Asserting Ethnic Identity and Power Through Language Essay

Week-1 The linguistic ideology at work here is founded both on the concept of the ‘mother tongue’ as well as on the ‘one nation, one language’ principle. Communities on the western side of the border are not interested in learning the language of their eastern neighbors. Eastern communities, on the other hand, are strongly motivated to learn western languages. The importance attributed to English as the ‘language of globalization’ is common to both sides. We can actually say that ‘language’ is a very hot and recurrent issue for some communities: namely the German-speaking community in Bernstein (D), the Czech-speaking community in Vejprty (CS), the German-speaking and Slovenian-speaking communities in Eisenkappel/Z? elezna Kapla (A),etc. The term ‘mother tongue’ is often used – forms the ‘way of thinking’ of its speakers, and thus the different ‘mentalities’ and ‘national characters’ are connected with the use of different languages. Many informants are convinced that it is the ‘mother tongue’ which determines thought, social behavior, and exhibition or control of affection and emotions. Thus the confrontation between languages automatically becomes a clash of mentalities. For example, the German-speaking community in Ba? renstein finds there is a relation between the insurmountable difficulty in pronouncing and learning the Czech language and the incomprehensibility of the words Czech-speaking people produce. People in western communities explain this widespread knowledge by saying that ‘the Others’ need to know my language, because my language is the superior One. We can also find indices of implicit prestige in many interview quotes, like ‘my language is useful to find a job’, ‘my language is more international than theirs’, ‘it represents a symbol of upward social mobility’; ‘the importance of my language forces them to learn it, and in this way they show practical sense, intelligence and cleverness, because they well know that the knowledge of the languages spoken on both sides of the border offers more professional and economic opportunities’. People are not generally interested in learning the language of the ‘Other’, and the reason is, as we have already seen, its ‘uselessness’, or its low value on the ‘language market’. They only learn what they need in their commercial transactions. Europe is a multilingual continent in which the tension between linguistic pluralism and assimilation is quite evident at present. Week-2 The topic for this week was â€Å"Creation of a Sense of Belonging through Language†, which we found very much interesting as we have to present our own explanation, views and thoughts. The topic is about Finland, Iceland and Latvia. Firstly we discussed about Finland that Swedish-speaking people along the coastlines, spoke a non-Scandinavian language, namely Finnish. The Finnish language was to become the most effective medium in the nation-building process as well as the most important criterion in creating an awareness of a collective identity. Language became a defining characteristic towards the ‘outside’ and a communicative driving force on the ‘inside’ within the great diversity of local and regional cultures. We could say that for the process of nation-building in Finland during the 19th century two main deficits had to be overcome: sovereign state structures had to be developed and an individual Finnish national consciousness had to be formed. In the process of spreading a Finnish national consciousness – a development often associated with the term ‘awakening’ as in a religious experience – the main focus was directed towards the common people, their language and culture. Finland’s modest cultural life, Finnish had gained the status of a modern cultural and scientific language. The civil servants, scholars, and many artists, continued to use Swedish as their language of communication and publication. But Finnish steadily gained ground. Many people were already, or became, bilingual. The accusation that Finnish was ‘too primitive’ was defeated by generating new terms, which proved that the language was innovative and possessed the potential for development. In the discussion about Iceland by Halfdanarson. The text tells a story of Icelandic nationalism and the struggle for independence of Iceland under Danish rule. Halfdanarson points out the rare case of Icelandic nationalism, and it’s non-violent nature. In fact, according to the article, both Iceland’s struggle for independence and the Danish reactions to it were both surprisingly pacific in nature, partly because of the idea of shared past and cultural heritage between the two countries. There are certain elements in common with the case study of Iceland and Herder’s text, such as the idea of mystic, shared past of a nation, the role of the single language of a social group forming the nation and so on. I think especially in cases like Iceland, language and linguistic identity have essential role in formation of national identity. Iceland is isolated, both in geographical terms as an island in the middle of Atlantic ocean and in terms of language. Although Icelandic is a language related to scandinavian languages, it still differes from them quite a lot. And lastly There was a question that is it possible to have a single language in whole Europe? We think its not possible to have a single langage in the whole Europe as there are many different countries with their own languages from last hundreds of years. In Europe, People communicate with each other using the shared language of their group. The group might be as small as a couple (married or unmarried partners, twins, mother and daughter etc. who share a ‘private’ language where only they know the meaning of some words) or as large as a nation, where everyone understands the allusions in their shared language (often allusions to shared history, to contemporary events, to media people of fact or fiction etc). The ‘secret’ language of the smallest group and the ‘public’ language of the national group are two ‘varieties’ of the same language. Every social group, large or small, has its own language variety, (regional groups have varieties of the national language (as opposed to regional or minority languages) which are usually called ‘dialects’) and there is overlap among all the varieties. However there is a possibility to use English as a second language as use of English gives a considerable advantage to the 13 % of EU citizens who are native English speakers, and to speakers of closely related languages (German, Dutch, Danish and Swedish), over all other Europeans. Week-3 In the week 3, we learned about ‘Language and subjective identity’. The two articles were on Franz Kafka and Simone de Beauvoir. Franz Kafka was German though he never lived among the Germans. He was then living in Prague, Czech. Hence Kafka knew both Czech & German languages. But, he preferred Czech Language as he was of the view that one could express his/her feeling in a better way in a particular language. In this case, he thought that Czech was a better language than German to express his feelings. Franz Kafka was in love with Czech translator Milena Jesenka. He used to demand Milena to write him letters in Czech language than German. He belived in a approach â€Å"belongs to a language†. When Milena replied his letters in Czech, he believed that Czech was much more affectionate, which removes all the uncertainties, he could see his lover more clearly, the movements of her body, her hands quickly which almost resembled as they both are meeting. This shows how Kafka prefered Czech more than German. Kafka encouraged his favourite sister Ottla in her marriage to Josef David, a Czech Catholic, against the opposition of parents and relatives, and wrote affectionately to his new brother-in-law in fluent Czech. For Prague Jews of Kafka’s generation, language and identity could be painfully dissonant. In Kafka’s case, this dissonance reached deep into his own family, conferring an alien quality on the most intimate of human relationships. Franz Kafka died of tuberculosis in 1924. He is buried beside his parents in the family plot in Prague’s New Jewish Cemetery. Simone de Beauvoir is a French Women. She was French writer, political activist, feminist, and social theorist. She gave her whole life for feminine rights and equality with men in Society. Beauvoir was an outstanding student. She did her postgraduate work at the Ecole Normale Superieure, the top postgraduate program in France, where she met Jean Paul Sartre. When World War II broke out in September 1939, Sartre was called for military service. He became a prisoner of war when the French army surrendered, but he was released and both Beauvoir and Sartre participated in the resistance, and after the Vichy Regime dismissed Beauvoir from her teaching position, she began a novel about the resistance. When the war ended, Beauvoir and Sartre became part of a group of leading French intellectuals, who concerned themselves with the perceived failures of modern French society. they founded Les Temps modernes as a means to explain their social and cultural views. At the same time, Sartre suggested to Beauvoir that she undertake a book on the status of women, and she published, La deuxieme sexe (The Second Sex). This was her most famous, and influential book. It became a sourcebook of modern feminism, particularly in the United States for later feminist thinkers such as Betty Friedan and Gloria Steinem. De Beauvoir used very specific and effective and powerful words to underline her matter. She is willing to deploy language and words towards others, because she knows about â€Å"a manner in which her body and her relation to the world are modified through the action of others than herself†.

