In this assignment you will be required to create two regression models using bike sharing data, interpret the diagnostics associated with those models, and create some predictions. You will first need to download the data, familiarize yourself with the data, and perform some data preparation tasks.
1. Create a new markdown file by selecting either R Notebook or R Markdown from the dropdown menu in the top, left-hand corner of RStudio. Name it using the following convention: [GRADEBOOK_NUMBER]_[LAST_NAME]_[FIRST_NAME].Rmd. (10%)
2. Accessing data – Download the archive from this link.
a. The folder will contain two datafiles hour.csv and day.csv. You will use day.csv for this assignment. The readme file in the folder has a description of the data, which you are encouraged to read so that you can successfully interpret the analytic results.
b. There are 16 columns in the dataset. You will need to use columns: dteday, temp, and cnt. cnt is the outcome variable, or dependent variable.
3. Presenting data – Use a code chunk to report a summary of the data. (5%)
4. Preparing data – Extract the month names from the dteday column using lubridate package, save them in a new column month_name, which has a chr data type. (10%)
a. Convert the column dteday into a date type using the lubridate package.
b. Use an appropriate lubridate function to extract the month from the dteday column and save it as month_name.
c. Remember the default function will extract the month numbers and not the month name. You will find a function argument to extract month names/labels.
d. Also, make sure to convert the column month_name to a character data type. If you want, you may convert it back to a factor. Do not keep it as an ordered factor data type.
e. You will use these three columns for further analysis: month_name, temp and cnt.
5. Running regression models – You will run one simple linear regression model, Model1, and one multiple regression model, Model2, as described below.
a. Model1:
i. Use a code chunk to run a simple linear regression model where the dependent variable is cnt and the independent variable is month_name and save the model as Model1. (10%)
ii. Use a code chunk to report the summary for Model1. (5%)
iii. Below the code chunk, use regular text to comment on the R-squared. (8%)
iv. With either a code chunk or regular text, use the coefficient estimates from Model1 to report the predicted cnt for the months of January and June. (10%)
b. Model2:
i. Use a code chunk to run a multiple linear regression model where the dependent variable is cnt and the independent variables are temp and month_name. Save the model as Model2. (10%)
ii. Use a code chunk to report the summary for Model2. (5%)
iii. Below the code chunk use regular text to comment on the R-squared. Please explain why the R-squared is different from the two simple regression models. (8%)
iv. Compare the coefficient estimates for the month_nameJan variable in Model1 and Model2. (2%)
v. With regular text explain why the coefficient estimates are different. (7%)
vi. With either a code chunk or regular text, use the coefficient estimates from Model2 to report the predicted cnt for the month of January when the temperature is 0.25. (10%)
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