You probably regard your university education as an investment. You spend your valuable time, effort, and tuition fees and in return you obtain a degree. The provincial and federal governements also regard their funding of universities to be an investment. But is the investment equally effective in producing graduates across all provinces? The data bellow indicates the number of graduates at the bachelors, masters and doctorate levels and funding from four sources: Investment of university endowment funds, provincial funding, federal funding, tuition fees. Can we estimate the number of graduates from the level of these sources of funding? Does population size impact the equation? What other factors could influence results?

The Problem

Facebook has long conducted digital experiments on various aspects of its website. For example, just before the 2012 election, the company conducted an experiment on the News Feeds of nearly 2 million users so that they would see more “hard news” shared by their friends. In the experiment, news articles that Facebook users' friends had posted appeared higher in their News feeds. Facebook claimed that the news stories being shared were general in nature and not political. The stories originated from a list of 100 top media outlets from the New York Times to Fox News. Industry analysts claim that the change may have boosted voter turnout by as much as 3 percent.

Next, Facebook decided to conduct a different kind of experiment that analyzed human emotions. The social network has observed that people's friends often produce more News Feed content than they can read. As a result, Facebook filters that content with algorithms to show users the most relevant and engaging content. For one week in 2012, Facebook changed the algorithms it uses to determine which status updates appeared in the News Feed of 689,000 randomly selected users (about 1 of every 2,500 Facebook users). In this experiment, the algorithm filtered content based on its emotional content. Specifically, it identified a post as “positive” or “negative” if it used at least one word previously identified by Facebook as positive or negative. In essence, Facebook altered the regular news feeds of those users, showing one set of users happy, positive posts while displaying dreary, negative posts to another set.

Previous studies had found that the largely positive content that Facebook tends to feature has made users feel bitter and resentful. The rationale for this finding is that users become jealous over the success of other people, and they feel they are not “keeping up.” Those studies, therefore, predicted that reducing the positive content in users' feeds might actually make users less unhappy. Clearly, Facebook would want to determine what types of feeds will make users spend more time on its site rather than leave the site in disgust or despair. Consequently, Facebook designed its experiment to investigate the theory that seeing friends' positive content makes users sad.

The researchers—one from Facebook and two from academia—conducted two experiments, with a total of four groups of users. In the first experiment, they reduced the positive content of News Feeds; in the second experiment, they reduced the negative content. In both experiments, these treatment conditions were compared with control groups in which News Feeds were randomly filtered without regard to positive or negative content.

The results were interesting. When users received more positive content in their News Feed, a slightly larger percentage of words in their status updates were positive, and a smaller percentage were negative. When positivity was reduced, the opposite pattern occurred. The researchers concluded that the emotions expressed by friends, through online social networks, elicited similar emotions from users. Interestingly, the results of this experiment did not support the hypothesis that seeing friends' positive content made users sad.

Significantly, Facebook had not explicitly informed the participants that they were being studied. In fact, few users were aware of this fact until the study was published in a paper titled “Experimental evidence of massive-scale emotional contagion through social networks” in the prominent scientific journal Proceedings of the National Academy of Sciences. At that point, many people became upset that Facebook had secretly performed a digital experiment on its users. The only warning that Facebook had issued was buried in the social network's one-click user agreement. Facebook's Data Use Policy states that Facebook “may use the information we receive about you . . . for internal operations, including troubleshooting, data analysis, testing, research, and service improvement.” This policy led to charges that the experiment violated laws designed to protect human research subjects.

Some lawyers urged legal action against Facebook over its experiment. While acknowledging the potential benefits of digital research, they asserted that online research such as the Facebook experiment should be held to some of the same standards required of government-sponsored clinical trials. What makes the Facebook experiment unethical, in their opinion, was that the company did not explicitly seek subjects' approval at the time of the study.

Some industry analysts challenged this contention, arguing that clinical research requirements should not be imposed on Facebook. They placed Facebook's experiment in the context of manipulative advertising—on the web and elsewhere—and news outlets that select stories and write headlines in a way that is designed to exploit emotional responses by their readers.

On July 3, 2014, the privacy group Electronic Privacy Information Center filed a formal complaint with the Federal Trade Commission claiming that Facebook had broken the law when it conducted the experiment without the participants' knowledge or consent. EPIC alleged that Facebook had deceived its users by secretly conducting a psychological experiment on their emotions.