Culture Evaluation Essay

In doing this debate paper our learning team was asked to take the debate topic and apply it to another country. Our team is to examine how the arguments or presentation of the arguments would need to be changed. The team had its choice between three countries; India, China or Japan. Learning team C chose Japan. There is a major cultural shift when it comes to how Japanese citizens and American citizens value even the ownership of a gun. In American our right to own a gun, or multiple guns, is protected by the second amendment. In Japan however, gun ownership is not a right it is a privilege. Here in the United States we can walk into a gun store and with just a few simple questions and a phone call have our weapon in hand that day as we walk out of the store. Japan, on the other hand, must first take a day long class, and then take a written exam. The next step is to go to a shooting range, take a class and pass the range test. Then it is off to the doctor’s office for a drug screen and mental health check-up. Now the police keep this on file and start a rigorous background investigation to ensure you aren’t linked to known criminals or have a criminal history yourself. Now, if you successfully pass all these checks you are allowed to own a shotgun or an air rifle. Assault style weapons and handguns of all types are strictly forbidden by law. Only a few handguns even exist in Japan and those are for competition shooters only. Your home, under Japanese law, is subject to random searches by the police and the gun must be stored and locked away separately from your ammunition, which is also required to be under lock and key. (Fisher, 2012) So what does all this lead too? The lack of capability to even own a firearm in Japan has led to a drastically reduced gun crime rate. Compared to America in 2008 had over 12,000 gun related deaths, Japan on the other end of the spectrum had 11. (Fisher, 2012) In conclusion, Japan cannot even own a handgun let alone an assault weapon. Trying to apply what works in one country to another in this case would not work. Some Japanese feel that they would not even want these freedoms because they look to the authority for answers first. (Talmadge, 2013) This is a cultural preference in Japan. The country as a whole tends to think about the good of the whole of the people before what is good for the individual. If it is better for them to not have firearms, and clearly the statistics prove it, then that is the direction they have chosen for themselves.

Zara's Secret for Fast Fashion Case Study Example | Topics and Well Written Essays - 1750 words

Zara's Secret for Fast Fashion - Case Study Example   Zara’s competitors in the fashion and industry were amazed at how the company was rapidly expanding both locally and internationally. It is important to note that all these stores were opened under the company’s brand but when the ventured into the Asian market, the company’s managers had different views of the market in that they decided to exercise the concept of Franchising. For instance, in Malaysia, Kuwait and Saudi Arabia, the company operates as a B2B Company. Zara did virtually no advertising unlike its international clothing competitors such as Gap, Benetton, and H&M. Instead, the company places only two ads to promote its yearly sales and announce the opening of the new store. This decision has led Zara to realize 0.3% average revenue instead of 4%. Zara store managers have no discretion about the look and the feeling of their stores (Wikipedia, n.d). Zara does not aim to produce classic clothes that are always in style instead of the company intend ed to have its clothes to have fairly short life spans both in stores and customers wardrobe. In the year 2003, Inditex operated 1,558 stores in more than 40 countries of which 550 were part of Zara chain stores. The company has 90,000 employees of which 80% are female while the rest are male. Currently, Inditex is the biggest and fastest growing retailer while Zara is the biggest leading retail innovator in the world and that has established its place in the fashion industry by offering not only apparel and accessories for women fashion but also for children and men. More so, Zara Company had provided and established a unique environment for shopping by altering the manner others companies such as Gap and H&M store appear. The company changes its layouts often to incorporate artwork. Zara has realized how to expand and make a profit due to its capability to face the apparel challenges in the market. This paper aims to discuss the businesses model and key elements of disruptive busi ness, identify distinctive competencies of disruptive companies and discuss the competitive advantages of disruptive companies. Disruptive business model Disruptive innovation is a creation that aids establish a new market and value network, and it usually goes on to disrupt the existing market and value network by replacing or displacing an earlier technology. Disruptive model is used by a company to improve a product in a manner that the market does not expect by designing for a different segment of consumers in the new market and afterward by lowering the prices in the current or existing market. Businesses that adapt and applies disruptive model usually have a competitive advantage over its competitors in the market (Wikipedia, n.d). For instance, companies such as Dell and Zara have this model in order to remain in front of their competitors. Dell has survived the bust and now looks better for it since, in one of the worst PC industry history, Dell has gained enough share to be come the leading PC seller in the world.  

