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EXPLANATORY MODEL FOR FATAL
VEHICLE ACCIDENTS IN THE UNITED STATESTORONTO AREA SAS SOCIETY
PRESENTED BY: KATHERINE HEIGHINGTON, MUKUL PANDEY, SUNNY GIROTI
SAS Student Symposium – Our Team
KATHERINE HEIGHINGTONB.Sc., B.Ed.
SUNNY GIROTIB.Eng.
MUKUL PANDEYB.Eng., M.B.A.
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Fatal Vehicle Accidents in the US
Compared to 19 other highincome countries, the UnitedStates had highest deaths per100,000 people.
2013
32,000
2 Million
Deaths
Injuries
2013 2015
number of deaths has
increased by 10% to over
35,000 deaths.
4.5
5.1
5.4
5.6
10.3
Japan
France
Canada
New Zealand
United States
Deaths per 100,000 people in 2013
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Fatality Analysis Reporting Data
ACCIDENT
VEHICLE
PARKWORK
PERSON
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Data Cleansing and Aggregation
Missing Data
Explanation: Impute the mean for continuous variables and Indicate level for categorical variables
Reason: linear regression models can’t have missing data
PROC HPIMPUTE
Aggregate Data from Multiple Files
•Explanation: Aggregated the variables so there is only one observation per crash
•Reason: Allow for proper comparison of different crashes
PROC SQL
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Variable Selection
Variable Selection Using Stepwise•Explanation: Selects significant variables that add
information to the model•Reason: Reduce the complexity of the model PROC REG
Multicollinearity between Variables•Explanation: Two variables provide the same
information (highly correlated)•Reason: Causes the model to be unstable PROC REG
Hierarchical Clustering to Reduce Categorical Variable Levels•Explanation: Collapses Levels in a way that
minimally disturbs the Chi square values•Reason: Reduce the complexity of the model
PROC CLUSTER
PROC TREE
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ANALYSIS &
REPORTING
Key explanatory factors for high fatality were Drugs Intake, Alcohol Consumption and Ejection from the Vehicle
Important finding – Day of the week (as well as the time such as Dawn) had a high correlation with fatality. - Wee hours of Saturday and Sunday were the worst
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Schulich – Master of Business AnalyticsFa
ll Te
rm 2
01
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•Predictive Modeling
•SAS Data Programming
•Data Science: Machine Learning
•Economic Forecasting: R
•Quantitative Methods for Business W
inte
r Te
rm 2
01
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SAS Data Programming
Multivariate Methods for Analytics
Data Science: Machine Learning using Python
Analytics Consulting
Case Analysis
Marketing Metrics and Research
Sum
me
r Te
rm 2
01
7
Capstone Project: 12-week project with hands-on problem driven research with a company
SAS Certified upon Graduation
WORKSHOPS: Big Data, Data Governance, Tableau, SAS Visualization Analytics, Text Analytics (Scheduled in March & April)
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Our Team - Mukul Pandey
ACADEMIC BACKGROUND:
B. Engineering, Electronics & Communication (University of Delhi, India)
M.B.A, Finance & Strategy (Indian School of Business, India)
PROFESSIONAL EXPERIENCE:
SAP ERP Consulting at PwC, ERP & Process Consulting at Ernst & Young
Strategy and Marketing at Schneider Electric
FAVOURITE MBAN COURSES:
Predictive Modeling using SAS, Economic Forecasting using R, and Data Sciences using Python
RELEVANT PROJECTS:
Forecasting Index of Industrial Production using advanced (ARIMA/Holt-Winters/VAR) Models
Account Based Marketing strategy for a GTA based Digital & Analytics startup
Machine Learning techniques for improved prediction of key global indices
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ACADEMIC BACKGROUND:
B.Sc. Human Biology (University of Toronto)
B.Ed. Mathematics Education (Ontario Institute for Studies in Education)
PROFESSIONAL EXPERIENCE:
Former Math Teacher
FAVOURITE MBAN COURSES:
Predictive Modeling (SAS Based Course) and Economic Forecasting
RELEVANT PROJECTS:
Forecasting Alcohol Sales with Time Series Analysis
Writing a Data Governance Case Study with the CDO of TD Bank and MBAN Program Director
Max TV Media Marketing Analysis Consultant (In progress)
Our Team - Katherine Heighington
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Our Team - Sunny Giroti
ACADEMIC BACKGROUND:
B. Engineering, Computer Science (Jaypee Institute of Information Technology, India)
Graduate Gemologist, Diamonds (Gemological Institute of America)
PROFESSIONAL EXPERIENCE:
SAP Business Intelligence Technical Consulting at Deloitte Consulting USI and Sopra Steria Pvt. Ltd.
Entrepreneur and Gemology Advisor at Giroti Jewels (Colored Stones, Diamonds, Gold)
FAVOURITE MBAN COURSES:
Predictive Modeling (SAS), Data Science (Machine Learning), Analytics Consulting and Case Analysis
RELEVANT PROJECTS:
MRKT360’s Market Identification and Penetration Strategy (Live project with the company, in progress)
Market Research based project to launch a new Baby Wearable Device in Canada
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Thank You !
Q&A
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