“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.
In: Computer Science
Ruphani Beverage Limited
Ruphani Beverage Limited entered the Indian wine industry in 1975
by acquiring the Mastana Wine Company of Shimla and two other
smaller wine companies at Kalka for Rs. 50 Lakh.
Despite hostility expressed by other wine makers and predictions
that Rupbani would very soon fail as other outsiders such as
Parminder Wine Company had, the entry succeeded. Rupbani Limited
performed the unheard of feat of establishing a volume of 30 lakh
cases within two years and taking the market share away from
premium brands such the National Wine Company of Bombay, Pearl
Drink Limited of Pune and Syndicate Cola Limited of Madras.
Rupbani advertised heavily and incurred Rs. 10 lakh in one year and
standardized the taste of its wines with considerable success. It
also invested Rs. 48 lakh in a large, new winery at Ahmedabad. A
Rupbani Executive said, “By 1995, consumption of wine in India will
be a liter per capita, compared with half a liter today.”
The industry reacted to Rupbani’s presence by doubling and tripling
advertising expenditure. ABC and Company began a costly campaign to
market premium and varied wines while reducing marketing emphasis
on its cheap wines such as Nahan Drinks and the Gola Beverage. ABC
maintained its 25 percent market share but had to resort to some
heavy price discounting to do so.
In 1982 Pearl Drinks formed a special wine unit to combine efforts
for all its brands. Mr. Sailesh Kumar former Vice President of the
National Wine Company had directed a project to coordinate Pearl’s
world-wide wine business and develop a worldwide strategy. The new
unit was, in face, a result of his work.
In 1983, wine consumption changed from growth at a rate of 5 per
cent to no growth. The government also lifted the ban on imports of
wine. This presented an even greater challenge because imported
wines were cheaper as well as superior in quality.
In 1984 Mr. Ranganathan took over as Managing Director of Rupbani.
He reviewed the recent performance of the company and its
competitive position. He noted that the company was losing its hold
over the market and it was not getting the return as expected. He
also found that the company’s performance in the syrup business was
excellent. He, therefore, thought of selling out the wine business
to Pearl Drinks, He convened an executive meeting and apprised the
executives of his proposal. He also informed them that Pearl Drinks
had offered the company to recapture its investment in the wine
business which was about Rs. One crore. Mr. Arun Mehta, General
Manager, observed that Rupbani was in and out in the past six years
and had joined different organizations in trying the wine business.
The finance Manager, M. Subhash Ghai said, “The return on assets in
the wine business is not the 30 to 35 per cent, which Rupbani is
used to getting in the syrup business. Gaining share and trying to
compete with ABC and Company left Rupbani with, eventually, the
number two position in the wine industry with profits of Rs. 60
lakh on Rs. 220 lakh in sales. The stockholders wanted immediate
return and hence, the company could not afford to make long-term
investments necessary to popularize the brands. Had they stayed for
five more years, they would have been a key leader in a large and
profitable industry”. Pearl Drinks immediately went from the sixth
position in the industry to a strong second place with an 11 per
cent market share. The Chairman of Pearl Drinks stated: “We believe
you can make money in this business in two ways. Remain a small
boutique winery or become large and achieve economies of
scale”.
Mr. Harish, Marketing Manager of Rupbani said, “It is no use
selling out our business to Pearl Drink and get back what we have
invested. We can compete with our competitors successfully and
improve our market share if we manufacture wines of varying
qualities to suit the varied preferences and pockets of diverse
sections of society. We should also offer price discounts to
attract the consumers. There should be wide publicity of our brands
throughout the country”.
a) Perform SWOT analysis of Rupbani. (10 Marks)
b) In the light of opportunities and threats of Ruphani Beverage
and its strengths and weaknesses, what strategy should it formulate
to improve its performance and strengthen its competitive position?
(5 Marks)
c) Should Rupbani spend on advertising in line with its
competitors? Discuss. (5 Marks)
d) What Other strategies would you suggest for Rupbani for
increasing their share of the market? (10 Marks)
In: Finance
Voluntary export restraints provide for rich interplay between economics and politics. Let's look at two examples. In the first, the United States forced one key exporter, Japan, to limit its exports of automobiles. In the second, a small VER, again between the United States and Japan, grew to become a wide-ranging set of export limits that covered many textile and clothing products, involved many countries, and lasted for decades.
TEXTILES AND CLOTHING: A MONSTER
In 1955, a monster was born. In the face of rising imports from Japan, the U.S. government convinced the Japanese government to “voluntarily” limit Japan's exports of cotton fabric and clothing to the United States. In the late 1950s, Britain followed by compelling India and Pakistan to impose VERs on their clothing and textile exports to Britain. The VERs were initially justified as “temporary” restraints in response to protectionist pleas from import-competing firms that they needed time to adjust to rising foreign competition. But the monster kept growing.