Is there an afterlife and what would be required for an afterlife Essay

Is there an afterlife and what would be required for an afterlife - Essay Example Death has been considered the only certain thing in life. There is even consensus to the effect that a number of changes take place during the transition from life to death, which therefore follows that changes making up life are distinct from those making up survival. In distinguishing these two varying changes, we can give a number of personal identity criteria through time to explain death (Baillie, 1993). First, we can use a criterion that has been popularized by Hume, Plato and a multiplicity of world religions. According to this criterion, human beings are either immaterial souls or even pure egos (Hume, 1739). This can be construed to mean that human beings possess the physical bodies only on a contingent basis and therefore not a necessity as far as living (in this life and the afterlife) is concerned. This being the case therefore, it is proper to argue that human being continues to live even after death. If anything their bodies are contingent and not necessarily a must-hav e in their living and especially their afterlife (Ayer, 2006). The second criterion has to do with the claim that a human being has two distinct components namely a body and a mind. This criterion, the so-called Cartesian Dualism, named so in honor of Rene Descartes, claim that the two components namely, the material body and the immaterial mind are distinct and therefore can exist separately. In fact it goes on to claim that the immaterial mind can exist separately from the material body particularly when the material body dies. This idea has however failed to convince many people because of a number of obvious faults in the reasoning behind it. For instance, is it logical for an immaterial mind to effect any change in a material body? This is the main problem that this idea has been unable to address, a problem that has since assumed the name â€Å"the problem of interactionism† (Levine, 1989). The reasons that Hume advance in arguing that death is survivable are convincing in whichever one looks at them. For instance, I am convinced that there must be another component that leaves the body to rot, otherwise what happened when a human being is resurrected by a supernatural being. Does he/she resurrect with another body or the same body which at the time must have long decomposed. This clearly demonstrates just how probable a human being might possess a separate invisible component that is left behind after his/her fresh dies and subsequently decomposes (Jerome, 1966). In opposing the idea of an afterlife, Hume argues that every creature’s ability is always proportionate to the task ahead of it. This is best demonstrated in a Hare’s or an Antelope’s ability to out-run a fox or a Lion respectively. It is also the reason why a Hare have not been equipped with the ability to appreciate Operas, which would be superfluous to its life. Given that a match between abilities and tasks has been found to cut across all creatures, it is reason able to assume that we are also matched to the tasks before us (Hume & Sayre-McCord 2006). Looked in the context of our ‘design flaws’ as far as having the ability to anticipate an afterlife is concerned, one can only conclude that there is no afterlife. Look at the way we are normally less concerned with doing good for a reward in our afterlife. Look a

Ebay Case Analysis Assignment Example | Topics and Well Written Essays - 750 words

Ebay Case Analysis - Assignment Example These include SWOT analysis, STEEP analysis, competitor analysis, and financial comparison. These analysis will enable the management determine whether the company has the prospects of growth and success in its operation and will assist in the formulation of policies required in overcoming the threats and the challenges (Campbell and Craig 73). The tools further reveals the opportunities and the strengths that eBay must continue to pursue to remain a market leader. In as much as eBay recorded growth since inception, recent growth rate has slowed and revenues have declined for the first time. The company growth was achieved because of the strategic alliances and acquisitions that were entered into by the company. Acquisitions and strategic alliances is a faster way of entering new markets and increasing the market share. eBay has started losing market its market share to new innovative competitors and is faced by a host of challenges. Through analyzing the case Donahoe, the company pr esident, aims at determining the following: The greatest opportunities and threats that faces eBay external environment eBay’s greatest weaknesses and strengths whether eBay purpose statement is sufficient in directing the management in making important decisions. Whether shifting of eBay shift from the core competencies is a right strategy for the company’s success. ... STEEP analysis will help in determining the competitiveness of eBay. Factors considered in this analysis are the social, technological, economic, environmental, and political environment. The social factors provide eBay with an opportunity to increase its returns and performance. With an increasing number of people using the internet, eBay is in a position to attract new entrants and increase the number of people buying through the company market place. The youthful population access most of the information online and eBay management can capitalize on this to market their products on face book, twitter, and even Skype. This will reduce marketing costs and further increase the scale of operation. The increasing aging population on the other hand will likely to hamper the performance of eBay because it reduces the company’s primary customers. Economic factors also influence the operations and performance of eBay. First, the economic downturn provides eBay an opportunity to incre ase the number of buyers. With the decline in the disposable income, increased unemployment, and mortgage crisis will make customers look for discounts and therefore prefer eBay as a market place (Campbell and Craig 76). On the other hand, economic downturn also poses a threat on eBay competitiveness as it could result into fewer buyers and force the company to lower the fixed prices by more than 70% to become competitive. This could therefore shrink the revenues and profits of thee business. Furthermore, the fees on charged on infrequent sellers could have a negative impact on the revenues. In addition, the expansion of developing countries would give eBay an

