Native Americans and Early American Colonists

Native American and Early American Colonists Grade school and even beginning level college history classes have taught early American exploration from a largely one sided view of the conflict between early explorers and Native Americans. The traditional image of the Native Americans as the sole victims, is an oversimplification of the conflict that existed between early explorers, settlers and Native Americans. Through the readings from Columbus, Bradford and some selected Native American writings, the traditional view of the Native American victim will be challenged and a broader view of the conflict will be presented.Columbus set out to explore a new land under the Spanish flag to bring riches and fame to Spain and the throne. In his letter to Santangel, Columbus (1493) explained how he hoped to find â€Å"great cities† and â€Å"king[s]† but instead found a primitive people and settlements he described as â€Å"small hamlets† that he viewed quite devolved from the bustling civilizations of Europe (pg. 26). One can clearly see, that Columbus’s hopes of finding rich kingdoms and cultures were dashed; instead his presence was met with resistance from the â€Å"Indians†.This relationship with the natives was described by Baym et. all (2008) as â€Å"disordered and bloody† (pg. 25). These natives were mistreated even though one could argue that they â€Å"threw the first punch† but, as Baym et. all (2008) describes earlier in the chapter, the Natives were not merely victims. They strategically used alliances with explorers and settlers to further their own interests and disputes with warring tribes and peoples. William Bradford (1897) describes quite a different account of his coming to the new world. He was part of a group of â€Å"pilgrims† seeking religious freedom.He likens their arrival to the new world, to the story in Acts were the apostles are met with such aggression from barbarians â€Å"who were readier to fill their sides full of arrows† (pg. 60). Later on in his account, he describes an attack they received from the natives he described as â€Å"enemies† (pg. 64). Later on in his account, Bradford (1897) describes some awful events surrounding early accounts of settler and native interactions in which the Native Americans treated the english as â€Å"worse than slaves† and were sent around and â€Å"ma[d]e sport with† (pg. 70).One last important viewpoint to give credence to is that of the Natives themselves. This account is unique and oftentimes not told. The first story mentioned is that of the freeing of John Smith as a ceremonial act that the natives hoped would earn them respect from the English. This instead had the opposite effect and eventually brought about an attack from the natives which killed over 500 colonists. In a speech from Pontiac (1763) he expresses concern over his people forgetting their heritage and blaming the English for the polluting of his people’s culture and beliefs.He holds the English in complete responsibility and calls for their blood. The traditional view of the natives as the sole victim is an oversimplification of the problems revolving around immigration and cultural diversity. Just from these three personal accounts from the time period we have three very different views of the issue. So, to say that one peoples are the victim is a gross oversimplification and misrepresentation of history. Columbus, C. (1493). Letter to Luis de Santagel Regarding the First Voyage. In Baym, N. (Ed. ). (2008). The Norton Anthology of American Literature (seventh ed. pp. 24-28). New York, NY: W. W. Norton & Company, Inc. Bradford, W. (1897). Of Plymouth Plantation. In Baym, N. (Ed. ). (2008). The Norton Anthology of American Literature (seventh ed. , pp. 57-74). New York, NY: W. W. Norton & Company, Inc. Pontiac (1763). Speech at Detroit. In Baym, N. (Ed. ). (2008). The Norton Anthology of American Literature (seventh ed. , pp. 208-209). New York, NY: W. W. Norton & Company, Inc. Baym, N. (Ed. ). (2008). The Norton Anthology of American Literature (seventh ed. , pp. 1-218). New York, NY: W. W. Norton & Company, Inc.

Present Day Business Condition

In present day business condition, learning is the most vital asset, data with respect to rivalry predominantly concerned their piece of the overall industry and offer, appeared to be a palatable volume of data. These days requires data about the opposition. These data allude not exclusively to contenders piece of the overall industry and their offer, yet additionally to the level and structure of their costs, items and administrations quality-value relations, deals volume, extent of exercises, money streams, liquidity, dissolvability and productivity. Building data framework that backings the administration and basic leadership, and that can be a wellspring of aggressive edge, isn't a simple errand. Issue is the way to get quality and helpful data. In the present business focus, there are huge amounts of things and organizations open to fulfill the necessities of individuals and associations. Your ability to perceive and mishandle the features and related favorable circumstances of your thing or advantage and show how it is one of a kind or better than anything the resistance will outfit you with an engaged edge. The edge or favored angle will outfit your firm with the instruments to: Augmentation arrangements and bit of the pie. Upgrade general incomes for a given time period in new or existing markets. Assurance your survival in to an awesome degree centered markets. Become hard-to-copy advancing mixes. An Aggressive EDGE : To start, you ought to amass each one of the data assembled about your target showcase designs, customers, things and contenders. Recorded different market design components is a structure of the distinctive market plan segments you should study to distinguish your focused edge. Every association must have no short of what one ideal position to adequately fight in the market. In case an association can't recognize one or essentially doesn't have it, contenders soon beat it and power the business to leave the market. There are various ways to deal with achieve the ideal position however only two key sorts of it: cost or division advantage. An association that can achieve predominance in cost or partition can offer customers the things at cut down costs or with more elevated amount of detachment or more all, can fight with its adversaries. Foundation: Right off the bat the recognizable proof of your association's qualities and shortcomings is an essential errand that should be proficient before any focused edge can be created. The forceful edge your firm picks will depend upon the reasons your customer will buy a particular thing or organization. An upper hand implies you have to offer a few things your rivals don't. You have to recognize what it is your rivals do well, and don't do well. For example, if your resistance has one recipe that various customers go to that diner for, essentially imitating their equation won't add to your high ground. Instead of trying to copy your adversary's purposes of intrigue, invigorate your own specific to make a unique course of action of characteristics that can't be imitated. There's no convincing motivation to enroll some individual to do what you can do yourself, however consider using research firms to find information that isn't unreservedly open. Here are two or three instruments you can use to secure forceful information. Online outputs are a quick method for finding forceful information. Regardless, this chase will simply give information that has been made open. On area view of the contender's stopping territory, customer organization, volume and case of suppliers' transports, et cetera can yield significant information about the state of the contender's business. Outlines and gatherings can yield a great deal of data about contenders and things. Research surveys and focus gathering interviews generally give more all around perspectives from an obliged case. Forceful benchmarking is used to take a gander at the affiliation's exercises against those of its adversaries. In making specific connections inside an industry, an affiliation grabs information about customary publicizing practices, available workforce, and suppliers. Issues to Increase Aggressive Edge over your Rival in Business: Competition is awesome. For sure, a sound rivalry incites you to work more splendid with the advantages you have. To do thusly, utilize your gathering's intriguing blessings and build a business contenders wouldn't set out test. Despite whether distinctive associations in your industry attempt to undermine your expenses and take your customers, contemplate ways they can empower your startup to create.Keeping up a vital separation from absence of concern. Sole suppliers in an industry quickly quit progressing basically in light of the way that they never again have any need to. Tragically, they accidentally center around keeping up the current situation. Contenders have a penchant for keeping you on your toes.Building brand clout. Make it your focal objective to develop as the primary pro in your space of capacity. Your social event of individuals will value your thought expert and regularly pick you over various merchant s.Making care. Enemies compel you to assess your characteristics and deficiencies. Use your superpowers to make a more uncommon motivating force to customers. Grasp your shortcomings and find ways to deal with overcome them.Enabling partition. Contenders will dependably endeavor to offer better customer advantage, thing quality and advancing. In sound markets, buyers will ask for the best solutions for their specific needs. Isolate your commitments with the target of influencing gigantic motivating force for the customers you to serve.Mishandling industry designs. Contention signals strong client ask. It offers endorsement to what you are doing. In new markets, this is an opportunity to propel a creating design that will get buyers and the media amped up for your work.Molding startling affiliations. Make associations together with comparative associations. Exchange development and mechanical assemblies, expand the general market, cross propel each other's things and collaborate on n ovel research to educate purchasers. Perhaps one day, you may merge with, or pick up, your most noteworthy adversary.Shared learning. Watch the resistance intentionally. The learning and resources they have may be both favored and unmistakable over yours. Viably pick up from how they direct and build up their undertaking. A little while later, you will discover ways to deal with apply those lessons made sense of how to your business.Narrowing down a claim to fame. Some person will reliably be better than you at something – and that is okay. Customers justify the best things and organizations to fulfill their individual needs. To make a gainful business, focus your undertakings on making a smaller part of the general market astoundingly cheery. By narrowing your claim to fame, you develop a forceful edge that averts advance contention.Masterminding whole deal. Without contenders, most firms lose all ability to read a compass in the ordinary exercise of keeping up their busines s. As various associations join the market, you should start testing yourself to accomplish more.Sorting out customer needs. Instead of focusing your imperativeness on surpassing the restriction, place assets into moving toward a customer driven affiliation. Thusly, you will help buyer reliability and easily shield against powerful suppliers or dealers objective on taking your clients. By the day's end, it is your customers – not your opponent – who can speak to the choosing minute your business.

ECONOMICS Essay Example | Topics and Well Written Essays - 1500 words

ECONOMICS - Essay Example The recovering signs of the world economy due to good performance by the policymakers led to the fall of gold prices as the demand for gold fell due to higher return on investment in other class of assets. Gold, Long a Secure Investment, Loses Its Luster: Background overview During the period of economic recession, the prices of gold soared and became the sought after investment for the investors. The rise in the prices of gold meant that the world economy was not performing well. Due to the economic recession and the global financial meltdown from 2008 to 2011, the gold prices reached its highest peak in 2011. This could be observed from the producer price index as given below. Due to the crisis in the economy, the total factor productivity of the nations was hit and the factors of production were affected due to the downturn of the economies. The crisis in the economy gave rise to a situation of liquidity crisis. The fall in income levels of the people led to the fall in consumptio n demand in the economy. Due to this, the productivity of the business houses and industrial bodies fell. The fall in revenue and profitability led to the erosion of wealth of the shareholders and market investors. Along with this the erosion of confidence of the investors on the stock performance of the companies led to the fall of valuation of the companies and market indices. As a replacement for the investment in stock markets, the investors confided on the investment in gold markets (McGuire, 2010, p.37)1. The investment in gold was considered to be lucrative as prices of gold increased on the back of high demand for gold. An investment in gold offered higher returns on investment and there was no erosion of wealth from the amount of investment. Apart from that gold could be sold at any point of time and was considered as marketable investment. The presence of large number of buyers gave the opportunity to transform it into cash at any point of time. All these factors led to th e rise in the prices of gold. Investment in gold was deemed to be an investment that would in which the returns obtained would never be lowered (Northcott, 2010, p.46)2. The spurt in he gold prices over the last few years fuelled by weak economic conditions has been represented below. Demand and Supply Analysis The fluctuation in the price of gold could be explained from the demand supply curve for gold as given below. As the demand for gold rose in a weaker economic condition, the demand curve shifted from position 1 to 2 depicted by the red line. As a result, in order to maintain a position of equilibrium with the supply, the prices of gold rose from P1 to P2. The reverse is also true for fall in prices for gold as a result of fall in demand. Gold looses its luster: Rational behind plunge in gold prices The unexpected plunge in the prices of gold in recent times has occurred as result of recovery in the performance of the economies all over the world. The correct strategies adopte d by the policymakers in order to maintain a proper balance of supply and demand in the economy, controlling inflation through appropriate interest rates, fiscal and monetary policies have led to turn around of the economies of the world. The economic reforms and recovery from the economic recession led to improvement in the performanc