Page 177
The 1961 Short-Term Arrangement led to the 1962 Long-Term Arrangement. In 1974, the Multifibre Arrangement extended the scheme to include most types of textiles and clothing. The trade policy monster became huge. A large and rising number of VERs, negotiated country by country and product by product, limited exports by developing countries to industrialized countries (and to a number of other developing countries).
The monster even had its own growth dynamic. A VER is, in effect, a cartel among the exporting firms. As they raise their prices, the profit opportunity attracts other, initially unconstrained suppliers. Production of textiles and clothing for export spread to countries such as Bangladesh, Cambodia, Fiji, and Turkmenistan. As these countries became successful exporters, the importing countries pressured them to enact VERs to limit their disruption to the managed trade.
The developing countries that were constrained by these VERs pushed hard during the Uruguay Round of trade negotiations to bring this trade back within the normal WTO rules (no quantitative limits, and any tariffs to apply equally to all countries—most favored nation treatment, rather than bilateral restrictions). The Agreement on Textiles and Clothing came into force in 1995 and provided for a 10-year period during which all quotas in this sector would be ended. On January 1, 2005, after almost a half century of life, the monster mostly died.
We say “mostly” because for a few more years a small piece of the monster lived on. As part of its accession agreement to the World Trade Organization, China accepted that other countries could impose China-specific “safeguards” if its rising exports of textiles or clothing harmed import-competing producers. As the United States phased out VERs, the U.S. government imposed such safeguards on some imports from China. By late 2005 a comprehensive agreement limited imports of 22 types of products from China. Similarly, the European Union imposed safeguard limits on imports from China on 10 types of products. Then, the monster finally took its last breaths. The EU limits expired at the end of 2007 and the U.S. limits expired at the end of 2008. (Still, we do not have free trade in textiles and clothing because many countries continue to have relatively high import tariffs in this sector. But the web of VERs has ended.)
Consumers are the big winners from the liberalization. Prices generally fell by 10 to 40 percent when the VERs ended. Another set of winners is countries, including China, India, and Bangladesh, that have strong comparative advantage in textiles and clothing but whose production and exports had been severely constrained by the VERs. On the other side, with rising imports, textile and clothing firms and workers in the United States and other industrialized countries have been harmed. Another set of losers is those developing countries, apparently including Korea and Taiwan, that do not have comparative advantage in textile and clothing production but that had become producers and exporters of textiles and clothing because the VERs had severely restricted the truly competitive countries. (This shows another type of global production inefficiency that resulted from the VERs.) These uncompetitive countries lost the VER rents that they had been receiving, and their industries shrank as those in countries such as China expanded.
Create a convincing case to justify DC's such as the United States and Britain imposing VERS on imported textiles and apparel. On the other hand beyond merely repeating the points already made in the text, make the case as an international economist, that VERS in textiles and apparel have been bad for global welfare.
In: Economics
The attached WorldSeriesWinners.txt ( since I cannot share it directly I will send the information for the text file ) file contains the name of the winner of the World Series (duh) and the year in which they won. 1904 and 1994 did not have World Series played, so "No Winner" is displayed for those years. Your job is to write a program that lets the user enter the name of a team (or "No Winner") and then display the number of times the team won and a list of the years in which they won. Some hints/tips:
Boston Americans 1903 No Winner 1904 New York Giants 1905 Chicago White Sox 1906 Chicago Cubs 1907 Chicago Cubs 1908 Pittsburgh Pirates 1909 Philadelphia Athletics 1910 Philadelphia Athletics 1911 Boston Red Sox 1912 Philadelphia Athletics 1913 Boston Braves 1914 Boston Red Sox 1915 Boston Red Sox 1916 Chicago White Sox 1917 Boston Red Sox 1918 Cincinnati Reds 1919 Cleveland Indians 1920 New York Giants 1921 New York Giants 1922 New York Yankees 1923 Washington Senators 1924 Pittsburgh Pirates 1925 St. Louis Cardinals 1926 New York Yankees 1927 New York Yankees 1928 Philadelphia Athletics 1929 Philadelphia Athletics 1930 St. Louis Cardinals 1931 New York Yankees 1932 New York Giants 1933 St. Louis Cardinals 1934 Detroit Tigers 1935 New York Yankees 1936 New York Yankees 1937 New York Yankees 1938 New York Yankees 1939 Cincinnati Reds 1940 New York Yankees 1941 St. Louis Cardinals 1942 New York Yankees 1943 St. Louis Cardinals 1944 Detroit Tigers 1945 St. Louis Cardinals 1946 New York Yankees 1947 Cleveland Indians 1948 New York Yankees 1949 New York Yankees 1950 New York Yankees 1951 New York Yankees 1952 New York Yankees 1953 New York Giants 1954 Brooklyn Dodgers 1955 New York Yankees 1956 Milwaukee Braves 1957 New York Yankees 1958 Los Angeles Dodgers 1959 Pittsburgh Pirates 1960 New York Yankees 1961 New York Yankees 1962 Los Angeles