Organizational Behavior Management Research Paper

Organizational Behavior Management - Research Paper Example Whenever a manager within the organization makes a conscious decision, there are always repercussions. The manager has a number of skills that he/she need to exploit. Human skills imply the knowledge to communicate and inspire the workers as individuals and as groups. In fact, Robbins & Judge 2011, p. 4 noted that â€Å"developing managers’ interpersonal skills also helps organisations attract and keep high performing employees†. Conceptual skills involve making sense of complex situations and handling them effectively. According to Sims p. 235, participative leadership model is a form where leadership is shared. This means that the decision making within the organization is a shared matter. No one single person is running all affairs within the organization while the rest of the employees are subservient to him/her. A leader in the organization who adopts this model has the benefit of a number of gains. One of these is the increased participation of everyone within the organization (Mayer p. 8). This means that the employees are made to feel as though their opinions matter. It also means that the management at the top does not hog the limelight for its own benefit. It also means that as far as accountability is concerned, the decisions taken at the top will be acceptable all the way down to the lowest cadres. Management has a number of roles which include information roles. The management needs to constantly scan and apply what other organizations are up to on the matter of participative leadership. It also means that new ideas are introduced in the process of discourse and application of the decisional role (Mayer p. 8). It is not always that the leadership at the top has all the answers. They may come across a matter that has them utterly confounded. In such an instance, participatory leadership will come in to provide insights as to how the problem can be surmounted.

Film, Fashion and Food in India Essay Example | Topics and Well Written Essays - 3000 words

Film, Fashion and Food in India - Essay Example The paper "Film, Fashion and Food in India" talks about the Indian fashion, film and food. These three Indian identities are unique, and one cannot miss to identify them with India. The article is going to focus on three of the most modern Indian cultures identified all over the world, film, fashion, and food.Indian Painting as well as its fashion sense, from history, may generally have a division into three great religious divisions- Buddhist, Hindu, and Islamic. The Hindu type of painting has a reference to as Rajput. The reason is that it has a connection with the Hill Rajput of the Punjab and Rajputana. The Islamic art is known as Mughal, as its existence is due to the support it had from the existing dynasty. Rajput and Buddhist paintings were representative in showing practically the religious life of India. Buddhist had a representation by the turban on their heads. The main message of both the paintings was religion, and the chief characteristic of the paintings was mysticism . Mughal painting, on the other hand, had true sophistication, and in nature diverse and realistic. Indian court paintings and designs are famous for Mughal court paintings of the 16th Century. The rise of Mughal court paintings had a fusion of Islamic, Indian, Persian and somehow European influence.The combination of all the materials led to the creation of something new and unique which the current generation distinguishes as Mughal Art. The Mughal kingdom was however not the first Islamic empire.

Nursing, current health developments to improve the risk of deep vain Essay

Nursing, current health developments to improve the risk of deep vain thrombosis for patients in hospital - Essay Example They are reliable in their field of practice and in researches related to their field of practice. The research is valid in terms of ethical processes, especially in ensuring that the research gathering process is voluntary and gained through informed consent (Hucker, 2001). The research is paid for by the authors, not by any private corporations. Hence, the validity of the results are not overshadowed by private funding (Friedman, 2004). Most of the materials used present unbiased and reliable results (Davies and Dodd, 2002). This study is significant in terms of establishing the importance of adopting and prioritizing preventive measures for DVT. Dennis, M. (2009) Effectiveness of thigh-length GCS to reduce DVT after stroke. 2518 patients Quantitative/experimental Outcome-blinded, randomised controlled trial Non-significant absolute reduction in risk of 0.5% (95% CI -1.9% to 2.9%). Skin breaks, ulcers, blisters, and skin necrosis were significantly more common in patients allocated to GCS than in those allocated to avoid their use. Results do not support use of thigh-length GCS in patients admitted to hospital with acute stroke. The author is a highly qualified expert in the field of medicine, most especially in cardiovascular medicine and in the clinical management of thrombus issues. He is reliable in his field and has carried out different researches in related studies (Williams, 2010). The research is valid in terms of the statistical processes applied, and different methodology applied (Strauss and Corbin, 1990). The research has been paid for by the author, not by any private corporation. The analysis and data results are based on thorough analysis of results (Glesne and Peshkin, 1992). No logical fallacies are seen from the author’s conclusions (Ethridge, 2004). This study is significant because it reviews the use of which length GC stockings in patients at risk for DVT Falanga, A (2005) To review the clinical significance of VTE in patients wit h cancer and the strategies for management of VTE in these patients, including the potential role of low molecular weight heparins (LMWHs). 49 studies Quantitative Clinical review The use of low molecular weight heparin (LMWH) therapy instead of VKAs may be beneficial in patients. This agent offers an effective alternative to VKAs in the long-term management of VTE, that is free from the practical problems associated with the use of VKAs and without increasing the risk of bleeding. Alternative means of managing DVT among cancer patients present with advantages which assist in their long-term care. These alternatives must be considered for patients. The author is also a highly qualified expert in the field of medicine, specifically in oncology and cardiovascular diseases. She has published several studies in cardiovascular diseases and in cancer management. She is highly respected in her field and has published numerous researches on cancer management, including thrombus management. This makes her a highly reliable author (Williams, 2010). The methods applied truly measure what they intend to measure (Joppe, 2000). The authors in the reviewed studies outline some of their methodology which is within ethical parameters of reliability and