Facebook's Response

Facebook Chief Operating Officer Sheryl Sandberg defended the experiment on the grounds that it was a part of ongoing research that companies perform to test different products. She conceded, however, that the experiment had been poorly communicated, and she formally apologized. The lead author of the Facebook experiment also stated, “I can understand why some people have concerns about it (the study), and my co-authors and I are very sorry for the way the (academic) paper described the research and any anxiety it caused.”

For its part, Facebook conceded that the experiment should have been “done differently,” and it announced a new set of guidelines for how the social network will approach future research studies. Specifically, research that relates to content that “may be considered deeply personal” will go through an enhanced review process before it can begin.

The Results

At Facebook, the experiments continue. In May 2015, the social network launched an experiment called Instant Articles in partnership with nine major international newspapers. This new feature allowed Facebook to host articles from various news publications directly on its platform, an option that the social network claims will generate a richer multimedia experience and faster page-loading times.

The following month Facebook began experimenting with its Trending sidebar, which groups news and hashtags into five categories among which users can toggle: all news, politics, science and technology, sports, and entertainment. Facebook maintained that the objective is to help users discover which topics they may be interested in. This experiment could be part of Facebook's new effort to become a one-stop news distributor, an approach that would encourage users to remain on the site for as long as possible.

A 2016 report asserts that Facebook's list of top trending topics is not quite objective. For example, one source stated that Facebook's news curators routinely excluded trending stories from conservative media sites from the trending section. Facebook strongly denied the claim.

Questions

1. Discuss the ethicality and legality of Facebook's experiment with human emotions.
2. Was Facebook's response to criticism concerning that experiment adequate? Why or why not?
3. Consider the experiments that Facebook conducted in May and June 2015. Is there a difference between these two experiments and Facebook's experiment with human emotions? Why or why not?
4. Should the law require companies to inform their users every time they conduct experiments? Why or why not?

In lecture we discussed Milgram’s 1967 experiment; he picked 300 people at random in Nebraska and asked them to send a letter to a stockbroker in Boston, by way of relaying the letter through a chain of people. The rule is that every person has to know the next person they are sending the 1 letter to on a first name basis. He found that on average each letter went through the hands of 6.4 people before reaching the stockbroker. This is where the expression ”six degrees of separation” comes from. When you tell your friend Dirk about this experiment he says he is not surprised. Dirk says that often, when he tells something to a friend, a couple of days later he hears back the same information from someone else! You decide to test whether the six degrees of separation principle can also be applied to oneself. Let’s imagine that “friendships” on Facebook are a good representation of Milgram’s rule for being on first-name terms with somebody. Assume you are given access to all of the friendship links on Facebook as a graph (where nodes are accounts, and links are “friends”). Design an algorithm to determine if there is a chain of at most 7 friends (because the average number in Milgram’s experiment was 6.4) such that Dirk is friends with both the first and the last person. You may assume that the graph is undirected. For full credit your algorithm should run in time O(m + n) (where n and m are the number of nodes and edges, respectively).

Hint: MODIFY/ USE BFS please helP!

A = [4, 5, 9]
B = [-4, 5, -7]
C = [2, -7, -8, 5]
D = [1, -9, 5, -3]
E = [3, 3, -1]

Uz = 1/|z| ^z
d(X,Y) = (Rθ) d = diameter R = Radius θ = Theta

Find

a. Uc
b. d (D, C)
c. Let P = B + 3E, UP =
d. A x B
e. 3B x E
f. C x D

You have three plants that produce a certain type of boats. The capacity for next month is 38 in San Diego, 45 in Santa

Ana, and 58 in San Jose. Production cost per boat is $1,065 in San Diego, $1,005 in Santa Ana, and $975 in San Jose.

Demand for next month is 42 in Newport Beach, 33 in Long Beach, 14 in Ventura, 10 in San Luis Obispo, and 22 in San

Francisco. The shipping costs per boat are summarized in the following table:

Develop a production and shipping schedule that minimizes the total cost of production and shipping while satisfying all

the demand.

A consumer preference study compares the effects of three different bottle designs (A, B, and C) on sales of a popular fabric softener. A completely randomized design is employed. Specifically, 15 supermarkets of equal sales potential are selected, and 5 of these supermarkets are randomly assigned to each bottle design. The number of bottles sold in 24 hours at each supermarket is recorded. The data obtained are displayed in the following table.

(a) Test the null hypothesis that μ_A, μ_B, and μ_C are equal by setting α = .05. Based on this test, can we conclude that bottle designs A, B, and C have different effects on mean daily sales? (Round your answers to 2 decimal places.)