Dodgers 1963 St. Louis Cardinals 1964 Los Angeles Dodgers 1965 Baltimore Orioles 1966 St. Louis Cardinals 1967 Detroit Tigers 1968 New York Mets 1969 Baltimore Orioles 1970 Pittsburgh Pirates 1971 Oakland Athletics 1972 Oakland Athletics 1973 Oakland Athletics 1974 Cincinnati Reds 1975 Cincinnati Reds 1976 New York Yankees 1977 New York Yankees 1978 Pittsburgh Pirates 1979 Philadelphia Phillies 1980 Los Angeles Dodgers 1981 St. Louis Cardinals 1982 Baltimore Orioles 1983 Detroit Tigers 1984 Kansas City Royals 1985 New York Mets 1986 Minnesota Twins 1987 Los Angeles Dodgers 1988 Oakland Athletics 1989 Cincinnati Reds 1990 Minnesota Twins 1991 Toronto Blue Jays 1992 Toronto Blue Jays 1993 No Winner 1994 Atlanta Braves 1995 New York Yankees 1996 Florida Marlins 1997 New York Yankees 1998 New York Yankees 1999 New York Yankees 2000 Arizona Diamondbacks 2001 Anaheim Angels 2002 Florida Marlins 2003 Boston Red Sox 2004 Chicago White Sox 2005 St. Louis Cardinals 2006 Boston Red Sox 2007 Philadelphia Phillies 2008 New York Yankees 2009 San Francisco Giants 2010 St. Louis Cardinals 2011 San Francisco Giants 2012 Boston Red Sox 2013 San Francisco Giants 2014 Kansas City Royals 2015 Chicago Cubs 2016
In: Computer Science
Case Study:
Grand Hospital is located in a somewhat rural area of a Midwestern state. It is a 209-bed, community, not-for-profit entity offering a broad range of inpatient and outpatient services. Employing approximately 1,600 individuals (1,250 full-time equivalent personnel), and having medical staff of more than 225 practitioners, Grand has an annual operating budget that exceeds $130 million, possesses net assets of more than $150 million, and is one of only a small number of organizations in this market with an A credit rating from Moody’s, Standard & Poor’s and Fitch Ratings. Operating in a remarkably competitive market (there are roughly 100 hospitals within seventy-five minutes’ driving time of Grand), the organization is one of the few in the region-proprietary or not-for-profit-that have consistently realized positive operating margins. Grand attends on an annual basis to the health care needs of more than 11,000 inpatients and 160,000 outpatients, addressing more than 36 percent of its primary service area’s consumption of hospital services. In expansion mode and currently in the midst of $57 million in construction and renovation projects, the hospital is struggling to recruit physicians, both to meet the health care needs of the expanding population of the service area and to succeed retiring physicians.
Grand has been an early adopter of health care information systems and currently employs a proprietary health care information system that provides (among other components)
- Patient registration and revenue management
- Electronic health records with computerized physician order entry
- Imaging via a PACS
- Laboratory management
- Pharmacy management
Information Systems Challenge
Since 1995, Grand Hospital has transitioned from being an institution that consistently received many more inquiries than could be accommodated concerning physician practice opportunities, to a hospital at which the average age of the medical staff has increased by eight years. There is a widespread perception among physicians that because of such factors as high malpractice insurance costs, an absence of substantive tort reform, and the comparatively unfavorable rates of reimbursement being paid physician specialists by the region’s major health insurer, this region constitutes a “physician unfriendly” venue in which to establish a practice. Consequently, a need exists for Grand to investigate and evaluate creative approaches to enhancing its physician coverage for certain specialty services. These potential approaches include the effective implementation of information technology solutions.
The findings and conclusions of a medical staff development plan, which has been endorsed and accepted by Grand’s medical executive committee and board of trustees, have indicated that because of needs and circumstances specific to the institution, the first areas of medical practice on which Grand should focus in approaching this challenge are radiology, behavioral health crisis intervention services, and intensivist physician services. In the area of radiology, Grand needs qualified and appropriately credentialed radiologists available to interpret studies 24 hours per day, 7 days per week. Similarly, it needs qualified and appropriately credentialed psychiatrists available on a 24/7 basis to assess whether behavioral health patients who present in the hospital’s emergency room are a danger to themselves or to others, as defined by state statute, and whether these patients should be released or committed against their will for further assessment on an inpatient basis. Finally, in as much as Grand is a community hospital that relies on its voluntary medical staff to attend to the needs of patients admitted by staff members such as some ED personnel, it also needs to have intensivist physicians available around the clock to assist in assessing and treating patients during times when members of the voluntary attending staff are not present within or immediately available to the intensive care unit.
The leadership at Grand Hospital is investigating the potential application of telemedicine technologies to address the organization’s need for enhanced physician coverage in radiology, behavioral health, and critical care medicine.
For this case study report, focus on the below areas as you apply relevant concepts to this particular case.