Tesco Plc Essay Example | Topics and Well Written Essays - 2250 words

Tesco Plc - Essay Example Through the concept of corporate social responsibility (CSR), companies have been forced top look beyond the economic returns of the business but also consider their impact on the environment and community around them. The importance of value creation through positive stakeholder relationships has an impact of significantly increasing the profitability of a firm (Edwards 1998). The increase in environmental legislation and the emerging trends of ethical consumers, the incorporation of social responsibility in the business strategy is more of a necessity in the competitive market. This report seeks to analyze the annual report of Tesco Plc for the year 2011 to investigate the way it accounts for its social, ethical and environmental impact. The report further compares the performance of the company with other companies in terms of environmental policies. Another aspect that is considered in this report is the nature of reporting that the company uses and recommendations for future str ategies to improve on the presentation of the company’s stakeholder’s strategy. 2. Business Review of Tesco Plc in 2011 Tesco is the most dominant retailer in the United Kingdom with a market share of about thirty percent. The company has a presence in several countries. It reported revenues in excess sixty billion in the financial year 2010/2011. The core purpose of Tesco Plc is to accord quality service to customers. The company’s goals are aligned to this vision that is clearly aimed at giving the company a competitive advantage in its market. The company has underpinned its commitment to the community and the environment by having a goal to put its responsibilities to the communities that they serve. To achieve this goal, the company has broken down its policy on environment and social issues into five key performance indicators that it uses to analyze the impact of its strategies. The key performance indicators on the social and environment issues for Tesco Plc for the period ending 2011 are analyzed below. 2.1 Responsibility in Buying and Selling of Products The customers require safe and affordable products. The company also lays emphasis on ensuring that the products that it sells are sourced in a way that is robust and meeting the required standards. Tesco has laid out strategies to help improve its relations with the suppliers through a program called â€Å"Trading Fairly†. However, the impact of this program cannot be quantified because the metrics of measuring the results are not available. In fact, it can be argued that these programs are publicity stunts since the same buying processes were used prior to the introduction of this program are still in place. A case in point is the widely documented practice by Tesco Plc to buy potatoes from the grey market instead of the official supply chain through Tyrells Crisps. The other issue of fair trade has been recently brought to the fore due to the price undercutting by super markets on bananas. The price wars between Tesco and Asda almost brought down the banana industry. The supermarkets opted to lower their margins and in turn reduced the amount they pay to their suppliers. According to Michaels (2004), the demand for cheaper products by supermarkets has become a ruthless way by the supermarkets to exploit the supplier. The net effect of lower supplier prices is that the workers in the farms are paid much less and this impacts negatively on the society. Sainsbury was the first supermarket to announce that it will sell one hundred percent fair trade bananas. Other supermarkets have followed suit but it is worth noting that Tesco was reluctant to commit to this cause. The increased money paid to the suppliers will have a positive impact on the community where the products are sourced from. The style of disclosure of the responsible sourcing and buying of products the Tesco

Crime and punishment Coursework Example | Topics and Well Written Essays - 2500 words

Crime and punishment - Coursework Example However, in a revocation hearing, guilt has been established so therefore some rights and rules of evidence are more relaxed. Even though the rights of a defendant in a criminal trial are more structured and strict than those of an offender whose probation is being revoked, the offender will always have the right to defend his or her position. In understanding the process to revoke probation, it must first and foremost be understood that without due process, probation cannot be revoked. This process is important in order to ensure that revocation is done for reasons that are valid and noteworthy and that the probation has been violated in such a way to warrant its revocation. Someone who has violated probation and will have it revoked will be afforded fewer rights than someone who has just been arrested. This does not mean they have no rights. The rights that they will be afforded is as follows: 1. Written notice of the violations before the revocation hearing 2. The right to see and hear the evidence against them. 3. The opportunity to be heard in person and to present witnesses and documentary evidence in their favor. 4. The right to confront and cross-examine the witnesses against them. 5. A hearing panel made up of neutral members. 6. A written statement by the hearing panel, including the evidence relied on and the reasons for revoking probation (Samaha 416). Revocation of probation is the result of having violated the terms of probation which may vary from person to person depending on the charges that have been levied against them. According to Sheb, there is a two step process in revoking probation on a federal charge as described in the Federal Rules of Criminal Procedure. The first step is a preliminary hearing which will allow the magistrate to assess whether or not there is probable cause to believe that a violation has occurred and that it is a just sanction to proceed with revoking the probation. The requirements for the preliminary hearing includ e the right to council for the defendant and that the proceeding be recorded by a court reporter. The defendant must be provided with written notice of the hearing which will include the violation with which the defendant is being charged. The defendant has the right to appear and to question any witness to his or her violation unless it is determined by the judge that the witness does not have to appear. At this point, the judge will determine whether the violation is valid and must go onto a revocation hearing, or if the charge is without validity and may be discharged (228). The next step is the revocation hearing. This hearing must be conducted within a reasonable time from the preliminary hearing and from the time of being taken into custody. This hearing can be waived by the defendant. The person is entitled to receive a written notice of this hearing, a list of evidence that is against them, notice of the right of council, and the right to make a statement and present any evi dence that pertains to their innocence or mitigating circumstance. At this hearing the formal revocation can be enacted (Sheb 229). While the rights for this procedure are more relaxed than the formalized rights for a trial, the defendant still has the right to defend his position. Some of the differences between a criminal trial and a probation revocation hearing is that in a criminal trial hearsay evidence cannot be presented. In a criminal trial evidence

Comparing two story( A small good thing & The girl with the pimply Essay

Comparing two story( A small good thing & The girl with the pimply face) - Essay Example The two doctors’ communication with their patients demonstrates how different the two doctors approach their jobs. The central tenet of Williams’ â€Å"The Girl with the Pimply Face† is that doctors are human and are subjected to human emotions. Carver approaches the topic from the other side by displaying the harmful effects of a doctor who is professional, but patronizing in â€Å"A Small, Good Thing†. The primary theme of Williams’ story is the sensitivity of the doctor toward a patient. Williams’ doctor is compassionate with his patients. He speaks with his patient’s family using informal everyday language. This shows the doctor’s humanity, his caring response to a family facing several problems. In this case, he has the desire to help, gives his work freely, and possesses a genuine interest in the family. The doctor does not criticise his patients when they are not able to pay. Even after promises of payment are not realized, the doctor continues to come back. The doctor returns after diagnosing the baby of the family with a bad heart. Even though he knows that the baby’s health will decline, with or without his help, the doctor tries to ease the family’s pain with his visits. The doctor also takes it upon himself to help his infant patient’s sister with her acne and the blemishes on her legs even though he was only responsible for the baby. Williams’ story emphasises the willingness of this doctor to step over professional boundaries and help the girl with advice and money even though she was not his patient. â€Å"The Girl with the Pimply Face† also shows that the doctor’s motives are not entirely unselfish. He is attracted to his patient’s sister. Although the doctor would never compromise his position by ever making inappropriate gestures towards the girl, but his attractions is a strong motive for his interest in the family. Even after finding out that the mother is a

Should violent video games be banned or regulated Essay

Should violent video games be banned or regulated - Essay Example This paper tends to discuss that violent video games should be banned since they create a negative impact upon the minds of the players, especially when they are at the tender age of adolescence (thesis). Nearly every teenager knows the names of some of the most violent video games like Mortal Combat, Resident Evil, Marvel vs. Capcom, Doom, Manhunt, Dead Rising, Gears of War, Grand Theft Auto, and the list continues. All of these games require the player to kill, shoot, slash, and stab their enemy using bombs, swords, and chainsaw. They have to kill the police officers to get to the target, hit the pedestrians, split the opponent using chainsaws, and what not. Preteens and teens want to play these games more than non-violent ones, and then, they apply it in their real lives because they are not capable of differentiating the gaming world from the real world. Last decade has shown an increase in violence rate among children in the United States, and studies show that children are incr easingly being treated for anger management thanks to the growing use of video games. Children tend to have less-developed ability of decision-making or critical thinking, and so, they cannot realize what is wrong with what they are viewing. Thus, their minds learn or absorb every act they see. The reason is that violent video games succeed in getting the person involved in the character he is playing, so much so that he starts playing that character in the real life. For example, females are normally portrayed as weaker and powerless characters in video games. When children watch this, they apply it real life because they would have learnt to disrespect women. This is only an example. There are thousands of different facets of violence and aggression that the players learn from violent video games. The tragedy is that the effect doubles in strength when the player is at the tender age of adolescence. Previous generations used to play doctors, police, thieves, chefs, and the oomph g enerated by the flight of the imagination would get used up in playing the game physically. But with video games, that energy comes out in the form of frustration, aggression, and violence. Smith, Lachlan and Tamborini suggest that mature games are more violent that those rated for general audience, and feature child perpetrators with frequent gun violence. When a child watches such violent acts and plays the character using his hands and mind, the energy generated by the flight of the imagination keeps the physical responses from getting expressed. So, when this energy gets its chance, it gets expressed in the real life. This is why children who watch violent video games are violent in their nature too. For example, parents do not know why their child, who plays Resident Evil on his PlayStation 3, is becoming violent day by day; beats his younger siblings; remains isolated most of the time; and, has complaints coming from his school that he bullies his class fellows. A check on his routine activities will show that the credit goes to video games if he plays some. All of these negative impacts make one claim that violent video games should be banned. Researchers like Bartholow and Anderson have found that teenagers who play violent video games have higher heart rate, get emotionally aroused easily, and are more aggressive verbally and physically than those who play non-violent games. Thus, the nature of video games played has an adverse

