sas sample pre

28
Database Management and Modeling Final Project CHENYE PAN, EN-CHIAO WEN JIALIN ZHAO, WENQIAN WANG, XUETING SHEN DECEMBER 7, 2015

Upload: jialin-nicole-zhao

Post on 16-Feb-2017

28 views

Category:

Documents


1 download

TRANSCRIPT

Page 1: SAS Sample Pre

DatabaseManagementandModelingFinalProject

CHENYEPAN,EN-CHIAOWENJIALINZHAO,WENQIANWANG,XUETINGSHEN

DECEMBER7,2015

Page 2: SAS Sample Pre

AGENDA

Background

ObjecRves

Methodology

Findings

Conclusion

Page 3: SAS Sample Pre

1

BACKGROUND

Page 4: SAS Sample Pre

SFOconductsayearlycomprehensivesurveyoftheirgueststogaugesaRsfacRonwiththeirfaciliRes,services,andameniRes.CustomerinterviewsheldatallairportterminalsandboardingareasfromMay11throughMay26,2011

DataSource

Page 5: SAS Sample Pre

2

OBJECTIVES

Page 6: SAS Sample Pre

•  UnderstandandmonitorpassengerpurchasebehaviorandpercepRonofSFO

•  IdenRfyfactorsthathavestrongimpactonin-terminalpurchaseandSFOsaRsfacRon

•  UnderstandwhatdifferentpassengergroupsexpectofSFO

•  IdenRfyareastoimprove

ObjecRves

Page 7: SAS Sample Pre

3

METHODOLOGY

Page 8: SAS Sample Pre

Thisdatasetcontains3188observaRonsand7corevariables,includingtwodependentvariables:•  Purchaseinin-terminalretailstores•  OverallsaRsfacRonOthercorevariablesare“Age”,“Gender”,“HouseholdIncome”,“TimesflownoutfromSFO”and“TripPurpose”.

Variables

Page 9: SAS Sample Pre

PurchaseCross-TabulaRonAnalysisLogitRegression

Sa+sfac+onIndexCross-TabulaRonAnalysisLogitRegression

Approach

Thisreport’smainpurposeistoanalyzewhichpassengersaremorelikelytopurchaseinSFOandwhichhavehighersaRsfacRonwithSFO.StaRsRcmeasuresandmodelareused.

Page 10: SAS Sample Pre

4

FINDINGS

Page 11: SAS Sample Pre

Purchase

Age,FlyingTimesandTripPurposehavenosignificantassociaRonwithPurchase

Page 12: SAS Sample Pre

•  TheChi-SquarestaRsRcsissignificant(p<0.05)indicaRnganassociaRonbetweenGenderandPurchaseinretailstoressuchthatthefemalepassengersaremorelikelytopurchaseinin-terminalretailstores(40%versus33%)

MeasureofAssociaRonGenderandPurchase

•  Gender0=male,gender1=femalePurchase0=no,purchase1=yes

Page 13: SAS Sample Pre

•  OddsraRo≅1.3,indicaRngthatfemalesare1.3Rmesmorelikelythanmalestopurchase

MeasureofAssociaRonGenderandPurchase

•  Gender0=male,gender1=femalePurchase0=no,purchase1=yes

Group1

Group2

Page 14: SAS Sample Pre

•  TheChi-SquarestaRsRcsissignificant(p<0.05)indicaRnganassociaRonbetweenHouseholdIncomeandPurchaseinretailstoressuchthatthepassengerswithover100Khouseholdincomearemorelikelytopurchaseinin-terminalretailstores(40%versus35%)

MeasureofAssociaRonHHIandPurchase

•  HHI0=$100Kandunder,HHI1=above$100KPurchase0=no,purchase1=yes

Page 15: SAS Sample Pre

•  OddsraRo(OR)≅1.25meanspassengerswithHHI>100Kare1.25RmesmorelikelythanpassengerswithHHI≤$100Ktopurchase.

MeasureofAssociaRonHHIandPurchase

•  HHI0=$100Kandunder,HHI1=fabove$100KPurchase0=no,purchase1=yes

Group1

Group2

Page 16: SAS Sample Pre

Gender Income Logit (Log of odds)

Odds of non-purchase

Male (0) HHI ≤100K (0) 0.8028 e0.8028=2.2318

Male (0) HHI >100K (1) 0.8028+(-0.2618)=0.5410 e0.5410=1.7177

Female (1) HHI ≤100K (0) 0.8028+(-0.3101)=0.4927 e0.4927=1.6367

Female (1) HHI >100K (1) 0.8028+(-0.3101)+(-0.2618)=0.2309 e0.2309=1.2597

!

