJank
DepartmentofDecisionandInformationTechnologies
RobertH.SmithSchoolofBusiness
UniversityofMaryland
VanMunchingHall
CollegePark,MD20742-1815
USA
[email protected]
SeriesEditors:
RobertGentleman KurtHornik
PrograminComputationalBiology DepartmentofStatistikandMathematik
DivisionofPublicHealthSciences Wirtschaftsuniversita¨tWien
FredHutchinsonCancerResearchCenter Augasse2-6
1100FairviewAvenue,N.M2-B876 A-1090Wien
Seattle,Washington98109 Austria
USA
GiovanniParmigiani
TheSidneyKimmelComprehensive
CancerCenteratJohnsHopkinsUniversity
550NorthBroadway
Baltimore,MD21205-2011
USA
ISBN978-1-4614-0405-7 e-ISBN978-1-4614-0406-4
DOI10.1007/978-1-4614-0406-4
SpringerNewYorkDordrechtHeidelbergLondon
LibraryofCongressControlNumber:2011934258
(cid:2)c SpringerScience+BusinessMedia,LLC2011
Allrightsreserved.Thisworkmaynotbetranslatedorcopiedinwholeorinpartwithoutthewritten
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connectionwithanyformofinformationstorageandretrieval,electronicadaptation,computersoftware,
orbysimilarordissimilarmethodologynowknownorhereafterdevelopedisforbidden.
Theuseinthispublicationoftradenames,trademarks,servicemarks,andsimilarterms,eveniftheyare
notidentifiedassuch,isnottobetakenasanexpressionofopinionastowhetherornottheyaresubject
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Printedonacid-freepaper
SpringerispartofSpringerScience+BusinessMedia(www.springer.com)
To myFamily:
Angel,Isabella,Alexander,Waltraud,
Gerhard,andSabina

Preface
This book is about analytics and data-driven decision making. As such, it could
easilybemistakenforabookonstatisticsordatamining.Infact,thisbookconveys
ideasandconceptsfrombothstatisticsanddatamining,withthegoalofextracting
knowledge and actionable insight for managers. However, this is not a statistics
book.Thereexistthousandsofbooksonthetopicofstatistics.Mostofthesebooks
are written by statisticians for statisticians. As a result, they often focus primarily
onmathematics,formulas,andequationsandnotso muchonthe practicalinsight
thatcanbe derivedfromthese equations.Thisbookusesconceptsandideasfrom
statistics (without ever getting bogged down in too much mathematical detail) in
ordertoextractinsightfromrealbusinessdata.
This is also not a book on data mining. There are many good data mining
books, some of which are written for data miners and computer scientists, others
forpractitioners.However,mostofthesebooksfocusonalgorithmsandcomputing.
Thatis,theyemphasizethemanydifferentalgorithmsthatexistinordertoextract
similar information from the same set of data. This book does not emphasize
algorithms. In fact, it acknowledges early on that while there may exist many
differentwaysto solveand tacklea particularproblem,the goalis to conveyonly
themainprinciplesofhowtodiscovernewknowledgefromdataandhowtomake
data-drivendecisionsinasmartandinformedway.
Andfinally,thisisalsonota bookonsoftware.Whilethisbookprovidesinits
finalchapteraquick-starttooneofthemostpowerfulsoftwaresolutions,emphasis
isplacedonconveyingdata-driventhinking(andnotsomuchonimplementation).
Theideasdiscussedinthisbookcanbeimplementedusingmanydifferentsoftware
solutionsfrom manydifferentvendors.In fact, this bookpurposefullysteers clear
of software implementation since it is our experience that books that do discuss
softwareoftenplacetoomuchemphasisonimplementationdetails,whichconfuses
readersanddistractthemfromthemainpoint.Afterall,themainpointofthisbook
is not to train new statisticians or data miners – there are better books that can
accomplishthatgoal.Themainpointistoconveytheuseandvalueofdata-driven
decision makingto managers.Managershardlyeverimplementcomplexmethods
vii
viii Preface
and models themselves – however, they frequently communicate with personnel
whodo.Withthatinmind,themaingoalsofthisbookareasfollows:
(cid:129) Toexcitemanagersanddecisionmakersaboutthe potentialthatresidesindata
andthevaluethatdataanalyticscanaddtobusinessprocesses.
(cid:129) To provide managers with a basic understanding of the main concepts of data
analytics and a common language to convey data-driven decision problems so
they can better communicate with personnel specializing in data mining or
statistics.
Afterall,wearelivinginaninformation-basedsociety,andusingthatinforma-
tionsmartlycanhavebenefitstoboththebusinessandtheconsumer.
January2011 WolfgangJank
Contents
1 Introduction .................................................................. 1
1.1 AnalyticsandBusiness ................................................. 1
1.2 GoalofThisBook ...................................................... 3
1.3 WhoShouldReadThisBook? ......................................... 4
1.4 WhatThisBookIsNot ................................................. 5
1.4.1 ThisIsNotaStatisticsBook................................... 5
1.4.2 ThisIsNotaDataMiningBook............................... 5
1.5 WhatThisBookIs...................................................... 5
1.6 StructureofThisBook.................................................. 6
1.7 UsingThisBookinaCourse........................................... 7
2 ExploringandDiscoveringData ........................................... 9
2.1 BasicDataSummariesandVisualizations:HousePriceData........ 10
2.2 DataTransformationsandTrellisGraphs:DirectMarketingData ... 20
2.3 TimeSeriesGraphs:SoftDrinkSalesData............................ 25
2.4 SpatialGraphs:OnlinePurchasePreferencesData.................... 28
2.5 Graphs for Categorical Responses: Consumer-to-
ConsumerLoanData ................................................... 30
2.6 GraphsforPanelData:CustomerLoyaltyData....................... 34
3 DataModelingI–Basics.................................................... 41
3.1 Introduction:WhyDoWeNeedModels? ............................. 42
3.2 Fitting and Interpreting a Regression Model:
LeastSquaresRegression............................................... 47
3.2.1 TheIdeaofLeastSquaresRegression......................... 48
3.2.2 InterpretingaFirstSimpleRegressionModel................. 50
3.2.3 EvaluatingaRegressionModel................................ 52
3.2.4 ComparingRegressionModels ................................ 55
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