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 the Bikesharing dataset for this assignment.

e. Are the coefficient(s) statistically significant? Explain.

f. Which factors have a positive effect on bike rentals and which ones have a negative effect? If there is a single input variable, just answer for that variable.

g. What portion of the variance in the total number of rentals is explained by your model?

h. Is the overall model statistically significant? Explain.

Get the data in the following Google share File:

https://docs.google.com/spreadsheets/d/1xxdzyzLe8fxNRWA_iecNqtic4LPk-YDezMD-LkE_qgI/edit?usp=sharing

You don't have to answer the following questions but it helps you to understand the questions above. These questions are the 1st part hw question 1: Build a linear regression model to explain the total number of rentals using windspeed. Question 1: Build a linear regression model to explain the total number of rentals using windspeed. Specifically, answer the following questions. a. What is the meaning of each row (observation) in this dataset? b. How many observations and variables are there in this dataset? c. Provide a scatterplot that

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