Management Overview Essay Example | Topics and Well Written Essays - 4000 words

Management Overview - Essay Example These approaches are preferred because companies have little control over external factors in their environment, including their competition, but have a great deal of control over internal factors within the firm. This is especially true with PLCs seeming to shorten over time, most notably in technology fields. With all this taken into account, it makes sense for organizations to focus internally to attempt to gain a competitive advantage, rather than being reactionary and responding to external factors consistently (Teece, 1997), which should help them not only get a competitive advantage in the first place but also sustain it over the long term. In this paper, the writer will attempt to analyze changes that have occurred in the field of strategic management. Dell Corporation will serve as the model for this analysis, as its business model has been praised recently due to its unique approach to markets. 1.2 Dell Computers Product Lie Spans have been shortening from some time in Dell ’s sector (computing), which obviously can create problems for a corporation in terms of creating a long term sustained advantage. This is because any advantage gained from innovation is unlikely to last more than one product life cycle, after which competitors will adapt or copy such innovations, meaning that without constant innovation, a company will quickly lag behind. This also means that a company must display excellent flexibility, and be able to change their strategic focus quickly: in Dell’s case, for instance, a weakening desktop market might be inevitable, so they would need to be able to quickly capitalize on other business such as laptops or accessories. Competition in this sector changes frequently, as new firms enter, and new technologies redefine the expectations of the consumer base. While this leads many firms to jump head first into this sector, it has also led to some existing it, when they cannot compete with their competitors, cope with external d ynamics, or maintain enough innovation to keep them competitive with their competitors in the long term. Failed companies within such organizations also frequently do not possess the necessary resources to create or maintain competitive posture: thus the problem can be viewed as both resource based and externally driven. HP, for instance, could not maintain competitive advantage in the tablet market or the PC business because of competition from firms like Apple and Google (Cellan-Jones, 2011), making the company rely solely on its printer/scanner division to stay afloat. Lawson and Samson (2011) show that competitive advantage in this field depends on organizational asset positions as well as strategic flexibility and strategic decisions; there are distinctive flavors of strategic paths that seem to lead to success in this field. This flavor touches on the way the firm is put together and operated, and has a lot to do with organizational culture including working conditions, expect ation, management systems and so on. Samson and Lawson create a definition of the dynamic corporation – which will maintain competitive advantage, which includes things like cross-functional resources and management fascilities, which spans research

Race Colors Judgement Essay Example for Free

Race Colors Judgement Essay The criminal justice system in the United States is one of the many places that I believe stereotypes are formed. For example, African-Americans make up only 13% of the U. S. population but represent 46% of the inmate population who have received sentences of more than one year (Hart, 2006, p. 1). Another example of a racial disparity can be seen the percentage of African-Americans who are drug users (14%) and those sentenced for drug offenses (53%) (Sentencing Project, 2009 p. 3). More African-American men are in prison or jail, on probation or parole then were enslaved in 1850, before the Civil War began,† (Alexander, 2010). However, this is not just a problem within the African-American community. More than 60% of the people in prison are now racial and ethnic minorities and three-fourths of all persons in prison for drug offenses are people of color (www. sentencingproject. org). The Bureau of Justice Statistics shows, that the likelihood for an African-American or Hispanic to be imprisoned is, 18. % for African-Americans and 10% for Hispanics, while the likelihood for Whites is 3. 4% (Bureau of Justice Statistics, 2005). Brennan and Spohn (2009) showed in their study, â€Å"The Joint Effects of Offender Race/Ethnicity and Sex on Sentence Length Decisions in Federal Courts†, that African-American males received a significantly longer sentence (93 months) than White males (86. 2 months) (Brennan Spohn, 2009). These are just some of the numbers, which cannot be ignored. An important question to ask; why are these racial disparities happening? In the study â€Å"White juror bias: An investigation of racial prejudice against Black defendants in the American courtroom†, Sommers Ellsworth (2001) have a quote, which, I think, sums up the reasoning for studying race and its effect on juries, it came from one of my favorite movies: â€Å"In our courts, when it is a white man’s word against a black man’s, the white man always wins. They’re ugly, but those are the facts of life†¦The one place where man ought to get a square deal is a courtroom, be he any color of the rainbow, but people have a way of carrying their resentments right into the jury box† (From To Kill a Mockingbird, Lee, 1960, p. 20). The thinking by many social psychologists is â€Å"Racism still exists in our society today but is no longer endorsed by explicit racist beliefs or overt acts of prejudice† (Sommers Ellsworth, 2003). Instead it’s a â€Å"Subtle, implicit, or aversive form of racism† (Sommers Ellsworth, 2003). Whites in our society are taught to embrace egalitarianism (equality) and make a conscious effort to behave non-prejudice, or have non-bias beliefs. However, that does not mean that they still don’t harbor prejudicial attitudes. In a trial setting aversive racism and race salience, or racially charged vs. racially neutral, go hand and hand. Studies have concluded, a trial that is racially charged reminds jurors of their egalitarianism, but in a trial not racially charged a jurors’ motivation to avoid being prejudice is not triggered; instead they demonstrate their racial bias (Sommers Ellsworth, 2001). It is the run of the mill trials where juror biases are displayed. White jurors need to be â€Å"reminded† that they should not have a bias. By â€Å"reminding† them, by a racially motivated incident, jury voir dire, jury instructions before deliberation, and others, White jurors are less likely to demonstrate racial bias towards an African-American defendant. Jury composition or heterogeneity vs. homogeneity groups, is theorized to be a huge factor in overall group decision-making skills. This is especially important in the jury decision-making process and verdicts because minorities are underrepresented on a jury. Sommers’s study â€Å"Racial Diversity and Group Decision Making† (2006) concluded, a jury, which has heterogeneity, rather than homogeneity considers a wider range of perspectives and information (Sommers, 2006). It was the diversity of the group influence on the White juror more than the performance of the African-American juror in the group (Sommers, 2006). This is not to say that the African-American juror did not perform well. Since many juries are not racially diverse, Whites on a jury may forget their egalitarian values, may not consider a wider range of perspectives and information, and will spend less time on their decisions. In-group bias is when people show a strong preference for fellow in-group members and tend to malign out-group members (Sommers Ellsworth, 2000). Thomas Pettigrew, current Research Professor of Social Psychology at the University of California, in his 1979 study demonstrated that negative behaviors of in-group members were attributed to situational forces but negative behaviors of out-group members were attributed to inherent dispositions, which is the opposite from positive behavior attribution (Sommers Ellsworth, 2000). This is a particularly important theory because juries for criminal trials are taking in facts pertaining to the negative behavior of a defendant who is either from their in-group or out-group. Systematic information processing is conceptualized as â€Å"Comprehensive analytic orientation to inform processing in which perceivers access and scrutinize a great deal of information for its relevance to their judgment task† (Tamborini et al. , 2007) Heuristic processing is conceptualized as â€Å"A more limited mode of information processing that requires less cognitive effort and fewer cognitive resources than systematic processing† (Tamborini et al. , 2007) Simple stated, heuristic information processing are shortcuts using previous knowledge and stereotypes, which influences peoples’ judgments. During a trial, jurors take in enormous amounts of information and when deliberating they tend to fill in the missing information with past experiences or stereotypes about certain crimes and criminals. This is not their intention, however it is how people cognitively process information-we put information into or take it out of certain categories. There are three main research methods used to study race and its effects on juries (Sommers Ellsworth, 2003). Archival analysis of actual cases is ideal but there are a lot of confounding variables, which are hard to measure and control statistically (Sommers Ellsworth, 2003). Another method used is post-trial juror interviews. This method is useful because you are asking direct questions of the jurors, who were part of the real trials. However, it is time consuming, has a small sample size, and relies on self-reporting by jurors (which in unreliable) (Sommers Ellsworth, 2003). The third method is mock juror experiments, which relies on the experimental method of social psychology and allows the experimenters to control the confounding variables (Sommers Ellsworth, 2003). There are some downfalls to using mock juror experiments as well, such as using college students as participants, written trial summaries, instead of witnessing a real trial, and the decision made by mock jurors have no real consequences (Sommers Ellsworth, 2003). According to Sommers and Ellsworth (2003) it is best to use multiple methods. For example compare archival data to mock jury data. As I stated earlier, aversive racism and race salience (racially charged vs. racially neutral) in trials go hand and hand. Sommers and Ellsworth (both social psychologists) first studied race salience in their study, â€Å"Race in he Courtroom: Perceptions of Guilt and Dispositional Attributions† (2000). Since the theory of aversive racism (modern or subtle) states, Whites are more motivated to â€Å"appear† non-prejudice when racial issues are salient or prominent. They found that when a trial involves race salience the race of the defendant did not influence the White jurors (Sommers Ellsworth, 2000). However, when a trail did not have race salience, the African-American defendants were found to be more guilty, aggressive, and violent by the White juror then the White defendant. This could have a profound effect, since Whites are not caught up in the day to day of racial issues, they may not take notice to the most subliminal racial issues in a trial. It may cause them to revert back to the more overt form of racism without even consciously knowing they are being racist or displaying their biases. A more recent study, â€Å"Diversity and Fairness in the Jury System†, conducted for the Ministry of Justice Research Series, by Thomas and Blamer (2007) concluded when a trial is racially charged (race salience), conviction rates for African-American defendants were lower. However, the conviction rate between White jurors and African-American jurors for African-American defendants were no different (Thomas Balmer, 2007) (44% and 43%). In trials that were racially neutral, White jurors had low conviction rates for African-American defendants, while African-American jurors had high conviction rates for White defendants and low conviction rates for African-American defendants (Thomas Balmer, 2007). This was a very interesting finding because in the Sommers and Ellsworth studies (2000, 2001) African-American jurors showed leniency both in race salience and non-race salience trials. Thomas and Balmer (2007) point out that in the Sommers and Ellsworth study that jurors did not decide cases as part of a jury with any deliberations (Thomas Balmer, 2007). The results in the Thomas and Blamer study showed that individual jurors had difference conviction rates, but as a jury there was no difference between race salience and non-race salience trails (Thomas Blamer, 2007). None of the juries (there were 8 in all) in the Thomas and Blamer (2007) study convinced the White defendant, The juries in England and Wales where this study took place have the same makeup as juries in the United States, majority White (Thomas Balmer, 2007). That makes a nice segway into my next theory of jury composition because it appears that they dynamic of a racially mixed jury helped ensure individual biases were not allowed to dictate verdicts (Thomas Balmer, 2007). Justice Thurgood Marshall said, â€Å"Diverse juries enjoy wider ranging discussions because White and Black jurors bring different experiences and perspectives to the jury room† (Sommers, 2006). Not only do African-American jurors bring different experiences but also, as we saw in the Thomas and Balmer (2007) study a racially mixed jury might help to ensure individual biases are not allowed to dictate verdicts. Again, referring to a study by Sommers (the leading researcher in this field) in which he specifically studies â€Å"The multiple effects of racial composition on jury deliberations† (Sommers, 2006). Having African-Americans (or minorities in general) on a jury can bring two different types of diversity-deep-level diversity and surface-level diversity (Sommers, 2007). Both can affect information exchange in different ways. Deep-level diversity brings the expertise, attitudes, and values of the individual members to the deliberation room (Sommers, 2007). Surface-level diversity brings members’ demographics and social category membership into the deliberation room (Sommers, 2007). Sommers’ (2006) found diverse groups spent more time deliberating, made fewer factual errors, and if there was an error it was more likely to be corrected, more open-mindness, and less resistance to discussions of controversial race topics (Sommers, 2006). The homogenous jury was the opposite (Sommers, 2006). Those results showed the affect deep-level diversity could bring to a jury. However, another aspect, which will bring me back to the theory of aversive racism and race salience, is the affect having diversity has on a White juror. By having a racially diverse jury, the White jurors have the issue of race and egalitarian values in the forefront of their minds. The White jurors are avoiding seeming bias. Sommers et al. , (2008) conducted a study to see if there are â€Å"Cognitive effects of racial diversity in a group. † The study found that Whites in a diverse group process information more thoroughly. They had no interaction with a diverse group member, it was simply being aware of a diverse group composition, which impacted the cognition of White members. It even improved reading comprehension of race-relevant passages, especially when Whites expected to have race-relevant conversation. This is important in a legal context as well. If a White juror’s cognitive ability, and information processing is improved they will use systematic processing which is â€Å"A comprehensive, analytic orientation to information processing in which perceivers access and scrutinize a great deal of information for its relevance to their judgment task†, instead of heuristics processing or shortcuts in their decision making (Tamborini et al. 2007). The Supreme Court attempted to make juries more racially diverse â€Å"Batson prohibition against race-based peremptories was based on two assumptions: (1) a prospective juror’s race can bias a jury selection judgments; (2) requiring attorneys to justify suspicious peremptories enables judges to determine whether a challenge is, indeed, race-neutral† (Batson v. Kentucky, 476 U. S. 79 (1986). To summarize the findings, White jurors tend to show their bias towards African-American defendants when the trial is not racially charged because they are not motivated to conceal their bias (aversive racism and egalitarian views). In homogenous juries Whites are more like to be bias, spend less time on their decisions, make more errors, consider fewer perspectives, are not motivated to conceal their bias. Also, when there is information overload jurors use heuristics (shortcuts) to process information, rather than a systematic review of the information. Tis effect, of using shortcuts, produces bias judgment for both African-American jurors and White jurors. All the aforementioned could be cause for the bias decision making of jurors and juries. However, there are positives that can be found throughout these studies. For instance, racially diverse juries, and race salience trials can help alleviate the biases by jurors and juries. It also proves that not all White juries are affected by the race of a defendant (in certain situations). Race and its effect on jury decisions is a topic that will be studied for years to come because of the complex nature of a jury and modern racism. Although studies have shown bias decision-making by White jurors there is still not enough statistics to make a causal connection. Research has also shown ways in which a jury’s bias can be minimized. The jury is one of the backbones of the court system, because of this, it is imperative that we continue to study juror bias and how to minimize their bias through different trial techniques and policies and procedures.