- What are the ways in which Grand's early adoption of other health care information system technologies might affect its adoption of telemedicine solutions?
- How might an early adoption make the transition to telemedicine easier?
- What do you see as the most likely barriers to the success of telemedicine in the areas of radiology, behavioral health, and intensive care?
- Which barriers will be the most difficult to overcome? Why?
- Which of these areas would be the hardest to transition into telemedicine? Why?
- Which of these areas do you think would be the easiest to transition into telemedicine? Why?
In: Nursing
|
Case Study |
VERs: An Example |
|
Voluntary export restraints provide for rich interplay between economics and politics. Let's look at two examples. In the first, the United States forced one key exporter, Japan, to limit its exports of automobiles. In the second, a small VER, again between the United States and Japan, grew to become a wide-ranging set of export limits that covered many textile and clothing products, involved many countries, and lasted for decades.
TEXTILES AND CLOTHING: A MONSTER
In 1955, a monster was born. In the face of rising imports from Japan, the U.S. government convinced the Japanese government to “voluntarily” limit Japan's exports of cotton fabric and clothing to the United States. In the late 1950s, Britain followed by compelling India and Pakistan to impose VERs on their clothing and textile exports to Britain. The VERs were initially justified as “temporary” restraints in response to protectionist pleas from import-competing firms that they needed time to adjust to rising foreign competition. But the monster kept growing.
Page 177
The 1961 Short-Term Arrangement led to the 1962 Long-Term Arrangement. In 1974, the Multifibre Arrangement extended the scheme to include most types of textiles and clothing. The trade policy monster became huge. A large and rising number of VERs, negotiated country by country and product by product, limited exports by developing countries to industrialized countries (and to a number of other developing countries).
The monster even had its own growth dynamic. A VER is, in effect, a cartel among the exporting firms. As they raise their prices, the profit opportunity attracts other, initially unconstrained suppliers. Production of textiles and clothing for export spread to countries such as Bangladesh, Cambodia, Fiji, and Turkmenistan. As these countries became successful exporters, the importing countries pressured them to enact VERs to limit their disruption to the managed trade.
The developing countries that were constrained by these VERs pushed hard during the Uruguay Round of trade negotiations to bring this trade back within the normal WTO rules (no quantitative limits, and any tariffs to apply equally to all countries—most favored nation treatment, rather than bilateral restrictions). The Agreement on Textiles and Clothing came into force in 1995 and provided for a 10-year period during which all quotas in this sector would be ended. On January 1, 2005, after almost a half century of life, the monster mostly died.
We say “mostly” because for a few more years a small piece of the monster lived on. As part of its accession agreement to the World Trade Organization, China accepted that other countries could impose China-specific “safeguards” if its rising exports of textiles or clothing harmed import-competing producers. As the United States phased out VERs, the U.S. government imposed such safeguards on some imports from China. By late 2005 a comprehensive agreement limited imports of 22 types of products from China. Similarly, the European Union imposed safeguard limits on imports from China on 10 types of products. Then, the monster finally took its last breaths. The EU limits expired at the end of 2007 and the U.S. limits expired at the end of 2008. (Still, we do not have free trade in textiles and clothing because many countries continue to have relatively high import tariffs in this sector. But the web of VERs has ended.)
Consumers are the big winners from the liberalization. Prices generally fell by 10 to 40 percent when the VERs ended. Another set of winners is countries, including China, India, and Bangladesh, that have strong comparative advantage in textiles and clothing but whose production and exports had been severely constrained by the VERs. On the other side, with rising imports, textile and clothing firms and workers in the United States and other industrialized countries have been harmed. Another set of losers is those developing countries, apparently including Korea and Taiwan, that do not have comparative advantage in textile and clothing production but that had become producers and exporters of textiles and clothing because the VERs had severely restricted the truly competitive countries. (This shows another type of global production inefficiency that resulted from the VERs.) These uncompetitive countries lost the VER rents that they had been receiving, and their industries shrank as those in countries such as China expanded.
Create a convincing case to justify DC's such as the United States and Britain imposing VERS on imported textiles and apparel. On the other hand beyond merely repeating the points already made in the text, make the case as an international economist, that VERS in textiles and apparel have been bad for global welfare.
In: Economics
#########################PANDAS LANGUAGE##################
#########################MATPLOT LIB#########################
# read movie.csv into a DataFrame called 'movie' # describe the dataframe #rename the column Runtime (Minutes) with Runtime_Minutes, and Revenue (Millions) with Revenue_Millions
# show if any column has null value
# count total number of null vlaues in the dataframe
# print those rows which has null values
# fill null values,
#if column is numerical than fill with means (if there is no numerical missing value in
#data frame then don't code in this)
#if column is categorical than fill with most frequent value (if there is no categorical missing value in
#data frame then don't code in this)
