You have a data set with the following variables: Househol…

You have a data set with the following variables: Household income, which runs from $30,000 to $120,000 Number of children in family, from 0 to 5 Number of years in current location, from 0 to 20+ Whether they rent or own, as a text field Number of miles driven per year, from 0 to 50,000 Population in their location, from 100 to 1 million + Where possible, you have standardized the variables, and you have recoded the rent/own text field into a binary 0 (for rent) and 1 (for own). You want to run a k-means algorithm on this entire data set, to try to determine different demographic niches. For example, you may want to separate out urban apartment-dwellers from rural retirees. Is it possible to run k-means clustering on all of these data fields? Say you have loaded the standardized variables above into columns 1 through 6 in a data frame called responses. Let’s say you want to try for 7 clusters.  In particular, can you do something like this? > fit

Choose ONE of the following to respond to.  1. Tetrodotoxin…

Choose ONE of the following to respond to.  1. Tetrodotoxin (TTX) is a potentially fatal neurotoxin produced by cone snails, pufferfish, and blue-ringed octopuses. TTX acts by blocking voltage-gated sodium channels, causing them to remain closed. What effect would TTX poisoning have on a neuron, and why would these effects be fatal?   OR   2. Parkinson’s disease is a neurological disorder in which cells in the basal ganglia die off, resulting in impaired motor function. A secondary result of Parkinson’s is that neurons that produce norepinephrine also die off.  What part of the nervous system would be most affected by the loss of norepinephrine, and what would be the effects?