G While Citibike cannot do anything about the weather, it can promote use of its bikes to target groups when different types of weather events occur. Does the past data provide any guidance to the marketing team regarding what types of weather place greater demand on Citibike usage? Are there differences based on routes/customer type/etc?

A) YES

B) YES

Explanation:

A) Does the past data provide any guidance to the marketing team regarding what types of weather place greater demand on Citibike usage?  YES.

The data has been processed to remove trips that are taken by staff as they service and inspect the system. As for this data set many features are studied, including features used for bike demand between all pairs of stations, and mean/median trip duration.

The data set was also collected from the National Oceanic and Atmospheric Administration (NOAA). That provide the access to GHCN (Global Historical

Climatology Network) – Daily Database, which is a database that addresses the critical need for historical daily temperature, precipitation, and snow records over global land areas. GHCN-Daily is a composite of climate records from numerous sources that were merged and then subjected to a suite of quality assurance reviews. The archive includes over 40

meteorological elements including temperature daily maximum/minimum, temperature

at observation time, precipitation, snowfall, snow depth, evaporation, wind movement, wind maximums, soil temperature, cloudiness, and more.

B) Are there differences based on routes/customer type/etc

YES.

After visualizing the trip duration for two different gender groups, it was found that despite the similar distribution of trip duration between those two groups, the median number for the female group is actually bigger than the male, which indicates that female tend to use Citi Bike for longer time. As for the male group, most of the trips they

made are less than 350 minutes. It is a reasonable time period for commuters and light exercise. While it is also surprising to find that within the female group, around 40 percent of the trips are longer than 350 minutes. This may indicate that the trip duration can be used to analysis the different usage pattern between two different gender groups. Given those characteristics, we assume that the duration and the start station location can be useful to predict the user gender.