Logit(purchase)=0.8028-0.3101*Gender(Female)-0.2618*HHI(>$100K)

•  FemalepassengerswhoseHHIismorethan$100KaremostlikelytopurchaseintheSFOretailstores

•  MalepassengerswhoseHHI≤$100KisleastlikelytopurchaseinSFOretailstores

LogitRegressionModel

Page 17: SAS Sample Pre

SaRsfacRon

Age,GenderandHouseholdIncomehavenosignificantassociaRonwithPurchase

Page 18: SAS Sample Pre

SaRsfacRonScore

Page 19: SAS Sample Pre

Index

Page 20: SAS Sample Pre

MeasureofAssociaRon

•  TheChi-SquarestaRsRcsissignificant(p<0.05)indicaRnganassociaRonbetweenFlyingFrequencyandOverallSaRsfacRonsuchthatthepassengerswhohaveflownmorethan6RmesfromSFOinthepast12monthsaremorelikelytohavelowsaRsfacRonwithSFOthanthoseflown1-6Rmes(31%versus24%)

FlyingTimesandSaRsfacRon

•  Times0=1-6Rmes,Rmes1=morethan6RmessaRsfacRon0=low,saRsfacRon1=high

Page 21: SAS Sample Pre

MeasureofAssociaRon

•  OddsRaRo(OR)≅0.7,indicaRngthatGroup1(thoseflying6Rmesandless)are30%lesslikelytohavelowsaRsfacRonthanGroup2(thoseflyingmorethan6Rmes)

Group1

Group2

FlyingTimesandSaRsfacRon

•  Times0=1-6Rmes,Rmes1=morethan6RmessaRsfacRon0=low,saRsfacRon1=high

Page 22: SAS Sample Pre

MeasureofAssociaRon

•  TheChi-SquarestaRsRcsissignificant(p<0.001)indicaRnganassociaRonbetweenTripPurposeandOverallSaRsfacRonsuchthatthepassengerswhoflyforbusinessaremorelikelytohavelowsaRsfacRonwithSFOthanthosewhoflyfornon-business(30%versus22%)

TripPurposeandSaRsfacRon

•  purpose0=business,purpose1=non-businesssaRsfacRon0=low,saRsfacRon1=high

Page 23: SAS Sample Pre

MeasureofAssociaRon

•  OddsRaRo(OR)≅1.5,indicaRngthatGroup1(thoseflyingforbusiness)are1.5RmesmorelikelytohavelowsaRsfacRonthanGroup2(thoseflyingfornon-business)

Group1

Group2

TripPurposeandSaRsfacRon

•  purpose0=business,purpose1=non-businesssaRsfacRon0=low,saRsfacRon1=high

Page 24: SAS Sample Pre

LogitRegressionModel

Purpose Times Logit (Log of odds)

odds of Low Satisfaction

Business (0) ≤6 times (0) -0.8940 e-0.8940=0.4090

Business (0) >6 times (1) -0.8940+ 0.2642=-0.6298 e-0.6298=1.8772

Non-business (1) ≤6 times (0) -0.8940+(-0.3939)=-1.2879 e-1.2879=0.2758

Non-business (1) >6 times (1) -0.8940+0.2642+(-0.3939)=-1.0237 e-1.0237=0.3592

!

•  Passengerswhotravelsfor

businessandhaveflownoutfromSFOformorethan6RmesareleastsaRsfiedwithSFO.

•  Passengerwhotravelsfornon-businessandhaveflownoutfromSFOfor1-6RmesaremostsaRsfiedwithSFO.

Logit(lowsaRsfacRon)=-0.8940-0.3939*Purpose(non-business)-0.2618*Times(>6)

Page 25: SAS Sample Pre

5

CONCLUSION

Page 26: SAS Sample Pre

•  Femalepassengersandhigh-incomepassengersaremorelikelytopurchaseinSFOØ  SFOmayprovidemoreproductsandbrandsthatcaterto

high-incomefemales•  Businesspassengersandpassengerswhohaveflownmorethan

6RmesoutfromSFOinthepast12monthsarelesssaRsfiedwithSFOØ  Furtherresearchisneededtofindoutwhybusiness

passengershavelowsaRsfacRonandfindawaytoimprovethereturningpassengers’saRsfacRonwithSFO

Insights

Page 27: SAS Sample Pre

AreastoImprove

•  ArtworkandexhibiRons•  Restaurants•  Retailshopsandconcessions•  InformaRonbooths(lowerlevelnearbaggageclaim)•  InformaRonbooths(upperlevel-departurearea)•  SignsanddirecRonsonSFOairportroadways•  AirportparkingfaciliRes•  Longtermparkinglotshutle(busride)

Page 28: SAS Sample Pre

THANKYOU!&

GOODLUCKWITHYOURFINALS