Urban Life in the Middle Ages Essay Example for Free

Urban Life in the Middle Ages Essay The book â€Å"Urban Life in the Middle Ages† by Keith D. Lilley discusses historical development and urban changes affected urban population during the Middle Ages. The author claims that: †the Middle Ages is a contested heritage – it means different things for different people† (p. 21). Lilley describes a medieval town as the main regional and even cultural unit which kept traditions, values and unique way of life. The book consists of an introduction, 7 chapters, conclusions, tables, figures and plates. The first three chapters address urban culture and heritage, legal foundations of towns and the main institutions. The author describes medieval culture and legacies, the main factors and driven forces of change. Also, Lilley draws a line between medieval urban heritage and contemporary culture stating that â€Å"medieval urbanism impinges upon the modern age’ (p. 17). The second chapter describes the main institutions and their impact on and role in urbanism. Lilley pays a special attention to chartered towns, functions of municipal government and urban governance. The fourth chapter discusses emergence of and development of towns in England and Wales, France and in east Central Europe. Lilley explains that in many countries, towns’ population was numbered thousands rather than hundreds, and the city was clearly differentiated from the rural settlements around it. Within the city, however, population, as not particularly dense, and certainly was not uniformly distributed. Lilley suggests that a significant proportion of the area within towns was used for agriculture or viniculture, while a town remained a center of cultural, religious and material life. The fifth and the sixth chapters are devoted to urban planning and ownership. Lilley writes that urban population paid much attention to landscapes and urban planning which marked city’s identity and national culture. Lilley gives examples of urban designs, structure of urban settlements and location of the central part and periphery of the city. The sixth chapter describes the main types of property rights and landholding. The fundamental fact about the property rights was their fragmentation. Holdings were scattered over a wide area: a couple of holdings in one settlement, a vineyard in the next, an estate in the next still. Even within rural settlements large, compact blocks of land or sizable estates comprising an entire settlement were extremely rare. In the seventh chapter, Lilley describes domestic life and personality of townspeople, their values and preferences, way of life and traditions, occupations and trade. The book does not have a separate chapter for church and its impact on town life, but Lilley discusses the problems and issues of churches in every chapter. He underlines that religion played a crucial role in lives of medieval people determining their way of life and traditions. Churches were predominantly found in urban contexts, and monastic foundations were increasingly favored by the elite. The surrounding countryside was dotted with small family monasteries, nunneries and proprietary churches. A society in which rural elites were increasingly prepared to invest in a local church or a family monastery was one in which they might also be prepared to make donations to large-scale monastic foundations to build up their local prestige. I would recommend this book to everyone interested in history and sociology, archeology, urban planning and cultural studies. The book is based on substantial analysis of resources and historical documents, and involves excellent illustrative materials for every chapter. A unique vision of historical development and comparative analysis with modern city planning and culture Works Cited 1. Lilley, K. D. Urban Life in the Middle Ages: 1000-1450 (European Culture and Society). Palgrave Macmillan, 2002.

Computers Essay Example for Free

Computers Essay Books will never be completely replaced by computers. Computers can crash, and all information will be lost. If the power goes out in your house, you cant read on your computer, but you can pick up a book. What you read on a comp. is just light or something on a screen. A book is is a permentent physical printing. If computers replaced books, wed all have serious eye problems. The joy of reading would be lost. Id hate to have to depend on a machine for my relaxing reading time. Books are forever, computers are until it breaks down, and everything will be lost. Books can be taken care of, as can computers, but there are books that are a hundred years old, I havent heard of anyone with the same computer for even 10 yrs. see more:essay on computer I guess comps are getting new and better, but books are still better to read. However, on the up side for comps, 1 comp, can store probebly over a hundred books, sort of like an i pod. But if the 1 comp breaks, that a hundred books too. Computers are good, and should store that kind of stuff, but I dont think it should completly replace the book. That would really suck. You cant haul your computer everywhere you go, like the bus, waiting rooms, the lunchroom, bed, etc. Too large, awkward and unwieldy. Even laptops. You cant just throw a laptop in your purse. I take books with me everywhere I go so I have something to kill the time with when Im waiting. I read everyday on my lunch hour. I read before I go to bed. Sitting in front of a coputer to read makes my eyes, butt and back hurt, because you have to sit up to do it and the computer screen is too bright. Plus, books dont need batteries. I think its okay for kids in school, and Im sure that there are other instances, but I highly doubt that books will ever become obsolete.f a book from reading an actual book instead of a screen. . Finding good novels or non-fiction would be more difficult because of the volume of all kinds and qualities of same. A computer, even a notebook, will never replace a pocket-sized paperback you can stick in a purse and read anywhere. Books dont need batteries, service, defragging or any of those things. Like many aspiring authors, Im excited by the possibilities posed by on-line publishing, but I have some serious reservations about what could happen if there is an unchecked volume of materials placed out there. Also, collecting royalties could make writing for profit even more of a challenge than it is today. Intro Science has made4 mch developments during the recent decades. It has developed many gadgets for our comfort but in my opinion they cn never replace the traditional things and ways. One of the greatest invention of technology and sciences is computer

