Introduction to Statistic questions answered
Math&146 | Optional Project DUE: Monday, March 16th Name: _________________________________________________

Analyzing AirBnB Data

Introduction In the module in Canvas where you found this document, you will also find the file “AirBnBData.xlsx”. You will need that dataset to complete this project. Follow the instructions on this worksheet to complete the project. The project is due Monday, August 17th. It is optional, but can improve your exam score if you choose to complete it.

The Data AirBnB allows much of their data to be viewed publicly and the website “Inside AirBnB” (http://insideairbnb.com/index.html) does a good job compiling and releasing the data for many cities around the world. One city they release data for is Seattle. The data you find in this module is a random selection of 100 AirBnB Listings in Seattle in 2019 (there are over 8000 listings, so I went ahead and selected 100 for you).

I have cleaned up the data a bit, and labeled the variables measured. There are a total of eight variables measured for each listing, and they are as follows:

• Neighborhood: Describes the neighborhood within Seattle that a listing is located in. • Room Type: Describes the type of rental, which can either be a single room within a larger dwelling, or an entire

house/apartment. • Price/Night: Describes the average price charged per night for each listing. • Min. Nights: Describes the minimum nights required to make a reservation at each listing. Some rentals require

guests to reserve more than one night at a time. • # Review: Describes how many total reviews have been submitted for each listing. • Reviews/Mo.: Describes, on average, how many reviews were submitted each month for each listing. • # Host Listing: Describes how many listings each listing person hosts on AirBnB. Some people/companies list

more than one rental on AirBnB. • Days Available: Describes how many days each listing was available to be rented out during the year of 2019.

You will notice at the end of each column for all of the quantitative (numerical) variables I have provided summary statistics consisting of the: sample mean, sample standard deviation, and the five-number summary. You will need these values to complete this project.

The Project Answer the following questions and fill in each part to perform a robust data analysis using the skills you have developed throughout the first 8 modules of this course. Use complete sentenced when appropriate, and show all work to receive full credit.

1.) [10 pts] The data set contains both qualitative (categorical) and quantitative (numerical) variables. Identify each of the eight variables from the dataset as either qualitative or quantitative by listing each one under one of the categories below.

Qualitative Variables Quantitative Variables

2.) For this part, choose only one of the qualitative variables from the dataset to analyze.

a.) [1 pt] Which of the qualitative variables did you choose to analyze?

b.) [4 pts] Create a relative frequency distribution that summarizes the qualitative variable.

c.) [5 pts] Create an appropriate graph that displays the qualitative variable you chose to analyze. Graphing by hand is just fine, but you are welcome to use any software you would like to create the graph.

d.) [2 pts] For the qualitative variable you chose pick one of the possible values of the variable and calculate ??, the sample proportion of listings that have the value you chose (Example: if I was analyzing the “Neighborhood” variable, I could calculate the proportion of listings that were in Ballard). Hint: You already calculate sample proportions in part (b.)!

Value you picked: ____________________ ?? =_______________

e.) [10 pts] Use your ?? from part (d.) and calculate a 90% confidence interval for the true proportion of listings that have the value you chose (Example: if I calculated the proportion of listings in Ballard in part (d.), I would be constructing a 90% confidence interval for the true proportion of listings that were in Ballard).

f.) [3 pts] Interpret your 90% confidence interval

3.) For this part, choose only one of the quantitative variables from the dataset to analyze.

a.) [1 pt] Which of the quantitative variables did you choose to analyze?

b.) [4 pts] Create a frequency distribution/table that summarizes the quantitative variable.

c.) [5 pts] Create a histogram that displays the qualitative variable you chose to analyze. Graphing by hand is just fine, but you are welcome to use any software you would like to create the graph.

d.) [2 pts] Based on your graph, how would you describe the shape of the distribution of the variable?

e.) [3 pts] For the variable you selected, compare the mean to the median (both found at the bottom of the dataset). How do they compare? Does this seem reasonable given the shape of the dataset you found in your graph?

(3 continued…)

f.) [5 pts] For the quantitative variable you selected, use the 5-Number Summary (found at the bottom of the dataset) to test for any outliers. Are there any outliers within the dataset for the variable you chose to analyze?

g.) [10 pts] For the quantitative variable you have chosen to analyze, use the sample mean and sample standard deviation (both found at the bottom of the dataset) to construct a 95% confidence interval for the true mean value of the variable.

h.) [3 pts] Interpret your 95% confidence interval.

i.) [2 pts] Did you find anything particularly interesting or noteworthy about the variable you chose to analyze? What kind of “takeaways” could you give someone about the quantitative variable you chose to analyze if they were not familiar with the dataset or statistics in general?