# plot histogram of the column name year in movie dataframe, which shows how many movies release in a year.
# print the movie detail with title 'Grumpier Old Men'.
# show those movies which are released after 1995-01-01
# sort the movie DataFrame in decending order based on release_date
# for each year, display the total number of movie with specific gerne for example Action=1000,adventure=400
# plot histogram the upper calculated total count
# filter the movies with specific gerne # like show only those movies which are selected Action gerne
# filter the movies with specific gerne
# like show only those movies which are selected Action gerne
# for each Director, display all the movies with detail.
# count the movies and plot barchart top 10 director's movies.
# for each Actor, display all the movies with detail.
# count the movies and visualize the top 10 actor's movies in plot
In [27]:
data file
| Rank | Title | Genre | Description | Director | Actors | Year | Runtime (Minutes) | Rating | Votes | Revenue (Millions) | Metascore | |
| 1 | Guardians of the Galaxy | Action,Adventure,Sci-Fi | A group of intergalactic criminals are forced to work together to stop a fanatical warrior from taking control of the universe. | James Gunn | Chris Pratt, Vin Diesel, Bradley Cooper, Zoe Saldana | 2014 | 121 | 8.1 | 757074 | 333.13 | 76 | |
| 2 | Prometheus | Adventure,Mystery,Sci-Fi | Following clues to the origin of mankind, a team finds a structure on a distant moon, but they soon realize they are not alone. | Ridley Scott | Noomi Rapace, Logan Marshall-Green, Michael Fassbender, Charlize Theron | 2012 | 124 | 7 | 485820 | 126.46 | 65 | |
| 3 | Split | Horror,Thriller | Three girls are kidnapped by a man with a diagnosed 23 distinct personalities. They must try to escape before the apparent emergence of a frightful new 24th. | M. Night Shyamalan | James McAvoy, Anya Taylor-Joy, Haley Lu Richardson, Jessica Sula | 2016 | 117 | 7.3 | 157606 | 138.12 | 62 | |
| 4 | Sing | Animation,Comedy,Family | In a city of humanoid animals, a hustling theater impresario's attempt to save his theater with a singing competition becomes grander than he anticipates even as its finalists' find that their lives will never be the same. | Christophe Lourdelet | Matthew McConaughey,Reese Witherspoon, Seth MacFarlane, Scarlett Johansson | 2016 | 108 | 4.2 | 60545 | 270.32 | 59 | |
| 5 | Suicide Squad | Action,Adventure,Fantasy | A secret government agency recruits some of the most dangerous incarcerated super-villains to form a defensive task force. Their first mission: save the world from the apocalypse. | David Ayer | Will Smith, Jared Leto, Margot Robbie, Viola Davis | 2015 | 123 | 3.2 | 393727 | 325.02 | 40 | |
| 6 | The Great Wall | Action,Adventure,Fantasy | European mercenaries searching for black powder become embroiled in the defense of the Great Wall of China against a horde of monstrous creatures. | Yimou Zhang | Matt Damon, Tian Jing, Willem Dafoe, Andy Lau | 2014 | 103 | 6.1 | 56036 | 45.13 | 42 | |
| 7 | La La Land | Comedy,Drama,Music | A jazz pianist falls for an aspiring actress in Los Angeles. | Damien Chazelle | Ryan Gosling, Emma Stone, Rosemarie DeWitt, J.K. Simmons | 2013 | 128 | 5.3 | 258682 | 151.06 | 93 | |
| 8 | Mindhorn | Comedy | A has-been actor best known for playing the title character in the 1980s detective series "Mindhorn" must work with the police when a serial killer says that he will only speak with Detective Mindhorn, whom he believes to be a real person. | Sean Foley | Essie Davis, Andrea Riseborough, Julian Barratt,Kenneth Branagh | 2010 | 89 | 6.4 | 2490 | 71 | ||
In: Computer Science
Highlight Year, GDP and Consumption expenditure data including labels. Select Insert, and from Charts option, select line chart. On horizonatal axis, you should see, Years, plus GDP and Consumption expenditure trend lines AS you can see, GDP and Consumption lines lie practically on top of each other from 1929 through early 1960s before diverging. Write a short paragraph explaining why GDP (a measure of output) exceeded Consumption starting 1960s. What happened to the economy in early 60's that brought about divergence of output and consumption?