International Mergers Essay Example for Free

International Mergers Essay Recent years have seen waves of mergers and acquisitions occurring in the international arena. Whilst the nature of such M A activity has enlarged from being mostly IT focussed in the 1990s to include other areas like consumer goods, automobiles, and metals in the 2000s, its intensity remained unabated until the onset of the global financial crisis in 2008. Although M A activity in the domestic space has continued to occur despite the high failure rate of such initiatives, international M A s face the additional challenge of having to overcome issues of different national cultures. The recent break-up of Daimler Chrysler evidences the difficulties that such initiatives face and the enormous harm that can occur if they do not work. This dissertation attempts to investigate the numerous challenges that confront the managements of the two concerned organisations, the hazards posed by such challenges, and the measures that can be adopted to overcome them. Table of Contents Serial Details Page Abstract 2 1. Introduction 5 1. 1. Background and Overview 5 1. 2. Definition of Problem 8 1. 3. Aims and Objectives 12 2. Literature Review 14 2. 1. Motivation for International M A Activity 14 2. 1. 1. Strategic Objectives 14 2. 1. 2. Other Drivers of International Mergers and Acquisitions 18 2. 2. The Cultural Context 20 2. 2. 1 National Culture 20 2. 2. 2. Organisational Culture 23 2. 2. 3 The Impact of Culture on International Mergers and Acquisitions 25 2. 2. 4 Overcoming Cultural Differences in International Mergers and Acquisitions 32 3. 0 Research Methodology 38 3. 1. Research Questions 38 3. 2. Choice of Research Methodology 38 3. 3. Quantitative and Qualitative Methods 39 3. 4. Choice of Methodology 41 3. 5. Primary and Secondary Data 41 3. 6. Ethics 42 4. 0 Data Collection 43 5. 0 Findings and Analysis 48 6. 0 Conclusions 52 Bibliography 53 1. Introduction 1. 1. Background and Overview Corporate mergers and acquisitions (M A) are an accepted form of external growth and are becoming increasingly common with time. With business corporations having realised the benefits of M A activity in terms of growth in sales, increase in capacity, accessing of new markets, obtaining of technology and skills, acquisition of brands, savings in costs, and achievement of synergies in areas of sales, production, and costs, it forms an integral component of the objectives and strategies of most forward looking and ambitious business firms (Gaughan, 2002). Two decades of globalisation, along with progressive development of technology, intensification of competition, increasing pressure on costs, and the continual emergence of new equal skill/ lesser cost production and service centres in Asia, East Europe, and South America are accentuating the need for consolidation and for achieving leadership in costs and quality, the basic tenets of Michael Porter’s theory of competitor advantage (Gaughan, 2002). Such developments are also increasing the number of companies searching for appropriate M A opportunities. The enormous changes that have taken place in the global, economic, political, and trade scenario have added another dimension to the issue of M A activity, that of international mergers and acquisitions (IMA). These pertain to those mergers and acquisitions that take place beyond the borders of specific countries and which are also known as global or cross border M As. The collapse of the Soviet Union, the crumbling of the Berlin Wall, the emergence of East European countries, the formation of the European Union, and the dismantling of trade barriers led to a significant increase in M A activity between European countries. Apart from the remarkable developments in Europe, the last two decades also saw a wave of trade liberalisation and economic and financial reforms sweep through the developing world, and the emergence first of China and then of India on the global economic scene, bringing with them huge markets, strong production and service skills and cheap costs (Gaughan, 2002). With western businesses having realised the import of the enormous business opportunities that are constantly being generated on a global basis, the lid has been taken off IMA activity, which is now increasing furiously, particularly in the USA, the UK, and Europe. â€Å"While USA has always been the pioneer in merger and acquisition activities, UK too has registered high levels of mergers and acquisitions. With the European countries gaining momentum in mergers and acquisitions, international mergers and acquisitions also received a major boost. † (International Mergers and Acquisitions, 2009) IMA is taking place in â€Å"different forms, for example horizontal mergers, vertical mergers, conglomerate mergers, congeneric mergers, reverse mergers, dilutive mergers, and accretive mergers† (International Mergers and Acquisitions, 2009). Whilst IMAs are also driven by the same motives as regular M A activity, international M A helps companies in accessing markets in distant lands, allows companies to build global competitive advantage, and otherwise leads to build up of Foreign Direct Investment. IMA activity is also far more complex than regular M A actions because of the presence of far greater complexities that arise from companies having to deal with different political structures, governmental regulations and policies, economical situations, and investment and other laws (Gaughan, 2002). Despite the presence of such obstacles, international M A activity was gathering pace until the onset of the financial crisis, which has effectively put all commercial and business activity in a state of suspended animation. â€Å"2006 was a record year for acquisitions worldwide when, for the first time, the annual value of these transactions exceeded US$ 4 trillion, and cross-border acquisitions alone amounted to a record high of US$ 1. 3 trillion (Larsen, 2007). This trend continues in 2007, given that the transaction value of global acquisitions in the first three months of the year reached US$ 1. 13 trillion, setting up a record for the busiest first quarter in acquisition history. † (Rottig, 2007) The size of North American IMA activity increased practically by 100 % in 2006 to USD 242 billion from USD 132 billion in 2005. The value of IMA deals in Europe in 2006 touched USD 451 billion (Rottig, 2007). Whilst the most of IMA activity took place in the US, it was followed by the UK and Germany (Rottig, 2007).