Economic Impacts of Climate Change

Economic Impacts of Climate Change Economic Impacts of Climate Change and Variability on Agricultural Production in the Middle East and North Africa Region 1. Introduction The accumulation of scientific evidences indicating that growing greenhouse gases will warm our planet becomes clearer. Higher temperature and changes in precipitation level will shrinkage crop yield in many countries. IPCC (2007) reported that most land areas will experience an increase in average temperature with more frequent heat waves, more stressed water resources and desertification. Stern and Treasury (2006) noted, that the â€Å"the poorest countries and populations† will bear the greatest costs of climate change. Therefore, the impact of climate change on agriculture has received increasing attention in the last decade literatures. Climate change coupled with population growth will deeply affect the availability and quality of water resources in the Middle East and North Africa (MENA) region (Alpert, Krichak, Shafir, Haim, Osetinsky, 2008; Evans, 2010; Gao Giorgi, 2008). In a similar way, Sowers and Weinthal (2010) argued that since most of the MENA region is arid and hyper-arid, slight changes in water accessibility and arable land have substantial consequences for human security. It is worth to take into account the climatic variability in addition to climate change in order to provide an integrated analysis of the impact of climate variables. Selvaraju and Baas (2007) stated that climate variability is the way climate fluctuates yearly above or below a long-term average value while climate change is the long-term continuous change (increase or decrease) to average weather conditions or the range of weather. In this study, we consider the possible impacts of climate changes and climate variability on agricultural production, with a focus on the region of Middle East and North Africa, where the deleterious impacts of climate change are generally projected to be greatest. In order to achieve such objective, Fixed Effect Regression (FER) is used to Estimate the agricultural production function using cross-section time series data of MENA countries. The advantages of panel data analysis are; getting actual responses is more informative to policy makers than resul ts from field trials. Second, country fixed effects capture all additive differences between various countries (Stock Watson, 2003). 2. Data Sources In order to estimate the production function, cross-sectional time series (panel data) are used. The panel set consists of 20 MENA countries for the time period between 1961 and 2009 including Algeria, Bahrain, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syrian Arab Republic, Tunisia, Turkey, United Arab Emirates, and Yemen. Table 1 shows the data description and data sources. Due to unavailability of the data for few countries, some observations are missing therefore panel data in the model are unbalanced. The data set consists of two variables group. The first is economics variables such as net agricultural production index number in international dollar, agricultural machinery, total fertilizers consumed, labors, and land. The second data subset is climatic variables like temperature and precipitation. The monthly climatic data were available by meteorological stations rather than by country as shown in Table 2. Therefore, it was necessary to calculate monthly country averages of climate variables and summed up into seasonal data. Table 1 Data description and sources Variable Unit Description Source Agricultural production 1000 I$ Net agricultural Production Index Number (2004-2006 = 100) FAO statistics Agricultural machinery (tractors) Number Agricultural tractors, refer to total wheel, crawler or track-laying type tractors and pedestrian tractors used in agriculture. FAO statistics Fertilizers consumption Ton nutrients Total consumption of chemical fertilizers (N+P2O5+K2O) International Fertilizer Industry Association Livestock Head Buffaloes + cattle FAO statistics Labor Million Total economically active population International Labor Organization (LABORSTA) Land 1000 Hectare Total area of cultivated land FAO statistics Temperature Celsius Monthly mean temperature FAOClim-NET: Agroclimatic database management system Precipitation millimeter Monthly mean precipitation FAOClim-NET: Agroclimatic database management system 3. Climate change and agriculture in Mena countries According to the World Bank, The Middle East and North Africa is one of the regions that is most vulnerable to climate change, with the highest level of water scarcity in the world. The region has a total area of about 14 million km2, of which more than 87 per cent is desert. It is characterized by a high dependency on climate-sensitive agriculture and a large share of its population and economic activities are located in flood-prone urban coastal zones. Bucknall (2007) classify the MENA countries into three groups on the subject of water source and availability. First group is countries have adequate quantities of renewable water, but the within-country and within year variations are problematically large including Iran, Lebanon, Morocco, and Tunisia. Second group is countries that have low levels of renewable water resources and highly dependent on non-renewable groundwater sources and supplies by desalination of sea water like Bahrain, Jordan, Kuwait, Libya, Oman, Qatar, Saudi Arabia, the United Arab Emirates, and Yemen. The last group is countries that mainly dependent on the inflow of transboundary rivers such as the Nile, the Tigris, and the Euphrates including Syria, Iraq, and Egypt. Table 2 Descriptive Statistics for Aggregated climatic variables during the Period 1961-2009 No. Metrological stations Temperature (c °) Precipitation (mm/year) Mean Std. Dev. Mean Std. Dev. Algeria 95 19.91 0.99 23.98 5.99 Bahrain 1 26.62 0.91 8.51 7.74 Egypt 52 22.42 0.63 4.14 2.15 Iran 67 17.31 2.70 20.03 9.05 Iraq 29 22.35 2.82 13.62 7.98 Israel 13 19.80 1.53 29.33 14.31 Jordan 15 18.95 1.08 15.77 5.04 Kuwait 15 25.91 1.23 13.73 7.49 Lebanon 12 18.49 1.78 56.58 17.08 Libya 27 21.14 0.80 14.74 4.12 Morocco 34 18.03 0.71 32.29 10.95 Oman 27 26.78 0.60 8.00 5.34 Qatar 2 27.46 0.70 6.40 5.05 Saudi Arabia 67 25.19 0.91 5.93 3.73 Sudan 47 28.30 0.89 48.51 57.62 Syrian Arab Republic 20 18.30 0.90 21.61 7.26 Tunisia 25 19.35 0.98 30.30 8.34 Turkey 315 13.03 0.89 51.31 7.73 United Arab Emirates 13 27.56 1.33 5.47 5.11 Yemen 12 25.52 3.52 9.70 7.44 4. Methodology There are various models can be employed to assess the impact of climate change on agricultural production. Ricardian model, Agronomic model, and crop simulation models are most widely adopted models for the climate impact studies (Lee, Nadolnyak, Hartarska, 2012). The Ricardian model estimates the examines the impact of climate and other variables on land values and farm revenues using cross-sectional data (Mendelsohn, Nordhaus, Shaw, 1994). Crop Simulation Models (CSM) restrict the analysis to crop physiology and compare crop productivity for different climatic conditions (Salvo, Begalli, Signorello, 2013). Because of the country level panel analysis, the production function model is adopted for the analysis in the present study. Model To estimate the impact of climatic change on agriculture production in MENA countries, an empirical production function for country i at time t net agricultural production index is a function of some economic inputs (Frisvold Ingram, 1995) and climatic variables: . Y represents the net agricultural production index,; M, F, L, A, and V are economic inputs which include agricultural machinery, fertilizer consumption, labor, cultivated area, and livestock respectively. T and represent temperature and precipitation. Number of agricultural tractors is used as proxy of agricultural capital stock and number of cattle and buffaloes is used as proxy of livestock production. For climatic variables temperature and precipitation, mean of the winter season (January, February, and March) , spring (April, May, and June), summer (July, August, and September), and Fall (October, November, and December) are involved in the model. Following (Barrios, Ouattara, Strobl, 2008; Belloumi, 2014; Lee et al. , 2012), The agricultural production model in the present study has the following specification form: (1) By taking the log on both sides, the fixed effect panel model is: (2) According to the fixed effect model, ÃŽ ±i (i=1†¦.n) is the unknown intercept for each country that absorb unabsorbed time variant effects and is a time varying effects. For climatic variables, both the linear and quadratic forms are integrated into the model in order to consider the nonlinear relationship between agricultural production and climatic variables. Variability As it is also sensible to estimate the impact of the variability of climatic variable along with the seasonal deviation and the mean temperature and precipitation, the squared of the mean differences of temperature and precipitation for each season observation is used in the second model. Then, This variability was measured by the seasonal coefficient of variation (CV) calculated as the seasonal ratio of the standard deviation to the mean of each climate variable for each country. 