Airbnb is an online marketplace used by many travelers when booking lodging all over the world. The company serves as a broker, receiving commissions from each booking. Guests have the opportunity to give a review of their host once their lodging is complete. We randomly selected 337 Airbnb listings from two popular cities in the U.S. (Los Angeles and Manhattan) and recorded many variables including price per night (in dollars) and number of reviews (count). Using this data, we would like to assess if the population mean number of reviews for Airbnb listings in Los Angeles (group 1) is higher than the population mean number of reviews for Airbnb listings in Manhattan (group 2) (using a 5% significance level). The subquestions include R output that was generated using a data set called AirBnB which contains the following variables among others: (1) id = ID number for the 337 Airbnb listings in the study (2) city = city of the selected listing (Los Angeles / Manhattan) (3) price = price per night (in dollars) (4) number_of_reviews = number of reviews per listing To check our assumptions when learning about 1-U2, we have to produce plots. Here's a side-by-side boxplot of number of reviews by city: Boxplots of Number of Reviews by City 400 300 200 Number of Reviews 100 Los Angeles Manhattan City Based on the side-by-side plot, select the appropriate statement(s). From the side-by-side boxplot, we can compare the two sample means and conclude that the sample mean number of reviews for AirBob listings in Los Angeles is higher than the sample mean number of reviews for listings in Manhattan. O From the side-by-side boxplot, we are able to compare the overall range for the number of reviews per AirBnb listing across these two cities (Los Angeles versus Manhattan) and say that the overall range for the two samples is similar. From the side-by-side boxplot, we are able to compare the two sample standard deviations and conclude that the standard deviation for the number of reviews per AirBob listing across these two cities (Los Angeles versus Manhattan) is similar. From the side-by-side boxplot, we are able to compare the two sample IQRs and conclude that the IQR for the number of reviews per AirBob listing across these two cities (Los Angeles versus Manhattan) is similar. At a 5% significance level, select the appropriate evaluation of the p-value and corresponding conclusion. O Based on this data, we reject Ho. We do not have enough evidence to suggest that, on average, the number of reviews for all AirBnB listings in Los Angeles is higher than that of Manhattan. Based on this data, we reject Ho. We have enough evidence to suggest that, on average, the number of reviews for all AirBnB listings in Los Angeles is higher than that of Manhattan. Based on this data, we cannot reject Ho. We do not have enough evidence to suggest that, on average, the number of reviews for all AirBnB listings in Los Angeles is higher than that of Manhattan. O Based on this data, we cannot reject Ho- We have enough evidence to suggest that, on average, the number of reviews for all AirBnB listings in Los Angeles is higher than that of Manhattan. Provide an interpretation of the p-value in context. No answer entered. Click above to enter an