Medical Data Analytics Using R

Medical Data Analytics Using R 1.) R for Recency => months since last donation, 2.) F for Frequency => total number of donation, 3.) M for Monetary => total amount of blood donated in c.c., 4.) T for Time => months since first donation and 5.) Binary variable => 1 -> donated blood, 0-> didnt donate blood. The main idea behind this dataset is the concept of relationship management CRM. Based on three metrics: Recency, Frequency and Monetary (RFM) which are 3 out of the 5 attributes of the dataset, we would be able to predict whether a customer is likely to donate blood again based to a marketing campaign. For example, customers who have donated or visited more currently (Recency), more frequently (Frequency) or made higher monetary values (Monetary) are more likely to respond to a marketing effort. Customers with less RFM score are less likely to react. It is also known in customer behavior, that the time of the first positive interaction (donation, purchase) is not significant. However, the Recency of the last donation is very important. In the traditional RFM implementation each customer is ranked based on his RFM value parameters against all the other customers and that develops a score for every customer. Customers with bigger scores are more likely to react in a positive way for example (visit again or donate). The model constructs the formula which could predict the following problem. Keep in repository only customers that are more likely to continue donating in the future and remove those who are less likely to donate, given a certain period of time. The previous statement also determines the problem which will be trained and tested in this project. Firstly, I created a .csv file and generated 748 unique random numbers in Excel in the domain [1,748] in the first column, which corresponds to the customers or users ID. Then I transferred the whole data from the .txt file (transfusion.data) to the .csv file in excel by using the delimited (,) option. Then I randomly split it in a train file and a test file. The train file contains the 530 instances and the test file has the 218 instances. Afterwards, I read both the training dataset and the test dataset. From the previous results, we can see that we have no missing or invalid values. Data ranges and units seem reasonable. Figure 1 above depicts boxplots of all the attributes and for both train and test datasets. By examining the figure, we notice that both datasets have similar distributions and there are some outliers (Monetary > 2,500) that are visible. The volume of blood variable has a high correlation with frequency. Because the volume of blood that is donated each time is fixed, the Monetary value is proportional to the Frequency (number of donations) each person gave. For example, if the amount of blood drawn in each person was 250 ml/bag (Taiwan Blood Services Foundation 2007) March then Monetary = 250*Frequency. This is also why in the predictive model we will not consider the Monetary attribute in the implementation. So, it is reasonable to expect that customers with higher frequency will have a lot higher Monetary value. This can be verified also visually by examining the Monetary outliers for the train set. We retrieve back 83 instances. In order, to understand better the statistical dispersion of the whole dataset (748 instances) we will look at the standard deviation (SD) between the Recency and the variable whether customer has donated blood (Binary variable) and the SD between the Frequency and the Binary variable.The distribution of scores around the mean is small, which means the data is concentrated. This can also be noticed from the plots. From this correlation matrix, we can verify what was stated above, that the frequency and the monetary values are proportional inputs, which can be noticed from their high correlation. Another observation is that the various Recency numbers are not factors of 3. This goes to opposition with what the description said about the data being collected every 3 months. Additionally, there is always a maximum number of times you can donate blood per certain period (e.g. 1 time per month), but the data shows that. 36 customers donated blood more than once and 6 customers had donated 3 or more times in the same month. The features that will be used to calculate the prediction of whether a customer is likely to donate again are 2, the Recency and the Frequency (RF). The Monetary feature will be dropped. The number of categories for R and F attributes will be 3. The highest RF score will be 33 equivalent to 6 when added together and the lowest will be 11 equivalent to 2 when added together. The threshold for the added score to determine whether a customer is more likely to donate blood again or not, will be set to 4 which is the median value. The users will be assigned to categories by sorting on RF attributes as well as their scores. The file with the donators will be sorted on Recency first (in ascending order) because we want to see which customers have donated blood more recently. Then it will be sorted on frequency (in descending order this time because we want to see which customers have donated more times) in each Recency category. Apart from sorting, we will need to apply some business rules that have occurred after multiple tests: For Recency (Business rule 1): If the Recency in months is less than 15 months, then these customers will be assigned to category 3. If the Recency in months is equal or greater than 15 months and less than 26 months, then these customers will be assigned to category 2. Otherwise, if the Recency in months is equal or greater than 26 months, then these customers will be assigned to category 1 And for Frequency (Business rule 2): If the Frequency is equal or greater than 25 times, then these customers will be assigned to category 3. If the Frequency is less than 25 times or greater than 15 months, then these customers will be assigned to category 2. If the Frequency is equal or less than 15 times, then these customers will be assigned to category 1 RESULTS The output of the program are two smaller files that have resulted from the train file and the other one from the test file, that have excluded several customers that should not be considered future targets and kept those that are likely to respond. Some statistics about the precision, recall and the balanced F-score of the train and test file have been calculated and printed. Furthermore, we compute the absolute difference between the results retrieved from the train and test file to get the offset error between these statistics. By doing this and verifying that the error numbers are negligible, we validate the consistency of the model implemented. Moreover, we depict two confusion matrices one for the test and one for the training by calculating the true positives, false negatives, false positives and true negatives. In our case, true positives correspond to the customers (who donated on March 2007) and were classified as future possible donators. False negatives correspond to the customers (who donated on March 2007) but were not classified as future possible targets for marketing campaigns. False positives correlate to customers (who did not donate on March 2007) and were incorrectly classified as possible future targets. Lastly, true negatives which are customers (who did not donate on March 2007) and were correctly classified as not plausible future donators and therefore removed from the data file. By classification we mean the application of the threshold (4) to separate those customers who are more likely and less likely to donate again in a certain future period. Lastly, we calculate 2 more single value metrics for both train and test files the Kappa Statistic (general statistic used for classification systems) and Matthews Correlation Coefficient or cost/reward measure. Both are normalized statistics for classification systems, its values never exceed 1, so the same statistic can be used even as the number of observations grows. The error for both measures are MCC error: 0.002577   and Kappa error:   0.002808, which is very small (negligible), similarly with all the previous measures. REFERENCES UCI Machine Learning Repository (2008) UCI machine learning repository: Blood transfusion service center data set. Available at: http://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center (Accessed: 30 January 2017). Fundation, T.B.S. (2015) Operation department. Available at: http://www.blood.org.tw/Internet/english/docDetail.aspx?uid=7741pid=7681docid=37144 (Accessed: 31 January 2017). The Appendix with the code starts below. However the whole code has been uploaded on my Git Hub profile and this is the link where it can be accessed. https://github.com/it21208/RassignmentDataAnalysis/blob/master/RassignmentDataAnalysis.R library(ggplot2) library(car)   # read training and testing datasets traindata à ¯Ã†â€™Ã… ¸Ãƒâ€šÃ‚   read.csv(C:/Users/Alexandros/Dropbox/MSc/2nd Semester/Data analysis/Assignment/transfusion.csv) testdata à ¯Ã†â€™Ã… ¸Ãƒâ€šÃ‚   read.csv(C:/Users/Alexandros/Dropbox/MSc/2nd Semester/Data analysis/Assignment/test.csv) # assigning the datasets to dataframes dftrain à ¯Ã†â€™Ã… ¸ data.frame(traindata) dftest à ¯Ã†â€™Ã… ¸ data.frame(testdata) sapply(dftrain, typeof) # give better names to columns names(dftrain)[1] à ¯Ã†â€™Ã… ¸ ID names(dftrain)[2] à ¯Ã†â€™Ã… ¸ recency names(dftrain)[3]à ¯Ã†â€™Ã… ¸frequency names(dftrain)[4]à ¯Ã†â€™Ã… ¸cc names(dftrain)[5]à ¯Ã†â€™Ã… ¸time names(dftrain)[6]à ¯Ã†â€™Ã… ¸donated # names(dftest)[1]à ¯Ã†â€™Ã… ¸ID names(dftest)[2]à ¯Ã†â€™Ã… ¸recency names(dftest)[3]à ¯Ã†â€™Ã… ¸frequency names(dftest)[4]à ¯Ã†â€™Ã… ¸cc names(dftest)[5]à ¯Ã†â€™Ã… ¸time names(dftest)[6]à ¯Ã†â€™Ã… ¸donated # drop time column from both files dftrain$time à ¯Ã†â€™Ã… ¸ NULL dftest$time à ¯Ã†â€™Ã… ¸ NULL #   sort (train) dataframe on Recency in ascending order sorted_dftrain à ¯Ã†â€™Ã… ¸ dftrain[ order( dftrain[,2] ), ] #   add column in (train) dataframe -   hold score (rank) of Recency for each customer sorted_dftrain[ , Rrank] à ¯Ã†â€™Ã… ¸ 0 #   convert train file from dataframe format to matrix matrix_train à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftrain, as.numeric)) #   sort (test) dataframe on Recency in ascending order sorted_dftest à ¯Ã†â€™Ã… ¸ dftest[ order( dftest[,2] ), ] #   add column in (test) dataframe -hold score (rank) of Recency for each customer sorted_dftest[ , Rrank] à ¯Ã†â€™Ã… ¸ 0 #   convert train file from dataframe format to matrix matrix_test à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftest, as.numeric)) # categorize matrix_train and add scores for Recency apply business rule for(i in 1:nrow(matrix_train)) { if (matrix_train [i,2]   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_train [i,6] à ¯Ã†â€™Ã… ¸ 3 } else if ((matrix_train [i,2] = 15)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_train [i,6] à ¯Ã†â€™Ã… ¸ 2 } else {   matrix_train [i,6] à ¯Ã†â€™Ã… ¸ 1   }   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   } # categorize matrix_test and add scores for Recency apply business rule for(i in 1:nrow(matrix_test)) { if (matrix_test [i,2]   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_test [i,6] à ¯Ã†â€™Ã… ¸ 3 } else if ((matrix_test [i,2] = 15)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_test [i,6] à ¯Ã†â€™Ã… ¸ 2 } else {   matrix_test [i,6] à ¯Ã†â€™Ã… ¸ 1 }   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   } # convert matrix_train back to dataframe sorted_dftrain à ¯Ã†â€™Ã… ¸ data.frame(matrix_train) # sort dataframe 1rst by Recency Rank (desc.) then by Frequency (desc.) sorted_dftrain_2à ¯Ã†â€™Ã… ¸ sorted_dftrain[order(-sorted_dftrain[,6], -sorted_dftrain[,3] ), ] # add column in train dataframe- hold Frequency score (rank) for each customer sorted_dftrain_2[ , Frank] à ¯Ã†â€™Ã… ¸ 0 # convert dataframe to matrix matrix_train à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftrain_2, as.numeric)) # convert matrix_test back to dataframe sorted_dftest à ¯Ã†â€™Ã… ¸ data.frame(matrix_test) # sort dataframe 1rst by Recency Rank (desc.) then by Frequency (desc.) sorted_dftest2 à ¯Ã†â€™Ã… ¸ sorted_dftest[ order( -sorted_dftest[,6], -sorted_dftest[,3] ), ] # add column in test dataframe- hold Frequency score (rank) for each customer sorted_dftest2[ , Frank] à ¯Ã†â€™Ã… ¸ 0 # convert dataframe to matrix matrix_test à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftest2, as.numeric)) #categorize matrix_train, add scores for Frequency for(i in 1:nrow(matrix_train)){    if (matrix_train[i,3] >= 25) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_train[i,7] à ¯Ã†â€™Ã… ¸ 3    } else if ((matrix_train[i,3] > 15) (matrix_train[i,3]   