Bike sharing demand. The timely availability and accessibility of rental bikes to the public is important as it lessens the waiting time. A group of researchers employed data mining techniques to predict bike sharing demand in the metropolitan city of Seoul and published their results in Computer Communications (Vol. 153, 2020). Using the partial data provided by the research for 30 November 2018, we are going to predict the hourly bike demand based on humidity. Therefore, use y = rented bike count and x = humidity (%).

a. Access the data, saved in the file, and construct a scatterplot. Do you observe a trend?

b. Fit the simple linear regression model, E1y2 = b0 + b1x, to the data and obtain estimates of the y-intercept and slope.

c. Practically interpret the estimated y-intercept and estimated slope.