Learning objectives include:

â€¢ Explain the concept of linear regression
â€¢ Scatter plots
â€¢ Measure SSE, MSE, SST, R-squared and adjusted R-squared in regression
â€¢ Explain Correlation and Multicollinearity in linear regression
â€¢ Use R to perform single and multiple linear regression
â€¢ Interpret the results of regression analysis in R

Assignment Instructions:

Capital Bikeshare is a bicycle sharing system that serves multiple areas such as Washington DC, Arlington, Alexandria and Fairfax, VA and Montgomery County, MD. "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"

"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."

In this assignment, you need to predict the Bikeshare demand in Washington DC based on the weather data. The dataset you need to use can be found here. The variables in this dataset are:
Variable Name Description
instant row number
dteday date
season 1 = spring, 2 = summer, 3 = fall, 4 = winter
yr 0 = first year, 2 = second year
mnth month
holiday 0 = not holiday, 1 = holiday
weekday number of the day in week
workingday 0 = not working day, 1 = working day
weathersit: 1: Clear, Few clouds, Partly cloudy, Partly cloudy, 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist, 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds, 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog
temp temperature in Celsius
atemp "feels like" temperature in Celsius
hum relative humidity
windspeed wind speed
casual number of non-registered user rentals initiated
registered number of registered user rentals initiated
cnt number of total rentals (demand)

Note that you need to split your dataset into two parts of train and test. Keep 70% of the rows for train and use the rest for test. To do so, open the data in Excel and copy the train and test sections into separate CSV spreadsheets. Then save them as CSV file. Try to cover these points:

1- Is the demand histogram different for various days of the week? how about different seasons and months?
2- How does the holidays affect the demand?
1- Come up with a model that best predicts the demand (cnt column) and explain all the steps you took for building it. Do NOT use casual and registered columns
2- Are the independent variables you used correlated? Explain
3- Do you observe any potential for encountering the multicollinearity problem? if yes, explain
4- How did you avoid multicollinearity?
5- In your model, what is SSE, SST, R-squared and adjusted R-squared for train and test sets?
6- How did you build and improve the model?
7- Interpret the results you got from R.
8- If you were a decision maker in Bikeshare company, would you use the model you built? why and how?

Learning objectives include: â€¢ Explain the concept of linear r...