|
Nominal Gross Domestic Product and Personal Consumption Expenditure in $billions 1929-2016 |
||
|
Source: Federal Reserve Bank of St. Louis |
||
|
Year |
Personal Consumption Expenditure |
Nominal GDP |
|
1929-01-01 |
77.4 |
104.6 |
|
1930-01-01 |
70.1 |
92.2 |
|
1931-01-01 |
60.7 |
77.4 |
|
1932-01-01 |
48.7 |
59.5 |
|
1933-01-01 |
45.9 |
57.2 |
|
1934-01-01 |
51.5 |
66.8 |
|
1935-01-01 |
55.9 |
74.3 |
|
1936-01-01 |
62.2 |
84.9 |
|
1937-01-01 |
66.8 |
93.0 |
|
1938-01-01 |
64.3 |
87.4 |
|
1939-01-01 |
67.2 |
93.5 |
|
1940-01-01 |
71.3 |
102.9 |
|
1941-01-01 |
81.1 |
129.4 |
|
1942-01-01 |
89.0 |
166.0 |
|
1943-01-01 |
99.9 |
203.1 |
|
1944-01-01 |
108.6 |
224.6 |
|
1945-01-01 |
120.0 |
228.2 |
|
1946-01-01 |
144.3 |
227.8 |
|
1947-01-01 |
162.0 |
249.9 |
|
1948-01-01 |
175.0 |
274.8 |
|
1949-01-01 |
178.5 |
272.8 |
|
1950-01-01 |
192.2 |
300.2 |
|
1951-01-01 |
208.5 |
347.3 |
|
1952-01-01 |
219.5 |
367.7 |
|
1953-01-01 |
233.0 |
389.7 |
|
1954-01-01 |
239.9 |
391.1 |
|
1955-01-01 |
258.7 |
426.2 |
|
1956-01-01 |
271.6 |
450.1 |
|
1957-01-01 |
286.7 |
474.9 |
|
1958-01-01 |
296.0 |
482.0 |
|
1959-01-01 |
317.5 |
522.5 |
|
1960-01-01 |
331.6 |
543.3 |
|
1961-01-01 |
342.0 |
563.3 |
|
1962-01-01 |
363.1 |
605.1 |
|
1963-01-01 |
382.5 |
638.6 |
|
1964-01-01 |
411.2 |
685.8 |
|
1965-01-01 |
443.6 |
743.7 |
|
1966-01-01 |
480.6 |
815.0 |
|
1967-01-01 |
507.4 |
861.7 |
|
1968-01-01 |
557.4 |
942.5 |
|
1969-01-01 |
604.5 |
1019.9 |
|
1970-01-01 |
647.7 |
1075.9 |
|
1971-01-01 |
701.0 |
1167.8 |
|
1972-01-01 |
769.4 |
1282.4 |
|
1973-01-01 |
851.1 |
1428.5 |
|
1974-01-01 |
932.0 |
1548.8 |
|
1975-01-01 |
1032.8 |
1688.9 |
|
1976-01-01 |
1150.2 |
1877.6 |
|
1977-01-01 |
1276.7 |
2086.0 |
|
1978-01-01 |
1426.2 |
2356.6 |
|
1979-01-01 |
1589.5 |
2632.1 |
|
1980-01-01 |
1754.6 |
2862.5 |
|
1981-01-01 |
1937.5 |
3211.0 |
|
1982-01-01 |
2073.9 |
3345.0 |
|
1983-01-01 |
2286.5 |
3638.1 |
|
1984-01-01 |
2498.2 |
4040.7 |
|
1985-01-01 |
2722.7 |
4346.7 |
|
1986-01-01 |
2898.4 |
4590.2 |
|
1987-01-01 |
3092.1 |
4870.2 |
|
1988-01-01 |
3346.9 |
5252.6 |
|
1989-01-01 |
3592.8 |
5657.7 |
|
1990-01-01 |
3825.6 |
5979.6 |
|
1991-01-01 |
3960.2 |
6174.0 |
|
1992-01-01 |
4215.7 |
6539.3 |
|
1993-01-01 |
4471.0 |
6878.7 |
|
1994-01-01 |
4741.0 |
7308.8 |
|
1995-01-01 |
4984.2 |
7664.1 |
|
1996-01-01 |
5268.1 |
8100.2 |
|
1997-01-01 |
5560.7 |
8608.5 |
|
1998-01-01 |
5903.0 |
9089.2 |
|
1999-01-01 |
6307.0 |
9660.6 |
|
2000-01-01 |
6792.4 |
10284.8 |
|
2001-01-01 |
7103.1 |
10621.8 |
|
2002-01-01 |
7384.1 |
10977.5 |
|
2003-01-01 |
7765.5 |
11510.7 |
|
2004-01-01 |
8260.0 |
12274.9 |
|
2005-01-01 |
8794.1 |
13093.7 |
|
2006-01-01 |
9304.0 |
13855.9 |
|
2007-01-01 |
9750.5 |
14477.6 |
|
2008-01-01 |
10013.6 |
14718.6 |
|
2009-01-01 |
9847.0 |
14418.7 |
|
2010-01-01 |
10202.2 |
14964.4 |
|
2011-01-01 |
10689.3 |
15517.9 |
|
2012-01-01 |
11050.6 |
16155.3 |
|
2013-01-01 |
11361.2 |
16691.5 |
|
2014-01-01 |
11863.4 |
17393.1 |
|
2015-01-01 |
12283.7 |
18036.6 |
|
2016-01-01 |
12757.9 |
18569.1 |
In: Economics
|
Nominal Gross Domestic Product and Personal Consumption Expenditure in $billions 1929-2016 |
||
|
Source: Federal Reserve Bank of St. Louis |
||
|
Year |
Personal Consumption Expenditure |
Nominal GDP |
|
1929-01-01 |
77.4 |
104.6 |
|
1930-01-01 |
