Bike Sharing Systems Bike sharing systems are a new generation of bike rentals where the whole process from membership, rental and return has become automatic. Through these systems, a user is able to easily rent a bike from a particular position and return the bike at another posi- tion. Currently, there are over 500 bike-sharing programs around the world, with some of the best and largest found in Hangzhou (China), Paris (France), London (England), New York City (US) and Montreal (Canada). Great interest in these systems exists due to their role in addressing traffic congestion, environmental impact and population health issues in big cities. The data for this assignment comes from one such program, called Capital Bikeshare, operating in Washington in the US. It has over 3000 bicycles that can be rented from over 350 stations across Washington, D.C., Arlington and Alexandria, VA and Montgomery County, MD. Their website encourages users to check out bikes for a trip to work, to run errands, go shopping, or visit friends and family. Users can join Capital Bikeshare for one to three days (casual membership), or for a month or a year (registered membership). Access to the Capital Bikeshare fleet of bikes is available 24 hours a day, 365 days a year. The first 30 minutes of each trip are free. You will use data derived from Capital Bikeshare trip records to build a statistical model for the purposes of predicting the total number of rentals per day. References and Data Sources: . Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository http://archive . ics. uci . edu/ml. Irvine, CA: University of California, School of In- formation and Computer Science. . Fanace-T, Hadi, and Gama, Joao, 'Event labeling combining ensemble detectors and background knowledge', Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg. . http://capitalbikeshare. com/system-data Data file for this assignment The data file for this assignment is called hourlyRent. sas7bdat and contains 1000 randomly selected hourly counts of bike rentals for 2011 and 2012, derived from Capital Bikeshare trip history data, with additional weather and seasonal information. The data was downloaded from the UCI Machine Learning Repository. Variables in that file are: Variable Description instant Record index dteday Date season 1:spring, 2: summer, 3: autumn 4: winter (northern hemisphere) yr 0:2011, 1:2012 mnth Month (1 to 12) wkday Day of the week (Monday to Sunday) workingday Working day=1, weekend and public holiday = 0 temp Normalised temperature in degrees Celsius; observed temperature di- vided by 41 (max) atemp Normalised 'feels like' temperature in degrees Celsius; values divided by 50 (max) hum Normalised humidity; observed values divided by 100 (max) 1:Clear, few clouds, partly cloudy weathersit 2: Mist+Cloudy, Mist+ Broken Cloud, Mist + Few clouds, Mist 3: Light snow, Light rain+ thunderstorm, light rain + scattered clouds windspeed Normalised wind speed; observed values divided by 67 (max) session peak: 7