Chapter 1 Regression Problem 1.1 For this problem set, we will use 13,103 observations of hourly counts from 2011 to 2012 for bike rides (rentals) from the Capital Bikeshare system in Washington DC. The data are recorded for hours after 6am every day. (We omit earlier hours for convenience since they often include zero ride counts.) This dataset is adapted from data originally compiled by Fanaee and Gama in ‗Event labeling combining ensemble detectors and background knowledge‘ (2013). This data can be used for modeling system usage (ride counts). Such usage modeling is a key input for operational planning. bikeshare.csv contains:  dteday: date  mnth: month (1 to 12)  holiday: whether day is holiday or not  weekday: day of the week, counting from 0:sunday.  workingday: if day is either weekend or holiday is 0, otherwise is 1.  weathersit: broad overall weather summary (clear, cloudy, wet)  temp: Temperature, measured in Celsius  hum: Humidity %  windspeed: Wind speed, measured in km per hour  cnt: count of total bike rentals that day Read the bikeshare.csv data into R. Plot the marginal distribution for the count of bike rentals and the conditional count distribution given the broad weather situation (weathersit). a-1. Use a histogram to plot the marginal distribution for the count of bike rentals. What is the shape of the distribution? a. skewed left b. fairly symmetric