Citibike Dataset Analysis (For NYU CUSP PUI2017 HW7)<Zhiao Zhou, zz1749>AbstractNew York Times has reported that there were more male bike-share members in NYC where about a third of members were female, who cared more about safety and convenience. However, it was also mentioned that quite a few women liked biking to work.\citep{gap} So it would be interesting to find out if the ratio of men biking at morning (commuting period for most people) over man biking the whole day is smaller than the ratio of women which would help balance the gender disparity.  Here we carried out a z test between proportions in iPython notebook to test my hypothesis using a sample of 201706 Citibike (The most popular bike-sharing system in NYC) public datasets. It turned out that the Z-score is 9.9977 and the p-value is 7.7958e-24. So we could accept our alternative hypothesis that women actually bike more at morning which would be useful for future analysis since the existing gender disparity seems to result from lack of infrastructure and safety for women. IntroductionFirst launched in 2013, Citibike has now totals of 706 stations and 12,000 bikes which pushed itself to become the biggest bike-sharing system in the USA.\citep{wikipedia}  However, Citibike has been struggling to figure out why men far outnumber women in using their services, with the number of men riders double that of women riders, as Sarah M. Kaufman,  the assistant director of tech programming at the Rudin Center for Transportation at NYU, said that women became early indicators of a successful bike system which means that if you had more women riders, it means that it would be convenient and safe. \citep{fitzsimmons2015} This phenomenon also emerged in Chicago and Washington where bike-sharing systems attracted more men. And till now it's still not solved yet what triggers this gender disparity.  The Citibike company was trying to introduce new stylish bikes or add new stations to woo women.What was fun was that there seemed to be a number of women who loved to commute by public-sharing bike. If we could find out that in fact, women bike more than men at morning, the company could focus more on service for women during a commute. Additionally, this hypothesis was untested, we could easily test it using z-test, nonetheless.  Figure 1 shows my null hypothesis and its corresponding maths expression as well as my significance level.