Use only RStudio to solve the following questions. Don't forget to submit your .R file along with your PDF file once you are done. Use Bikesharing dataset for this assignment.

Now include all the variables except datetime, weather, casual, and registered in the model to explain the total number of rentals. Also, briefly explain why we need to exclude datetime, weather, casual, and registered columns on the screenshot data.

Bike Sharing Demand (This is from Kaggle.com) Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able rent a bike from a one location and return it to a different place on an as-needed basis. Currently, there are over 500 bike-sharing programs around the world. The data generated by these systems makes them attractive for researchers because the duration of travel, departure location, arrival location, and time elapsed is explicitly recorded. Bike sharing systems therefore function as a sensor network, which can be used for studying mobility in a city. Here you are presented the dataset of historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bikeshare program in Washington, D.C. datetime - hourly date + timestamp season - 1 = spring, 2 = summer, 3 = fall, 4 = winter holiday - whether the day is considered a holiday workingday - whether the day is neither a weekend nor holiday temp - temperature in Celsius atemp - "feels like" temperature in Celsius humidity - relative humidity windspeed - wind speed casual - number of non-registered user rentals initiated registered - number of registered user rentals initiated count - number of total rentals