70.1 |
92.2 |
|
1931-01-01 |
60.7 |
77.4 |
|
1932-01-01 |
48.7 |
59.5 |
|
1933-01-01 |
45.9 |
57.2 |
|
1934-01-01 |
51.5 |
66.8 |
|
1935-01-01 |
55.9 |
74.3 |
|
1936-01-01 |
62.2 |
84.9 |
|
1937-01-01 |
66.8 |
93.0 |
|
1938-01-01 |
64.3 |
87.4 |
|
1939-01-01 |
67.2 |
93.5 |
|
1940-01-01 |
71.3 |
102.9 |
|
1941-01-01 |
81.1 |
129.4 |
|
1942-01-01 |
89.0 |
166.0 |
|
1943-01-01 |
99.9 |
203.1 |
|
1944-01-01 |
108.6 |
224.6 |
|
1945-01-01 |
120.0 |
228.2 |
|
1946-01-01 |
144.3 |
227.8 |
|
1947-01-01 |
162.0 |
249.9 |
|
1948-01-01 |
175.0 |
274.8 |
|
1949-01-01 |
178.5 |
272.8 |
|
1950-01-01 |
192.2 |
300.2 |
|
1951-01-01 |
208.5 |
347.3 |
|
1952-01-01 |
219.5 |
367.7 |
|
1953-01-01 |
233.0 |
389.7 |
|
1954-01-01 |
239.9 |
391.1 |
|
1955-01-01 |
258.7 |
426.2 |
|
1956-01-01 |
271.6 |
450.1 |
|
1957-01-01 |
286.7 |
474.9 |
|
1958-01-01 |
296.0 |
482.0 |
|
1959-01-01 |
317.5 |
522.5 |
|
1960-01-01 |
331.6 |
543.3 |
|
1961-01-01 |
342.0 |
563.3 |
|
1962-01-01 |
363.1 |
605.1 |
|
1963-01-01 |
382.5 |
638.6 |
|
1964-01-01 |
411.2 |
685.8 |
|
1965-01-01 |
443.6 |
743.7 |
|
1966-01-01 |
480.6 |
815.0 |
|
1967-01-01 |
507.4 |
861.7 |
|
1968-01-01 |
557.4 |
942.5 |
|
1969-01-01 |
604.5 |
1019.9 |
|
1970-01-01 |
647.7 |
1075.9 |
|
1971-01-01 |
701.0 |
1167.8 |
|
1972-01-01 |
769.4 |
1282.4 |
|
1973-01-01 |
851.1 |
1428.5 |
|
1974-01-01 |
932.0 |
1548.8 |
|
1975-01-01 |
1032.8 |
1688.9 |
|
1976-01-01 |
1150.2 |
1877.6 |
|
1977-01-01 |
1276.7 |
2086.0 |
|
1978-01-01 |
1426.2 |
2356.6 |
|
1979-01-01 |
1589.5 |
2632.1 |
|
1980-01-01 |
1754.6 |
2862.5 |
|
1981-01-01 |
1937.5 |
3211.0 |
|
1982-01-01 |
2073.9 |
3345.0 |
|
1983-01-01 |
2286.5 |
3638.1 |
|
1984-01-01 |
2498.2 |
4040.7 |
|
1985-01-01 |
2722.7 |
4346.7 |
|
1986-01-01 |
2898.4 |
4590.2 |
|
1987-01-01 |
3092.1 |
4870.2 |
|
1988-01-01 |
3346.9 |
5252.6 |
|
1989-01-01 |
3592.8 |
5657.7 |
|
1990-01-01 |
3825.6 |
5979.6 |
|
1991-01-01 |
3960.2 |
6174.0 |
|
1992-01-01 |
4215.7 |
6539.3 |
|
1993-01-01 |
4471.0 |
6878.7 |
|
1994-01-01 |
4741.0 |
7308.8 |
|
1995-01-01 |
4984.2 |
7664.1 |
|
1996-01-01 |
5268.1 |
8100.2 |
|
1997-01-01 |
5560.7 |
8608.5 |
|
1998-01-01 |
5903.0 |
9089.2 |
|
1999-01-01 |
6307.0 |
9660.6 |
|
2000-01-01 |
6792.4 |
10284.8 |
|
2001-01-01 |
7103.1 |
10621.8 |
|
2002-01-01 |
7384.1 |
10977.5 |
|
2003-01-01 |
7765.5 |
11510.7 |
|
2004-01-01 |
8260.0 |
12274.9 |
|
2005-01-01 |
8794.1 |
13093.7 |
|
2006-01-01 |
9304.0 |
13855.9 |
|
2007-01-01 |
9750.5 |
14477.6 |
|
2008-01-01 |
10013.6 |
14718.6 |
|
2009-01-01 |
9847.0 |
14418.7 |
|
2010-01-01 |
10202.2 |
14964.4 |
|
2011-01-01 |
10689.3 |
15517.9 |
|
2012-01-01 |
11050.6 |
16155.3 |
|
2013-01-01 |
11361.2 |
16691.5 |
|
2014-01-01 |
11863.4 |
17393.1 |
|
2015-01-01 |
12283.7 |
18036.6 |
|
2016-01-01 |
12757.9 |
18569.1 |
Regress Consumption against the GDP from the data sheet. Include the Excel ANOVA table.Although irrelevant run a "F" test as well as individual coefficient test . Write a short paragraph discussing the results. For example, how this information can be used to forecast future consumption or any other interesting conclusions you can draw Material in your textbook as well as outside reading can be very helpful
In: Economics
Please complete the 2019 federal income tax return for Alice J. and Bruce M. Byrd.
