Time Series Regression Analysis to Forecast Bike Rental Demands and Analysis of Bike Rental Patterns Using JMP® 11


In today’s world, with environmental and health issues gaining significant importance, commuting by ecofriendly modes of transportation is becoming popular. One such environment friendly means which is gaining popularity in the USA is biking activity. With the increase in the number of bike enthusiasts, more and more bike renting companies are trying to find their foothold in this market. One challenge these companies face is to predict the number of casual customers, who rent a bike occasionally and the number of registered customers, who rent more regularly and have a membership with the company. It is also important for these rental companies to understand the renting patterns of different customers over time.


Zabiulla Mohammed is a Masters’ student in Management Information Systems at Spears School of Business, Oklahoma State University. He holds SAS Statistical Business Analyst and Base Programmer for SAS 9 Credentials. He has 5 years of experience working with two Fortune 100 companies. Currently he is enrolled in the SAS and OSU Data Mining Certificate program and works as Graduate Assistant in the Economics department. He has an undergraduate degree in Computer Science and Engineering.

Vandana Reddy is a Graduate student in Management Information Systems at Oklahoma State University and works as a Graduate Assistant for the department of Marketing. She won the SAS Global Forum-2014 student scholarship award. She is Base SAS® 9 certified professional, a certified SAS ® 7 predictive modeler, JMP Software data exploration certified and holds the SAS and OSU Data Mining certification. She has one paper and 4 poster publications in SAS conferences.