A screening program for neuroblastoma (a type of cancer) was undertaken in Germany among children born between November 1, 1993, and June 30, 2000, who were between 9 and 18 months of age between May 1995 and April 2000. A total of 1,475,773 children participated in the screening program. Of whom 204 were diagnosed between 12 and 60 months of age. The researchers expected the incidence rate of neuroblastoma to be 7.3 per 100,000 children during this period in the absence of screening. We wish to test if the number of cases detected by the screening program is significantly greater than expected.

a) Write hypotheses to test this claim. Explain why you should use a one sided alternative.

b) You may assume any necessary conditions have been met. Perform your test.

c) Do you think that the number of cases detected by the screening program is significantly greater than expected? Explain.

d) Give a 95% confidence interval for the incidence rate of neuroblastoma in the screened population.

e) Express your confidence interval from part d) as (p1, p2), where p1 and p2 are in the units of number of cases per 100,000 children.

specifically need help with question e

The town of Cypress Creek is preparing to go to war against the American government. To do this, it is building a giant satellite laser! To build the laser, the government of the town will resort to taxation to fund its expenditure. The initial economy of Cypress Creek can be expressed by the following agents:

Consumers, C = 25 + 0.95(Y-T)

Output, Y = 5000

Government expenditures, G = 2000

Taxation, T = 2000

Investors, I = 750-125r

Markets are fully competitive and the equilibrium condition for markets are:

Goods and service market: Y =C + I + G

Financial market: I = S

When it builds the Satellite, government and taxation change to

Government expenditures, G = 4000

Taxation, T = 4000

Hank Scorpio makes another announcement "People of North Haverbrook! We must all work together in this to crush the American Government - I implore you to save you wages! Don't spend!"

m) [2 points] by how much would consumers need to reduce their Marginal propensity to consume

(MPC) such that the market clearing interest rate does not change?

n) [2 points] Who will end up paying the burden of this project? (consumers or investors? And by

how much?)

Consider the following database schema:

LIKE(person, sport),

PRACTICE(person, sport),

where person and sport are keys in both tables. The table LIKE gives the sports a person likes, the table PRACTICE gives the sports a person practices. We assume that a person likes at least one sport and practices at least one sport. We assume also that a person does not like a sport if the sport is not listed among the sports that person likes

Express the following queries in mySQL preferred

1. List the people who practice at least one sport they do not like

2. List the people who like all the sports they practice

(DO NOT COPY ANSWER FROM PREVIOUS QUESTION LIKE THIS THEY ARE NOT WORKING/WRONG)

Consider the following database schema:

LIKE(person, sport),

PRACTICE(person, sport),

where person and sport are keys in both tables. The table LIKE gives the sports a person likes, the table PRACTICE gives the sports a person practices. We assume that a person likes at least one sport and practices at least one sport. We assume also that a person does not like a sport if the sport is not listed among the sports that a person likes


Express the following queries in Calculus

List the people who practice at list one sport they like
List the people who practice at least one sport they do not like
List pairs of people who practice at least one common sport
List the people who like all the sports they practice
List the people who practice all the sports they like
List the people who practice all the sports John likes

Consider the following database schema:

LIKE(person, sport),

PRACTICE(person, sport),

where person and sport are keys in both tables. The table LIKE gives the sports a person likes, the table PRACTICE gives the sports a person practices. We assume that a person likes at least one sport and practices at least one sport. We assume also that a person does not like a sport if the sport is not listed among the sports that person likes.

Express the following queries in relational algebra

1. List the people who practice at list one sport they like
2. List the people who practice at least one sport they do not like
3. List pairs of people who practice at least one common sport
4. List the people who like all the sports they practice
5. List the people who practice all the sports they
6. List the people who practice all the sports John

Gerald Glynn manages the Michaels Distribution Center. After careful examination of his database​ information, he has determined the daily requirements for​ part-time loading dock personnel. The distribution center operates 7 days a​ week, and the daily​ part-time staffing requirements are                                                                                                                      Day M T W Th F S Su Requirements 5 5 3 3 7 2 4 Find the minimum number of workers Glynn must hire. Prepare a workforce schedule for these individuals so that each will have 2 consecutive days off per week and all staffing requirements will be satisfied. Give preference to the​ S-Su pair in case of a tie. ​(Note: If there is a tie that cannot be broken by following the preference to the​ S-Su pair, choose the pair closest to the beginning of the considered​ week.) The minimum number of workers is 7 workers. ​(Enter your response as a whole​ number.) Specify the workforce schedule for each employee by indicating the day offs​ (other days are considered to be work​ days.) Make sure to give preference to the​ S-Su pair in case of a tie. ​(Note: If there is a tie that cannot be broken by following the preference to the​ S-Su pair, choose the pair closest to the beginning of the considered​ week, with Monday being considered as the first day of the​ week.)

Consider the following database schema:

LIKE(person, sport),

PRACTICE(person, sport),

where person and sport are keys in both tables. The table LIKE gives the sports a person likes, the table PRACTICE gives the sports a person practices. We assume that a person likes at least one sport and practices at least one sport. We assume also that a person does not like a sport if the sport is not listed among the sports that person likes

1. List the people who practice at least one sport they do not like
2. List pairs of people who practice at least one common sport
3. List the people who like all the sports they practice
4. List the people who practice all the sports they like
5. List the people who practice all the sports John likes
6. Who practices the largest number of sport (SQL only)?