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_train[i,7] à ¯Ã†â€™Ã… ¸ 2    } else {   matrix_train[i,7] à ¯Ã†â€™Ã… ¸ 1   }   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   } #categorize matrix_test, add scores for Frequency for(i in 1:nrow(matrix_test)){    if (matrix_test[i,3] >= 25) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_test[i,7] à ¯Ã†â€™Ã… ¸ 3    } else if ((matrix_test[i,3] > 15) (matrix_test[i,3]   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_test[i,7] à ¯Ã†â€™Ã… ¸ 2    } else {  Ãƒâ€šÃ‚   matrix_test[i,7] à ¯Ã†â€™Ã… ¸ 1   } } #   convert matrix test back to dataframe sorted_dftrain à ¯Ã†â€™Ã… ¸ data.frame(matrix_train) # sort (train) dataframe 1rst on Recency rank (desc.) 2nd Frequency rank (desc.) sorted_dftrain_2 à ¯Ã†â€™Ã… ¸ sorted_dftrain[ order( -sorted_dftrain[,6], -sorted_dftrain[,7] ), ] # add another column for the Sum of Recency rank and Frequency rank sorted_dftrain_2[ , SumRankRAndF] à ¯Ã†â€™Ã… ¸ 0 # convert dataframe to matrix matrix_train à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftrain_2, as.numeric)) #   convert matrix test back to dataframe sorted_dftest à ¯Ã†â€™Ã… ¸ data.frame(matrix_test) # sort (train) dataframe 1rst on Recency rank (desc.) 2nd Frequency rank (desc.) sorted_dftest2 à ¯Ã†â€™Ã… ¸ sorted_dftest[ order( -sorted_dftest[,6],   -sorted_dftest[,7] ), ] # add another column for the Sum of Recency rank and Frequency rank sorted_dftest2[ , SumRankRAndF] à ¯Ã†â€™Ã… ¸ 0 # convert dataframe to matrix matrix_test à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftest2, as.numeric)) # sum Recency rank and Frequency rank for train file for(i in 1:nrow(matrix_train)) { matrix_train[i,8] à ¯Ã†â€™Ã… ¸ matrix_train[i,6] + matrix_train[i,7] } # sum Recency rank and Frequency rank for test file for(i in 1:nrow(matrix_test)) { matrix_test[i,8] à ¯Ã†â€™Ã… ¸ matrix_test[i,6] + matrix_test[i,7] } # convert matrix_train back to dataframe sorted_dftrain à ¯Ã†â€™Ã… ¸ data.frame(matrix_train) # sort train dataframe according to total rank in descending order sorted_dftrain_2 à ¯Ã†â€™Ã… ¸ sorted_dftrain[ order( -sorted_dftrain[,8] ), ] # convert sorted train dataframe matrix_train à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftrain_2, as.numeric)) # convert matrix_test back to dataframe sorted_dftest à ¯Ã†â€™Ã… ¸ data.frame(matrix_test) # sort test dataframe according to total rank in descending order sorted_dftest2 à ¯Ã†â€™Ã… ¸ sorted_dftest[ order( -sorted_dftest[,8] ), ] # convert sorted test dataframe to matrix matrix_test à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftest2, as.numeric)) # apply business rule check count customers whose score >= 4 and that Have Donated, train file # check count for all customers that have donated in the train dataset count_train_predicted_donations à ¯Ã†â€™Ã… ¸ 0 counter_train à ¯Ã†â€™Ã… ¸ 0 number_donation_instances_whole_train à ¯Ã†â€™Ã… ¸ 0 false_positives_train_counter à ¯Ã†â€™Ã… ¸ 0 for(i in 1:nrow(matrix_train)) {    if ((matrix_train[i,8] >= 4) (matrix_train[i,5] == 1)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   count_train_predicted_donations = count_train_predicted_donations + 1   } if ((matrix_train[i,8] >= 4) (matrix_train[i,5] == 0)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   false_positives_train_counter = false_positives_train_counter + 1}    if (matrix_train[i,8] >= 4) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   counter_train à ¯Ã†â€™Ã… ¸ counter_train + 1   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   }    if (matrix_train[i,5] == 1) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   number_donation_instances_whole_train à ¯Ã†â€™Ã… ¸ number_donation_instances_whole_train + 1    } } # apply business rule check count customers whose score >= 4 and that Have Donated, test file # check count for all customers that have donated in the test dataset count_test_predicted_donations à ¯Ã†â€™Ã… ¸ 0 counter_test à ¯Ã†â€™Ã… ¸ 0 number_donation_instances_whole_test à ¯Ã†â€™Ã… ¸ 0 false_positives_test_counter à ¯Ã†â€™Ã… ¸ 0 for(i in 1:nrow(matrix_test)) {    if ((matrix_test[i,8] >= 4) (matrix_test[i,5] == 1)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   count_test_predicted_donations = count_test_predicted_donations + 1   } if ((matrix_test[i,8] >= 4) (matrix_test[i,5] == 0)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   false_positives_test_counter = false_positives_test_counter + 1}    if (matrix_test[i,8] >= 4) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   counter_test à ¯Ã†â€™Ã… ¸ counter_test + 1   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   }    if (matrix_test[i,5] == 1) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   number_donation_instances_whole_test à ¯Ã†â€™Ã… ¸ number_donation_instances_whole_test + 1   Ãƒâ€šÃ‚   } } # convert matrix_train to dataframe dftrain à ¯Ã†â€™Ã… ¸ data.frame(matrix_train) # remove the group of customers who are less likely to donate again in the future from train file dftrain_final à ¯Ã†â€™Ã… ¸ dftrain[c(1:counter_train),1:8] # convert matrix_train to dataframe dftest à ¯Ã†â€™Ã… ¸ data.frame(matrix_test) # remove the group of customers who are less likely to donate again in the future from test file dftest_final à ¯Ã†â€™Ã… ¸ dftest[c(1:counter_test),1:8] # save final train dataframe as a CSV in the specified directory reduced target future customers write.csv(dftrain_final, file = C:\Users\Alexandros\Dropbox\MSc\2nd Semester\Data analysis\Assignment\train_output.csv, row.names = FALSE) #save final test dataframe as a CSV in the specified directory reduced target future customers write.csv(dftest_final, file = C:\Users\Alexandros\Dropbox\MSc\2nd Semester\Data analysis\Assignment\test_output.csv, row.names = FALSE) #train precision=number of relevant instances retrieved / number of retrieved instances collect.530 precision_train à ¯Ã†â€™Ã… ¸Ãƒâ€šÃ‚   count_train_predicted_donations / counter_train # train recall = number of relevant instances retrieved / number of relevant instances in collect.530 recall_train à ¯Ã†â€™Ã… ¸ count_train_predicted_donations / number_donation_instances_whole_train # measure combines PrecisionRecall is harmonic mean of PrecisionRecall balanced F-score for # train file f_balanced_score_train à ¯Ã†â€™Ã… ¸ 2*(precision_train*recall_train)/(precision_train+recall_train) # test precision precision_test à ¯Ã†â€™Ã… ¸ count_test_predicted_donations / counter_test # test recall recall_test à ¯Ã†â€™Ã… ¸ count_test_predicted_donations / number_donation_instances_whole_test # the balanced F-score for test file f_balanced_score_test à ¯Ã†â€™Ã… ¸ 2*(precision_test*recall_test)/(precision_test+recall_test) # error in precision error_precision à ¯Ã†â€™Ã… ¸ abs(precision_train-precision_test) # error in recall error_recall à ¯Ã†â€™Ã… ¸ abs(recall_train-recall_test) # error in f-balanced scores error_f_balanced_scores à ¯Ã†â€™Ã… ¸ abs(f_balanced_score_train-f_balanced_score_test) # Print Statistics for verification and validation cat(Precision with training dataset: , precision_train) cat(Recall with training dataset: , recall_train) cat(Precision with testing dataset: , precision_test) cat(Recall with testing dataset: , recall_test) cat(The F-balanced scores with training dataset: , f_balanced_score_train) cat(The F-balanced scores with testing dataset:   , f_balanced_score_test) cat(Error in precision: , error_precision) cat(Error in recall: , error_recall) cat(Error in F-balanced scores: , error_f_balanced_scores) # confusion matrix (true positives, false positives, false negatives, true negatives) # calculate true positives for train which is the variable count_train_predicted_donations # calculate false positives for train which is the variable false_positives_train_counter # calculate false negatives for train false_negatives_for_train à ¯Ã†â€™Ã… ¸ number_donation_instances_whole_train count_train_predicted_donations # calculate true negatives for train true_negatives_for_train à ¯Ã†â€™Ã… ¸ (nrow(matrix_train) number_donation_instances_whole_train) false_positives_train_counter collect_trainà ¯Ã†â€™Ã… ¸c(false_positives_train_counter, true_negatives_for_train, count_train_predicted_donations, false_negatives_for_train) # calculate true positives for test which is the variable count_test_predicted_donations # calculate false positives for test which is the variable false_positives_test_counter # calculate false negatives for test false_negatives_for_test à ¯Ã†â€™Ã… ¸ number_donation_instances_whole_test count_test_predicted_donations # calculate true negatives for test true_negatives_for_testà ¯Ã†â€™Ã… ¸(nrow(matrix_test)-number_donation_instances_whole_test)- false_positives_test_counter collect_test à ¯Ã†â€™Ã… ¸ c(false_positives_test_counter, true_negatives_for_test, count_test_predicted_donations, false_negatives_for_test) TrueCondition à ¯Ã†â€™Ã… ¸ factor(c(0, 0, 1, 1)) PredictedCondition à ¯Ã†â€™Ã… ¸ factor(c(1, 0, 1, 0)) # print confusion matrix for train df_conf_mat_train à ¯Ã†â€™Ã… ¸ data.frame(TrueCondition,PredictedCondition,collect_train) ggplot(data = df_conf_mat_train, mapping = aes(x = PredictedCondition, y = TrueCondition)) +    geom_tile(aes(fill = collect_train), colour = white) +    geom_text(aes(label = sprintf(%1.0f, collect_train)), vjust = 1) +    scale_fill_gradient(low = blue, high = red) +    theme_bw() + theme(legend.position = none) #   print confusion matrix for test df_conf_mat_test à ¯Ã†â€™Ã… ¸ data.frame(TrueCondition,PredictedCondition,collect_test) ggplot(data =   df_conf_mat_test, mapping = aes(x = PredictedCondition, y = TrueCondition)) +    geom_tile(aes(fill = collect_test), colour = white) +    geom_text(aes(label = sprintf(%1.0f, collect_test)), vjust = 1) +    scale_fill_gradient(low = blue, high = red) +    theme_bw() + theme(legend.position = none) # MCC = (TP * TN FP * FN)/sqrt((TP+FP) (TP+FN) (FP+TN) (TN+FN)) for train values mcc_train à ¯Ã†â€™Ã… ¸ ((count_train_predicted_donations * true_negatives_for_train) (false_positives_train_counter * false_negatives_for_train))/sqrt((count_train_predicted_donations+false_positives_train_counter)*(count_train_predicted_donations+false_negatives_for_train)*(false_positives_train_counter+true_negatives_for_train)*(true_negatives_for_train+false_negatives_for_train)) # print MCC for train cat(Matthews Correlation Coefficient for train: ,mcc_train) # MCC = (TP * TN FP * FN)/sqrt((TP+FP) (TP+FN) (FP+TN) (TN+FN)) for test values mcc_test à ¯Ã†â€™Ã… ¸ ((count_test_predicted_donations * true_negatives_for_test) (false_positives_test_counter * false_negatives_for_test))/sqrt((count_test_predicted_donations+false_positives_test_counter)*(count_test_predicted_donations+false_negatives_for_test)*(false_positives_test_counter+true_negatives_for_test)*(true_negatives_for_test+false_negatives_for_test)) # print MCC for test cat(Matthews Correlation Coefficient for test: ,mcc_test) # print MCC err between train and err cat(Matthews Correlation Coefficient error: ,abs(mcc_train-mcc_test)) # Total = TP + TN + FP + FN for train total_train à ¯Ã†â€™Ã… ¸ count_train_predicted_donations + true_negatives_for_train + false_positives_train_counter + false_negatives_for_train # Total = TP + TN + FP + FN for test   total_test à ¯Ã†â€™Ã… ¸ count_test_predicted_donations + true_negatives_for_test + false_positives_test_counter + false_negatives_for_test # totalAccuracy = (TP + TN) / Total for train values totalAccuracyTrain à ¯Ã†â€™Ã… ¸ (count_train_predicted_donations + true_negatives_for_train)/ total_train # totalAccuracy = (TP + TN) / Total for test values totalAccuracyTest à ¯Ã†â€™Ã… ¸ (count_test_predicted_donations + true_negatives_for_test)/ total_test # randomAccuracy = ((TN+FP)*(TN+FN)+(FN+TP)*(FP+TP)) / (Total*Total)   for train values randomAccuracyTrainà ¯Ã†â€™Ã… ¸((true_negatives_for_train+false_positives_train_counter)*(true_negatives_for_train+false_negatives_for_train)+(false_negatives_for_train+count_train_predicted_donations)*(false_positives_train_counter+count_train_predicted_donations))/(total_train*total_train) # randomAccuracy = ((TN+FP)*(TN+FN)+(FN+TP)*(FP+TP)) / (Total*Total)   for test values randomAccuracyTestà ¯Ã†â€™Ã… ¸((true_negatives_for_test+false_positives_test_counter)*(true_negatives_for_test+false_negatives_for_test)+(false_negatives_for_test+count_test_predicted_donations)*(false_positives_test_counter+count_test_predicted_donations))/(total_test*total_test) # kappa = (totalAccuracy randomAccuracy) / (1 randomAccuracy) for train kappa_train à ¯Ã†â€™Ã… ¸ (totalAccuracyTrain-randomAccuracyTrain)/(1-randomAccuracyTrain) # kappa = (totalAccuracy randomAccuracy) / (1 randomAccuracy) for test kappa_test à ¯Ã†â€™Ã… ¸ (totalAccuracyTest-randomAccuracyTest)/(1-randomAccuracyTest) # print kappa error cat(Kappa error: ,abs(kappa_train-kappa_test))