5. Results and discussion Review different papers to strengthen the discussion Table 3 shows the results of fixed effects regression analysis in which we estimated the impact of agricultural inputs and climatic variables on agricultural production in MENA countries. The results show that the regression coefficient of temperature is positive and statistically significant in spring, summer, and fall seasons. By contrast, temperature in winter has negative coefficient at significance level of 0.01. Regarding the estimated parameters of precipitation, precipitation during spring showed negative impact at significance level of 1%. The estimated parameters of nonlinear climatic variables indicated that each of the squared summer temperature has positive coefficient at significance level 0.05 while squared winter temperature has negative and significant impact at level of 0.05. In addition, squared spring precipitation showed positive influence. As expected, production inputs showed significant and positive relation with agricultural production except machinery and fertilizers consumption. As inputs and agricultural production are in logarithmic form, the regression coefficients reflect the production elasticity of each input. Therefore, 1 percent increase in each input of livestock, labor, and land, with keeping all other inputs the same, leads to increase in agricultural production by 0.16%, 0.98%, and 0.91% respectively. Table 3 Fixed Effects Regression analysis of climate change Variables Coefficients S.E. P value Intercept -0.0582 0.0160 -0.058 Winter Temperature -0.0582** 0.0160 0.000 Spring Temperature 0.0431* 0.0212 0.042 Summer Temperature 0.0730** 0.0213 0.001 Fall Temperature 0.0408** 0.0154 0.008 Winter Temperature Squared -0.0024* 0.0010 0.014 Spring Temperature Squared 0.0002 0.0016 0.892 Summer Temperature Squared 0.0043* 0.0019 0.028 Fall Temperature Squared -0.0005 0.0010 0.643 Winter Precipitation -0.0006 0.0004 0.128 Spring Precipitation 0.0004* 0.0002 0.050 Summer Precipitation -0.0001 0.0002 0.760 Fall Precipitation 0.0002 0.0003 0.438 Winter Precipitation Squared -5.0600E-06 5.1400E-06 0.325 Spring Precipitation Squared 3.8800E-06 6.2400E-06 0.535 Summer Precipitation Squared 1.5300E-05* 7.6600E-06 0.047 Fall Precipitation Squared -3.4000E-06 4.7100E-06 0.470 Machinery -0.0471 0.0282 0.095 Fertilizers Consumption -0.0269 0.0166 0.107 Livestock 0.1599** 0.0389 0.000 Labor 0.9802** 0.0481 0.000 Land 0.9128** 0.1000 0.000 R2 within 0.8932 R2 between 0.7827 R2 overall 0.7917 F test 120.8300 F-ui=0 951.88** Obs. No 980 The results of Fixed Effects Regression analysis of climate variability as explanatory variables and agricultural production are presented in Table 4. The results suggest that temperature variability in fall season seems to have significant and positive relation with agricultural production while it has negative relation in spring. Squared variability of temperature during winter and summer seasons have significant and negative relation. Furthermore, variability of winter precipitation have positive and significant relation. Likewise, the regression coefficient of squared variation of winter and summer precipitation showed significant and positive relation with agricultural production.. Table 4 Fixed Effects Regression analysis of climate variability Variables Coefficients S.E. P value Intercept 3.8918** 0.0422 0.000 Winter Temperature -0.2451 0.1818 0.178 Spring Temperature -0.5086** 0.1921 0.008 Summer Temperature 0.0418 0.1850 0.821 Fall Temperature 0.8505** 0.1929 0.000 Winter Temperature Squared -0.0825* 0.0408 0.044 Spring Temperature Squared 0.0204 0.0370 0.581 Summer Temperature Squared -0.0571** 0.0216 0.008 Fall Temperature Squared -0.0071 0.0487 0.884 Winter Precipitation 0.0425** 0.0090 0.000 Spring Precipitation 0.0269 0.0774 0.728 Summer Precipitation 0.1717 0.2138 0.422 Fall Precipitation -0.1943 0.1946 0.319 Winter Precipitation Squared 0.0221** 0.0062 0.000 Spring Precipitation Squared -0.0020 0.0034 0.558 Summer Precipitation Squared 0.0005* 0.0003 0.044 Fall Precipitation Squared 0.0056 0.0042 0.18 R2 within 0.793 R2 between 0.943 R2 overall 0.769 F test 11.620 F-ui=0 11.330 Obs. No 980 Marginal Impact analysis The excepted marginal effects of climatic change and variability on agricultural production appraised at the mean are calculated by the first-order differentiation of the equation 2 to temperature and precipitation respectively: (3) (4) The elaticities of climate change and variability of temperature and precipitation are derived from equations (3) and (4) respectively by dividing both equation (3) on and equation (4) on . therefore, the elasticities can be computed as : (5) (6) Where and refer to temperature change or variability and precipitation change or variability respectively. The marginal impact of climate change and climate variability on agricultural production in the MENA region are presented in Table 5. The impact and the elsticities of Climate change and climate variability are calculated using the regression coefficient and mean values of temperatures and precipitation. The results indicate that increase of temperature in winter season has negative impact on agricultural production as one percent increase in temperature during winter season will lead to a decrease in agricultural production value by 1.12 percent. Instead, increasing the temperature during the other seasons showed positive impact. Temperature variability negative impact on agricultural production during winter and spring as one percent increase of temperature variability, will lead to about 0.09 and 0.14 percent decrease in agricultural production. In regard to the impact precipitation changes, the results confirmed that increasing precipitation during winter and fall season have negative impact on agricultural production in MENA countries while it has positive impact in spring and summer seasons. Moreover, the results of the impact of precipitation variability showed that precipitation variability has negative impact during winter and summer seasons, whereas one percent increase of precipitation variability will lead to decrease in agricultural production in the MENA region by 0.037 and 0.013 percent respectively. However, precipitation variability showed positive impact during the season of spring and fall. Table 5 Marginal impacts of climate change and variability on agricultural production Climate change Climate Variability Marginal impact Elasticity Marginal impact Elasticity Temperature Winter -4.517 -1.115 -12.408 -0.087 Spring 3.746 1.567 -29.211 -0.139 Summer 4.130 2.025 7.039 0.027 Fall 2.897 0.927 41.713 0.265 Precipitation Winter -0.162 -0.092 -2.884 -0.037 Spring 0.019 0.005 1.038 0.013 Summer 0.272 0.046 -3.303 -0.071 Fall -0.040 -0.019 0.071 0.001 References Alpert, Pinhas, Krichak, Simon O, Shafir, Haim, Haim, David, Osetinsky, Isabella. (2008). Climatic trends to extremes employing regional modeling and statistical interpretation over the E. Mediterranean. Global and Planetary Change, 63(2), 163-170. Barrios, Salvador, Ouattara, Bazoumana, Strobl, Eric. (2008). The impact of climatic change on agricultural production: Is it different for Africa? Food Policy, 33(4), 287-298. Belloumi, Mounir. (2014). Investig

