anchor.diff:  
   One approach would be to aggregate the data from each city and compare the *mean* room rates. This approach will
   give us some useful information, but the mean is a summary statistic which loses a significant amount of information
  -about the data. For example we don't have an understanding of how the distribution of room rates is effecting the
  +about the data. For example we don't have an understanding of how the distribution of room rates is impacting the
   mean.
   
  -The *median* room rate provides another interesting data point but it's still not the entire picture. It sill just
  +The *median* room rate provides another interesting data point but it's still not the entire picture. It's sill just
   one point of reference.
   
   Is there a way that we can compare the markets without losing valuable information in the data?
   
   === K Nearest Neighbor
   
  -The use case we're thinking about can often be approached using a k Nearest Neighbor (knn) algorithm.
  +The use case we're reasoning about can often be approached using a K Nearest Neighbor (knn) algorithm.
   
   With knn we use a *distance* measure to compare vectors of data to find the k nearest neighbors to
   a specific vector.
  
positive.code: We have an existing hotel in *cityA* that is very profitable.
  We are contemplating opening up a new hotel in a different city.
  We're considering 4 different cities: *cityB*, *cityC*, *cityD*, *cityE*.
  We'd like to open a hotel in a city that has a very similar room rate to *cityA*.
  
  How do we determine which of the 4 cities we're considering has the most similar room rates to *cityA*
  
  === The Data
  
  
negative.code: 
  === Vectors
  
  But how to create the vectors from a our data set? Remember we have un-aggregated room rates from each of the cities.
  How can we vectorize the data so it can be compared using the `distance` function.
  
  We have a Streaming Expression that can retrieve a *random sample* from each of the cities. The name of this
  expression is `random`. So we could take a random sample of 1000 room rates from each of the five cities.
  
  But random vectors of room rates are not comparable because the distance algorithm compares values at each index
  in the vector. How can make these vectors comparable?
  
  We can make them comparable by *sorting* them. Then as the distance algorithm moves along the vectors it will be
  comparing room rates from lowest to highest in both cities.
  
  === The code
  
  