Use of Literary Techniques to Characterize Rachel in Cisneros’ Eleven E

Use of Literary Techniques to Characterize Rachel in Cisneros’ Eleven In â€Å"Eleven†, written by Sandra Cisneros, Cisneros uses literary techniques such as diction and imagery to characterize Rachel’s character during her transition from age ten to age 11. These literary techniques help to describe how Rachel feels in certain situations while also explaining her qualities and traits. Through the use of these literary techniques Cisneros also collaborated on Rachel’s feelings when she was other ages and how she felt at that time during her life. The diction that Cisneros uses is descriptive. Her words help explain Rachel’s feelings more in depth. In the opening line of â€Å"Eleven† it states, â€Å"what they don’t understand about birthdays and what they never tell you is when you’re eleven, you’re also ten, and nine, and eight, and seven, and six, and five, and four, and three, and two and one.† From this quote Cisneros paints a picture of how Rachel feels about turning eleven. She shows an idea of how Rachel will be acting throughout most of the story. Not only does Cisneros use that lin...

5.7 Liter Supersport :: English Literature Essays

5.7 Liter Supersport Finally! Almost there. I’ve been in this pick-up truck driving to work for the past 20 minutes, yet it seems like hours and hours have passed. It’s really hot outside and this old truck doesn’t even have air conditioning. Anyways, the reason why I’m riding in this broke down pick-up truck is because my dad is giving me a ride to work, his liquor store. Now I already know what everyone is probably thinking, â€Å"18 year-old guy without a car, still having daddy take you everywhere.† Well, not anymore because I’m meeting this â€Å"gentlemen† for a test-drive on his really nice car. â€Å"O.K. dad, you don’t have to slow down before even getting in the parking lot. I know you’re just trying to delay the task at hand, huh† â€Å"Shut up Sunny, your ass could wait a few more seconds can’t it †¦and one more thing, don’t act so desperate in front of the guy, O.K.? Make him want to sell you the car, not you desperately wanting it.† This right here is very typical of my dad to try giving me advice. He does it every chance he gets. So just like every other situation, I give him my usual reply. †Aright pops; quit trippin’; I got this, aight?† My dad just laughs pulling into a parking spot right in front of our liquor store. It’s in a pretty rough area. The area has been known for its weekly shootings by the local gang-members. To the side of the store I notice the car out of the corner of my eye. It looks just like the picture I saw on the internet. I get out of the truck faster then Marion Jones sprinting so I can have a better look at the fine piece of machinery. My dad goes into the store to look for the guy. From just looking at the car, my heart is melting. I could stare at this car for days. A 2001 Chevy SuperSport Camaro, a Corvette powered sports-car that was going to be mine. I’ve been researching this car for the last three years, and now I finally have earned the opportunity to buy this car. Now what was it my dad was telling me? Oh that’s right, to not let him see me drooling over the car. I know that Jim told me he wants to sell his car for twenty-eight thousand five hundred dollars, but it seems a little steep.

Accounting for Employee Benefits (Pas 19)

REACTION PAPER The Seminar about â€Å"Accounting for Employee Benefits (PAS 19)† was held at Multi-Purpose Hall of Engineering Building last July 20, 2012. The speaker for this event was Mr. Ysmael Acosta. At first I was really curious about that seminar of what topic does the speaker would discuss. When the seminar goes on, there are some topic that are really interesting and some are not quite clear for me.Sometimes it makes me feel bored when listening to him because he really speak very fast, that’s why I can’t understand him very well. For now, I would like to discuss those topics that makes me interested. Employee Benefits outlines the accounting requirements for employee benefits, including short-term benefits (like wages and salaries, annual leave), post-employment benefits such as retirement benefits, other long-term benefits (like long service leave) and termination benefits.The standard establishes the principle that the cost of providing employee ben efits should be recognised in the period in which the benefit is earned by the employee, rather than when it is paid or payable, and outlines how each category of employee benefits are measured, providing detailed guidance in particular about post-employment benefits. The basic principle of PAS 19 is the cost of providing employee benefits should be recognised in the period in which the benefit is earned by the employee, rather than when it is paid or payable.The objective of â€Å"PAS 19† as I remembered correctly, is to prescribe the accounting and disclosure for employee benefits (that is, all forms of consideration given by an entity in exchange for service rendered by employees). The principle underlying all of the detailed requirements of the Standard is that the cost of providing employee benefits should be recognised in the period in which the benefit is earned by the employee, rather than when it is paid or payable. The scope of PAS 19 applies to wages and salaries; compensated absences (paid vacation and sick leave); profit sharing plans; bonuses; medical and ife insurance benefits during employment; housing benefits; free or subsidised goods or services given to employees; pension benefits; post-employment medical and life insurance benefits; long-service or sabbatical leave; ‘jubilee' benefits; deferred compensation programmes; and termination benefits. As time goes by, there are changes and ammendments on the PAS 19. From the simple Exposure Draft E16 Accounting for Retirement Benefits in Financial Statements of Employers (April 1980) to The Limit on a Defined Benefit Asset, Minimum Funding Requirements and their Interaction last June 16, 2011.This issue tells that the accounting for employee benefits, particularly pensions and other post-retirement benefits, has long been a complex and difficult area and initial plans for a full review of pension accounting had to be deferred in light of competing priorities, ultimately leaving the I ASB to proceed alone on improving specific aspects of the existing requirements of IAS 19. Prior to the amendment, IAS 19 permitted choices on how to account for actuarial gains and losses on pensions and similar items, including the so-called ‘corridor approach' which resulted the deferral of gains and losses.The Exposure Draft proposed eliminating the use of the ‘corridor' approach and instead mandating all remeasurement impacts be recognised in OCI (with the remainder in profit or loss) – and in fact had proposed extending these requirements to all long-term employee benefits (e. g. certain long service leave schemes). The final amendments make the OCI presentation changes in respect of pensions (and similar items) only, but all other long term benefits are required to be measured in the same way even though changes in the recognised amount are fully reflected in profit or loss.Also changed in PAS 19 is the treatment for termination benefits, specifically the p oint in time when an entity would recognise a liability for termination benefits. The final amendments do not adopt the equivalent US-GAAP requirements verbatim (which requires individual employees to be notified), but the recognition timeframe may be extended in some cases. Finally, various other amendments to IAS 19 may have impacts in particular areas.For instance, employee benefits not settled wholly before twelve months after the end of the annual reporting period would be captured as an ‘other long term benefit' rather than a ‘short term benefit', and whilst presented as a current item in the statement of financial position, would be measured differently under the amendments. PAS 19 requires an entity to determine the rate used to discount employee benefits with reference to market yields on high quality corporate bonds. However, when there is no deep market in corporate bonds, an entity is required to use market yields on government bonds instead.The global financ ial crisis has led to a widening of the spread between yields on corporate bonds and yields on government bonds. As a result, entities with similar employee benefit obligations may report them at very different amounts. If adopted, the amendments would ensure that the comparability of financial statements is maintained across jurisdictions, regardless of whether there is a deep market for high quality corporate bonds. Time flew very fast. This seminar taught us so many things that we can use in the near future. Though there is not enough time to discuss everything, we still accomplished a lot.