PROBLEM 3: Given the following relational database table: Patients(ID, name, symptom, days_in_hospital) The following insertions are performed on the table Patients: Insert record <20, Johnson, cough, 3> Insert record <10, Black, fever, 5> Insert record <30, Miller, fever, 10> Insert record <70, Brown, fatigue, 2> Insert record <60, Grant, headache, 4> Insert record <50, Miller, nausea, 15> Insert record <90, Brown, cough, 8 > Assume each block in the Patients file can store up to 2 patient records. Do the following: 1. Assuming that Patients is organized as a heap file, show the contents of the file after the last insertion. 2. Assuming that Patients is organized as a sequential file with days_in_hospital as the ordering field, show the contents (i.e. the data values as well as the associated block/bucket/record addresses) of the file after the last insertion. 3. Assuming that Patients is organized as an index-sequential file on the search key days_in_hospital and assuming that the primary index, the secondary index on ID, and the secondary index on name have been created, show the contents of Patients, the primary index, and the two secondary indices after the last insertion. 4. Given the index-sequential file organization as described in (3), explain step-by-step how the DBMS would conduct search on this file organization to answer the following SQL query: select name from Patients where ID between 30 and 60

“So much Fake News. Never been more voluminous or more inaccurate,” tweeted President Trump. A database of Trump remarks contains 320 references to fake news, named as term of the year in 2017. Leading news channels are not immune, for example in 2016 a story claiming HH Shaikh Mohammad Bin Zayed Al Nayhan had chanted a Hindu prayer went viral in India and was tweeted by main news channels. Fake news has been blamed for causing tension between countries, for example the Deputy Chairman of Dubai Police blamed Al Jazeera for deepening the crisis between Qatar and the UAE. Fake news has also resulted in tighter regulation of social media, and is now seen as a threat to democracy and free debate.

Historically, political interests have always misrepresented facts, but the identification, categorization and concept of fake news has become more complex and challenging. One team of students from Berkeley identified four classifications; clickbait, propaganda, commentary and humour and built a tool www.classify.news which scores the truth of information based on its URL. Their site claims 84% accuracy but the sample is based on only 5000 articles. IBM tested a prototype Question Answering Machine (QAM) called Watson to separate fact from fiction, and Google funded a fact checking operation called Full Fact to develop an automated fact-checking system. However, the successful implementation of fact checking models requires a constantly updated corpus of knowledge which is verified.

There are different data science architectures to check facts. The traditional NLP method of fake news detection is used by Thomson Reuters, a trusted global news source. Tracer News is a sensitive algorithmically driven system which filters news stories and social feeds for truth, and assigns a veracity score. It’s claimed to be 84% accurate, and with a sample of 5 tweets the system achieves 78% accuracy on distinguishing rumour and fact.

Research has shown that tweets containing false news spread faster and wider on Twitter than those with valid news. One estimate claims that in the month before the 2016 US election people read up to 3 articles of fake news. How this may possibly effect our attitudes is unknown, and psychologists have taken an interest in fake news. The Cognitive Reflection Test (CRT) was used to measure the ability to think analytically and consequently to predict people who can distinguish fake news from real news. Research has shown that if people agree with a message then they are more likely to believe it.

Social platforms such as Facebook are attempting to crack down on fake news in response to pressure. Facebook was accused of publishing fake posts using the name Lewis, who is a financial expert. Many people were thereby scammed to trust a financial product. Lewis pursued legal action to force social media to change their policy on advertising and be liable for hosting scams. Facebook are now playing an editorial role by changing the way News Feed functions. CEO Zuckerberg commented that sensationalism, misinformation and polarization are too common.

Countries such as Malaysia are making fake news punishable with up to 10 years in prison in an effort to protect national security. The law penalizes those who create, offer, circulate, print or publish fake news, which is defined as “any news, information, data and reports which is, or are, wholly or partly false whether in the form of features, visuals or audio recordings or in any other form capable of suggesting words or ideas”. Opponents call this an attack on freedom of speech and fear the new law could be used to penalize critical attacks on the government.

Human fact checkers are a rigorous and expensive way to combat fake news. A simple claim could take hours to verify and the manpower required could be considerable. If the responsibility lies with algorithms, false positives and negatives could lead to the suppression of a news story. In the UAE, the Youth Media Council is playing a role in the UAE’s strategy of developing the media sector and verifying credible from fake news. In a Dubai competition the winners research had explored a fake news incident whereby students’ names were spread on social media as soldiers who had died. Workshops to educate and teach young people skills to identify fake news were suggested.

Technology has enabled anyone to create news and for that news to go viral. The success of the message is not reliant on the truth of the contents, and there is too much information to validate. Many questions are raised about the effects of tagging news as fake, susceptibility to fake news, is fake news more real if its viral, and how to identify fake news. How can we create and sustain a global culture which promotes and values truth? What indeed is the truth of an event when multiple perspectives of the same event can hold truth.

1. What is/are the problem/problems here? Is there an underlying fundamental problem?
2. Who are the major stakeholders and what are their perspectives?
3. What are the major ethical, legal, and security aspects associated with the problem.
4. What are the intended and unintended consequences of existing computing solutions? Consider the consequences on individuals, organizations and society within local and global contexts
5. What recommendations would you propose that will lead to potential solutions.

Please answer ASAP

Database

Consider the following relation scheme:
Players = {name, position, year, team, manager}
We use the following abbreviations:
Players = {n, p, y, t, m}
with the following FDs
n --> p
n, y --> t
t, y --> m
(3.a) Is Players in BCNF? Explain why.
(3.b) Find all candidate keys of Players. Show your work.
(3.c) Obtain a decomposition of players using the 3NF decomposition algorithm. Assume the
given FDs are a minimal (canonical) cover. Show your work.
(3.d) Assume the following FD also holds in players (in addition to the FDs listed above).
t, m ! y
Find all candidate keys of Players for this case. Show your work.