Consider the 4 numbers 31, 33, 35, 37.  Assume I think of a particular permutation  (say 37, 33, 31, 35), and that you must find that permutation by asking questions that are answered by “yes” or “no”. To solve that task, what is the smallest number of questions that will always suffice?

Consider the 4 numbers 31, 33, 35, 37.  Assume I think of a particular permutation  (say 37, 33, 31, 35), and that you must find that permutation by asking questions that are answered by “yes” or “no”. To solve that task, what is the smallest number of questions that will always suffice?

A confusion matrix is used for

[Z Corp] While you are working on your plan, you sent your CEO two articles to read as background: Gary Hamel’s Brining Silicon Valley Inside and Gary Pisano’s The Hard Truth about Innovative Cultures.  He has just dropped by your office and excitedly exclaimed, “these are great articles, but they both can’t be right. Which is it?!  Should we be as ‘freewheeling’ as Silicon Valley OR as ‘disciplined’ as Pisano argues?” Your CEO reminds you of the Uber case.  He points to Uber’s leadership acting quickly, ‘breaking’ some rules, and quickly creating a new innovative business for themselves.  He ended the story about Uber with the following question, “does that example prove that the Silicon Valley model is the future of innovation?” a) How do you respond?  Is one author right, and the other one wrong?  Who is right?  b) How can you reconcile the Hamel and Pisano perspectives?  Can they both be relevant for Z Corp?  How?

One of the following groups is arguably the most diverse metabolically because it contains species that get their carbon and energy in the greatest variety of ways – it includes species that are chemoorganoautotrophs, chemoorganoheterotrophs, chemolithoautotrophs, chemolithoheterotrophs, photoautotrophs, and photoheterotrophs. Which group includes different species that fit into each of the above categories for how they obtain carbon and energy?

A company is interested in understanding the factors that drive purchase on their website.  They ran a test where they collected data from 2500 visitors to their website and observed if a purchase was made or not.  This analysis includes the following variables:  Customer ID: Unique identifier for each customer Purchase: 1 = purchased; 0 = did not purchase Female: 1 = Female; 0 = Male Paid Ad: 1 = Arrived on webpage via a paid advertisement; 0 = came from somewhere else Urban: 1 = livesin an urban location; 0 = does not To understand the relationship between these variables and purchase they ran a regression where “Purchase” was the dependent variable.  Results appear in the table below.  Use these results to answer the following questions: By how much does the probability of purchase change if the individual is female? 

We shall consider the general method, employed by an adversary,  to prove that no algorithm can always decide a given problem X using less than M questions. For that purpose, the adversary maintains Q, and R_1 … R_M, such that : 1. X(Q) is false2. X(R_i) is true for all i \in 1 … M and such that after k questions from the algorithm:3. Q is consistent with all k answers from the adversary 4. for all i in 1 … M, except for at most k such, it holds that R_i is consistent with all k answers from the adversary.For each declaration from the algorithm, how should the adversary respond?

What is the average probability of purchase for all people that come to the website (independent of the specific landing page)?

A company is interested in understanding the factors that drive purchase on their website.  They ran a test where they collected data from 2500 visitors to their website and observed if a purchase was made or not.  This analysis includes the following variables:  Customer ID: Unique identifier for each customer Purchase: 1 = purchased; 0 = did not purchase Female: 1 = Female; 0 = Male Paid Ad: 1 = Arrived on webpage via a paid advertisement; 0 = came from somewhere else Urban: 1 = livesin an urban location; 0 = does not To understand the relationship between these variables and purchase they ran a regression where “Purchase” was the dependent variable.  Results appear in the table below.  Use these results to answer the following questions: By how much does the probability of purchase change if the individual is female? 

What the the EMV for this customer?