A nurse is suctioning the endotracheal tube of a client who is on a ventilator. The client’s heart rate increases from 86/min to 110/min and becomes irregular. The client’s pulse ox drops from 99% to 88%. Which of the following actions should the nurse take?

A client who has a history of myocardial infarction (MI) is prescribed aspirin 81mg.  The nurse recognizes that the aspirin is given due to which of the following actions of the medication?

Education for calcium channel blockers include (select all that apply):

What is the lift associated with a trained Classifiation Tree model? How would you calculate it?  What does the lift of a model tell you? How would you use it when making decisions about which model instance to use in answering a business question?

This refers to the Camping affinity analysis script and output.What is the very best rule to use? Give the rule number, tell me the rule text, what it says in plain English, and give me an outline of your reasoning. Make sure you tell me which areas of the output you used to derive your answer.

What is unsupervised learning?

You have a data set of 1000 records, representing the income and age data of 1000 different survey respondents. You have standardized your income and age variables.If you run a k-means algorithm with k=10 on your standardized data, what will your output be?

We evaluate the quality of a classification model (note: not a classification decision tree model) using

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

You are running the website for a camping store, and you’ve downloaded transaction shopping cart data.You have run some affinity analysis to determine which items might be most highly associated with other items. Attached is the R script you wrote, and then the output when you ran it on your camping data. (The camping data field names sometimes have a ___ at the end. That’s just to keep them all the same length; please ignore those in your analysis.)You are welcome to download these files to your desktop and use Notepad, Excel, or any other program you like to view them. Please delete them at the end of your exam.Here is the script:Camping_Script.R Here is the output: Camping_Script_Output.txt