Step $2$: Identify the Dependent $($Outcome$)$ Variable

Because we are trying to determine the appropriate nightly rental rate, price is our dependent $($outcome$)$ variable.

Step $3$: Identify Potential Independent $($or Explanatory$)$ Variables

The first step in determining the best data to use is to consider the various explanatory, or independent, variables. Which variables a likely to explain the price charged for an Airbnb rental in New York City? Recall that the price of one night's rental is the dependen $($outcome$)$ variable that we are trying to explain $($or predict$).$ As an analyst, it is your job to identify the variables that are most likel$)$ explain the price. Look over the data dictionary to determine which are likely to be the best candidate explanatory variables.

Deliverable $1$:

Once you have identified the variables that may explain the price of an Airbnb rental, prepare a document $($in Microsoft Word$)$ save it as "Project $1,$ Deliverable $1$: Airbnb Price Analysis First Name and Last Name" $($inserting your first and last name$).$ Title first section of the document "Dependent and Independent Variables". Next, within the document, identify and type the names the dependent $($outcome$)$ variable and at least five proposed independent variables that you think explain price.

Step $4$: Clean and Transform the Data in Preparation for Analysis

To prepare the data for analysis, complete this four$-$step process:

Because some of the listings have no date of last review, they are missing data. To remedy this problem and still be able to inclu listing in the analysis, replace date of last$\_$review with March $28,2011.$ Color the font in red so you know you inserted those da