Firsthand experience Essay

Happiness comes in all shapes and sizes. What makes you happy may not make someone else happy. The idea of happiness may not be the same for any two people, or maybe not for anyone you come across with. Happiness is an emotion causes by thousands of things. It is an abstract idea that cannot be fully described. What makes you happy changes as you get older, you do not like the same things your whole life so it is normal that your interests start to change. Like mentioned before not everyone has the same interests and cannot feel happiness from the same things. From firsthand experience I can say that I am the perfect example of finding different happiness. They have changed from my years in elementary school, middle school, and high school. Let’s start with elementary school. When I was in elementary school what brought me happiness was having time to spend at my friend’s house after school. Because I lived far away from the school that I attended I didn’t really know anyone in my neighborhood. There were days though that my parents would let me go to a friend’s house and stay there for a few hours while they got out of work. I didn’t have to be at my grandma’s house bored so that made me really happy, and I had a lot of fun while I was there so that made it even better. Another thing that made me feel happiness was being able to go two days out of the week to band practice. I liked being able to play my instrument and making beautiful sound come out of something so little. Playing with the band was my thing I could not stop smiling and feeling all this joy inside as I played. When the director put his arms up and signaled us to start playing was so thrilling knowing that we were about to make beautiful music all together. I think back and see how simple things made me really happy as a child. Now let’s talk about middle school. While I was in middle school I was still in band and yes it still brought me happiness. The main thing that brought me happiness while I was in middle school was being a little more free from home and my parents. If I remember correctly it was seventh grade that I went to my first dance, you can imagine my excitement. When I was at the dance my friends and I were having a great time, we were laughing and dancing the whole time. Because it was my first dance it was a very fun time, and it brought so much happiness to be able to share that experience with my friends. When I was in high school all of my interests changed and even now I have the same interests. I found my passion for singing so I auditioned to join the school choir and I made it. Being in choir class was the best thing that I did when I was in high school. The happiness that I felt when I walked into that class every day I have no words to describe it. To top it all off when I started to sing and hear the chords we would all make together gave me the chills. There was not a single day that passed where hearing myself and others around me sing was not magical. When I would have a bad day I waited for third period to talk into class grab my folder from my cubic and start to sing and just forget about everything. Music was my escape from being upset, it was the way for me to forget about what was going on in my life. The happiness I felt was so great, even now I turn to music for help and comfort. In conclusion, happiness is an abstract emotion that cannot be easily defined. Not everyone feels happiness in the exact same way or from the same things. But no matter where our happiness comes from we all feel it. We feel it as kids, as teenagers, and as adults. Even though what causes us happiness may change the characteristics of it do not. The smile, the giggle, and the butterflies in your stomach do not disappear. From firsthand experience I know that we all go through phases, but not matter how old we get, we are always going to be happy.

Currency War Between China and Usa Essay

Currency War: Currency war, also known as competitive devaluation, is a condition in international affairs where countries compete against each other to achieve a relatively low exchange rate for their own currency. As the price to buy a particular currency falls so too does the real price of exports from the country. Imports become more expensive too, so domestic industry, and thus employment, receives a boost in demand both at home and abroad. However, the price increase in imports can harm citizens’ purchasing power. The policy can also trigger retaliatory action by other countries which in turn can lead to a general decline in international trade, harming all countries. Reasons of Currency War Between USA and China: Competitive devaluation has been rare through most of history as countries have generally preferred to maintain a high value for their currency,but it happens when devaluation occur. China keeps its dollar artificially low so that countries like the US will buy its goods. China is the US’s largest trading partner and if they didn’t sell their goods for super cheap, markets like India would be able to under cut the Chinese and then the US would buy goods from Indian instead of China. There is so much trade between China and the US that China profits immensely without needing it’s Yuan to appreciate. This of course hurts the average Chinese person in that their labour is devalued but it beneficial for the country as a whole as it has quickly become a super power economicaly. In 2008, a trader paid one Ghana Cedi for one U.S. dollar, but at the beginning of April 2012, the same trader travelling to Dubai paid GH ¢1.74 for one U.S. dollar. This means that year-on-year decline in the value of cedi against the US dollar was 74 per cent over a three-year period. A point to note is that during the global economic crises of 2008-2009, the cedi depreciated by 25 per cent against the dollar. Between 2010 and 2011, the cedi again depreciated 18.5 per cent against the US dollar. For last month, the cedi exchange rate depreciated 4.29 percent against the US dollar. So is the current downward slide in the cedi value as a result of the slowdown in the global economy or due to internal structural weaknesses? This question requires a detailed research work beyond the scope of this article but it is a very relevant question to ask at this time. In economics, depreciation is basically the symptoms of an underlying problem, specifically imbalances in the Balance of Payment (BOP), emanating from excess demand for dollars. So instead of discussing the depreciating cedi, I will rather focus my attention on the causes or factors that cause currency to depreciate and what the government can do to arrest this problem in special cases. Before then, I must let readers know the difference between currency fluctuation and depreciation. Fluctuations in currency value are a common event and are usually no cause for concern. The minor daily increases and decreases in value are generally due to â€Å"random walk† and not due to an economic event or fundamental problems. However, changes in currency value become significant when the decline in value of the currency is an ongoing trend. Technically, when currency depreciates, it loses value and purchasing power, with impact on the real sectors of the economy. Although, the economic effects of a lower cedi take time to happen, there are time lags between a change in the exchange rate and changes in commodity prices. Factors that determine the value of a currency include the current state of the overall economy, inflation, trade balance (the difference between the value of export and import), level of political stability, etc. Occasionally, external factors like currency speculations on the foreign exchange market can also contribute to depreciation of the local currency. Such being the case, a government can intervene into the foreign exchange market to support its national currency and suppress the process of depreciation. Currency depreciation can positively impact the overall economic development, though. It boosts competitiveness through lower export costs and secures more income from exported goods in a similar way devaluation does. On the contrary, depreciation makes imports more expensive and discourages purchases of imported goods stimulating demand for domestically manufactured goods. Globally, governments intentionally influence the value of their currency utilising the powerful tool of the base interest rates, which are usually set by the country’s central bank and this tool is often used to intentionally depreciate the currency rates to encourage exports. Factors that can cause a currency to depreciate are: Supply and Demand †¢ Just as with goods and services, the principles of supply and demand apply to the appreciation and depreciation of currency values. If a country injects new currency into its economy, it increases the money supply. When there is more money circulating in an economy, there is less demand. This depreciates the value of the currency. On the other hand, when there is a high domestic or foreign demand for a country’s currency, the currency appreciates in value. Inflation †¢ Inflation occurs when the general prices of goods and services in a country increase. Inflation causes the value of the cedi to depreciate, reducing purchasing power. If there is rampant inflation, then a currency will depreciate in value. What causes inflation? †¢ Printing Money. Note printing money does not always cause inflation. It will occur when the money supply is increased faster than the growth of real output. †¢ Note: the link between printing money and causing inflation is not straightforward. The money supply does not just depend on the amount the government prints. †¢ Large National Debt. To finance large national debts, governments often print money and this can cause inflation. Economic Outlook If a country’s economy is in a slow growth or recessionary phase, the value of their currency depreciates. The value of a country’s currency also depreciates if its major economic indicators like retail sales and Gross Domestic Product, or GDP, are declining. A high and/or rising unemployment rate can also depreciate currency value because it indicates an economic slowdown. If a country’s economy is in a strong growth period, the value of their currency appreciates. Trade Deficit A trade deficit occurs when the value of goods a country imports is more than the value of goods it exports. When the trade deficit of a country increases, the value of the domestic currency depreciates against the value of the currency of its trading partners. The demand for imports should fall as imports become more expensive. However, some imports are essential for production or cannot be made in the country and have an inelastic demand—we end up spending more on these when the exchange rate falls in value. This can cause the balance of payments to worsen in the short run (a process known as the J curve effect) Collapse of Confidence If there is a collapse of confidence in an economy or financial sector, this will lead to an outflow of currency as people do not want to risk losing their currency. Therefore, this causes an outflow of capital and depreciation in the exchange rate. Collapse in confidence can be due to political or economic factors. Price of Commodities if an economy depends on exports of raw materials, a fall in the price of this raw material can cause a fall in export revenue and depreciation in the exchange rate. For example, in 2011, a ton of cocoa sold for US4,000 per ton. Currently, it is going for US$2,300 per ton, translating into fewer inflows of dollars. Interest rate Differential I will use the International Fischer Effect to explain the relationship between the expected change in the current exchange rate between the cedi and the dollar, which is approximately equivalent to the difference between Ghana and US’ nominal interest rates for that time. For example, if the average interest rate in Ghana for 2011 was 24 per cent and for US was three per cent, then the dollar should appreciate roughly 21 per cent or the cedi must depreciate 21 per cent compared to the dollar to restore parity. The rationale for the IFE is that a country with a higher interest rate will also tend to have a higher inflation rate. This increased amount of inflation should cause the currency in the country with the high interest rate to depreciate against a country with lower interest rates. Market Speculations Market speculations can contribute to a process of spiraling depreciation after smaller market players decide to follow the example of the leading dealers, the so-called market makers, and after they lost confidence in a particular currency start to sell it in bulk amounts. Then only a quick reaction of the country’s central bank can restore the confidence of investors and stop the currency rates of the nation’s currency from continuous decline. When the currency depreciation is based on market speculations, in other words, not backed by fundamental economic factors, then the central bank comes to the rescue- intervene. A sterilised intervention against depreciation can only be effective in the medium term if the underlying cause behind the currency’s loss of value can be addressed. If the cause was a speculative attack based on political uncertainty this can potentially be resolved. Because after a sterilised intervention the money supply remains unchanged at its high level, the locally available interest rates can still be relatively low, so the carry trade continues and if it still wants to prevent depreciation the central bank has to intervene again. This can only go on so long before the bank runs out of foreign currency reserves. In conclusion, currency depreciation is the result of fundamental deficiencies with the domestic economy which must be corrected over a period of time to restore balance. However, where the depreciation is out of speculative attacks on the currency, then the central bank can intervene to correct the temporary anomalies, which, often is short term in nature. Lastly, intervention in the foreign exchange market by the central bank to correct fundamental weaknesses, just like the Ghanaian situation will not work, because, very soon, the central bank will run out of international reserves; hence, the cedi must therefore seek its equilibrium level. The writer is an economic consultant and former Assistant Professor of Finance and Economics at Alabama State University. Montgomery, Alabama. Currency War in the Great Depression During the Great Depression of the 1930s, most countries abandoned the gold standard, resulting in currencies that no longer had intrinsic value. With widespread high unemployment, devaluations became common. Effectively, nations were competing to export unemployment, a policy that has frequently been described as â€Å"beggar thy neighbour†.[30] However, because the effects of a devaluation would soon be counteracted by a corresponding devaluation by trading partners, few nations would gain an enduring advantage. On the other hand, the fluctuations in exchange rates were often harmful for international traders, and global trade declined sharply as a result, hurting all economies. The exact starting date of the 1930s currency war is open to debate.[23] The three principal parties were Great Britain, France, and the United States. For most of the 1920s the three generally had coinciding interests, both the US and France supported Britain’s efforts to raise Sterling’s value against market forces. Collaboration was aided by strong personal friendships among the nations’ central bankers, especially between Britain’s Montagu Norman and America’s Benjamin Strong until the latter’s early death in 1928. Soon after the Wall Street Crash of 1929, France lost faith in Sterling as a source of value and begun selling it heavily on the markets. From Britain’s perspective both France and the US were no longer playing by the rules of the gold standard. Instead of allowing gold inflows to increase their money supplies (which would have expanded those economies but reduced their trade surpluses) France and the US began sterilising the inflows, building up hoards of gold. These factors contributed to the Sterling crises of 1931; in September of that year Great Britain substantially devalued and took the pound off the gold standard. For several years after this global trade was disrupted by competitive devaluation. The currency war of the 1930s is generally considered to have ended with the Tripartite monetary agreement of 1936.