Alice J. and Bruce M. Byrd are married taxpayers who file a joint return. Their Social Security numbers are 123-45-6784 and 111-11-1113, respectively. Alice’s birthday is September 21, 1971, and Bruce’s is June 27, 1970. They live at 473 Revere Avenue, Lowell, MA 01850. Alice is the office manager for Lowell Dental Clinic, 433 Broad Street, Lowell, MA 01850 (employer identification number 98-7654321). Bruce is the manager of a Super Burgers fast-food outlet owned and operated by Plymouth Corporation, 1247 Central Avenue, Hauppauge, NY 11788 (employer identification number 11-1111111).
The following information is shown on their Wage and Tax Statements (Form W–2) for 2019.
Line Description Alice Bruce
1 Wages, tips, other compensation $58,000 $62,100
2 Federal income tax withheld 4,500 5,300
3 Social Security wages 58,000 62,100
4 Social Security tax withheld 3,596 3,850
5 Medicare wages and tips 58,000 62,100
6 Medicare tax withheld 841 900
15 State Massachusetts Massachusetts
16 State wages, tips, etc. 58,000 62,100
17 State income tax withheld 2,950 3,100
The Byrds provide over half of the support of their two children, Cynthia (born January 25, 1995, Social Security number 123-45-6788) and John (born February 7, 1999, Social Security number 123-45-6780). Both children are full-time students and live with the Byrds except when they are away at college. Cynthia earned $6,200 from a summer internship in 2019, and John earned $3,800 from a part-time job. Both children received scholarships covering tuition and materials.
During 2019, the Byrds provided 60% of the total support of Bruce’s widower father, Sam Byrd (born March 6, 1943, Social Security number 123-45-6787). Sam lived alone and covered the rest of his support with his Social Security benefits. Sam died in November, and Bruce, the beneficiary of a policy on Sam’s life, received life insurance proceeds of $1,600,000 on December 28.
The Byrds had the following expenses relating to their personal residence during 2019:
Property taxes $5,000
Qualified interest on home mortgage (acquisition indebtedness) 8,700
Utilities 4,100
Repairs 1,000
Fire and theft insurance 1,900
The Byrds had the following medical expenses for 2019:
Medical insurance premiums $4,284
Operation for Sam 8,500
Prescription medicines for Sam 900
Hospital expenses for Sam 3,500
Reimbursement from insurance company, received in 2019 3,600
Other relevant information follows:
• When they filed their 2018 state return in 2019, the Byrds paid additional state income tax of $900.
• During 2019, Alice and Bruce attended a dinner dance sponsored by the Lowell Police Disability Association (a qualified charitable organization). The Byrds paid $300 for the tickets. The cost of comparable entertainment would normally be $50.
• The Byrds contributed $5,xxx (last 3 digits of your student ID) cash to Lowell Presbyterian Church.
• Via a crowdfunding site (gofundme.com), Alice and Bruce made a gift to a needy family who lost their home in a fire ($400). In addition, they made several cash gifts to homeless individuals downtown (estimated to be $65).
• In 2019, the Byrds received interest income of $2,750, which was reported on a Form 1099–INT from Second National Bank, 125 Oak Street, Lowell, MA 01850 (Employer Identification Number 98-7654322).
• In 2019, the Byrds aslo received interest income of $1,000 from municipal bonds.
• The home mortgage interest was reported on Form 1098 by Lowell Commercial Bank, P.O. Box 1000, Lowell, MA 01850 (Employer Identification Number 98-7654323). The mortgage (outstanding balance of $425,000 as of January 1, 2019) was taken out by the Byrds on May 1, 2015.
• The couple spent a weekend in Atlantic City in November and came home with gross gambling loss of $1,200 (no other gambling activities during the year).
• The Byrds do not keep the receipts for the sales taxes they paid and had no major purchases subject to sales tax. • All members of the Byrd family had health insurance coverage for all of 2019.
• Alice and Bruce paid no estimated Federal income tax.
PLEASE ANSWER WITH THE INTUIT PROCONNECT TAX SOFTWARE
In: Accounting