F=______ ; P-Value = ______; (reject/do not reject) H0: bottle design (Does/Does not) have an impact on sales.

(b) Consider the pairwise differences μ_B – μ_A, μ_C – μ_A , and μ_C – μ_B. Find a point estimate of and a Tukey simultaneous 95 percent confidence interval for each pairwise difference. Interpret the results in practical terms. Which bottle design maximizes mean daily sales? (Round your answers to 2 decimal places. Negative amounts should be indicated by a minus sign.)

(c) Find a 95 percent confidence interval for each of the treatment means μ_A, μ_B, and μ_C. Interpret these intervals. (Round your answers to 2 decimal places. Negative amounts should be indicated by a minus sign.)

Let T : V → V be a linear map. A vector v ∈ V is called a fixed point of T if Tv = v. For example, 0 is a fixed point for every linear map T. Show that 1 is an eigenvalue of T if and only if T has nonzero fixed points, and that these nonzero fixed points are the eigenvectors of T corresponding to eigenvalue 1

The urgent care manager must resubmit her budget based on 2018 projected visits. The 2018 budget should be based on actual expenses and visits incurred during the first seven months of 2017. Cost standards (2017) and budget standards (2018) must be prepared for all variable costs (indicated by a V behind the object code description). All fixed costs (F) should be projected using the incremental budgeting methodology. Projected visits is 2018 is 52,560. Salary and fringe benefit (object codes 4000-4199) increases for all personnel should be estimated at 4.0% except for health insurance (4120) which is expected to increase by 8%. Input price increases for supplies and other costs (4200-4960) should be increased by 3% except for medications, 5% increase and profession insurance, 10%. All increases are effective January 1, 2015. The budget report, BudgetCh06, Problem 6.1 tab, shows the urgent care centers expenses for 2016 and the actual and budgeted expenses for the first seven months of 2017. The CEO has requested all department heads submit their 2015 operating budget by Wednesday, March 21, 2014. The 2018 fiscal year begins January 1, 2018.

B. Compare and contrast the incremental budget (prepared in homework 4) and the flexible budget.

C. Assume 2015 has been completed and actual output is 53,011, restate (or flex) the budget, what is the final budget the manager will be accountable to?

D. Assume 2015 has been completed and actual output is 51,543, restate the budget what is the final budget the manager will be accountable to?

Exp 7. Preparation of Copper (I) Chloride Name ________________________ Experiment replacement assignment. The process of preparing copper (I) chloride, CuCl, from metallic copper consists of the reactions listed below:

Rxn 1. Cu (s) + 4H+ (aq) + 2NO3 – (aq) --> Cu2+ (aq) + 2NO2 (g) + 2H2O(l)

Rxn 2. omitted, as it does not involve copper

Rxn 3. Cu2+ (aq) + CO3 2- (aq) ---> CuCO3 (s)

Rxn.4 CuCO3 (s) + 2H+ (aq) + 4Cl– (aq) ---> CuCl4 2- (aq) + CO2 (g) + H2O (l)

Rxn 5. CuCl4 2- (aq) + Cu (s) + 4Cl– (aq) ---> 2 CuCl4 3- (aq) additional copper

Rxn 6. CuCl4 3- (aq) --> CuCl (s) + 3Cl - (aq)

1. Analyze all reactions and “follow” the initial cupper as it changes into different forms until the final product, CuCl, is made. List all of these forms, in the proper order; include the physical states.

2. Metallic copper used in Rxn1 is the limiting reactant in this process. All other reactants in all steps are available in sufficient amounts. If 2.00 g of copper is used as the starting material, how many grams of the final product, CuCl, can be made?

3. If the yield of this overall process is 75.0%, what mass, in grams, of CuCl amount is produced when from 2.00 g of the initial Cu?

4. Nitrogen dioxide is the toxic product of Rxn 1. Calculate what volume, in L, of NO2 is produced by complete reaction of 2.00g of copper if the gas is collected at the pressure of 745.0 mm Hg and temperature of 22.0 o C.

5. Another way to write a balanced equation for reaction 1 is: Cu (s) + 4 HNO3 (aq) --> Cu(NO3)2 (aq) + 2 NO2 (g) + 2 H2O (l) If 55.0 mL of the 3.0 M nitric acid, HNO3, and 2.00 g of Cu were used, which reactant remained after the reaction was complete?