QA Fred Smith CEO of the FDX holding company that includes FedEx :: GCSE Business Marketing Coursework

QA Fred Smith CEO of the FDX holding company that includes FedEx Federal Express Corp. started tracking packages electronically well before the commercial Internet emerged. Now, that infrastructure has been firmly plugged into the Internet, letting customers track shipments in real time and even pull reams of shipping data into their internal systems. Meantime, the shipping giant is taking those lessons into new territory. It has launched a consulting practice that helps manufacturers tighten their own supply chains and reduce inventory requirements by more closely tracking the movement of supplies and finished products. Related Story: FedEx Delivers On CEO's IT Vision Additional Transforming Business Strategy Stories Transformation Of The Enterprise Home Page Colleagues and outsiders say it was all part of founder Fred Smith's vision, well before the commercial Internet, that "the information about the package would become as important as the package itself." Smith, now CEO of the FDX holding company that includes FedEx, spoke with editor in chief Robert Preston and managing editor David Joachim at FDX headquarters in Memphis. Excerpts follow: InternetWeek: How hands on are you when it comes to FDX's Internet strategy? Smith: I'm very intimately involved with our strategies as they apply to information and telecommunications. I'm not an expert in them, but I think I have a very good understanding of what they can do and the respective trends under way in those fields. InternetWeek: How vital is CEO involvement in the Internet strategy of a company? Smith: I think it's vital in almost every industry I know of. I mean, I don't know many industries as well as I do my own, but I am constantly amazed at the profundity of IT and the Internet in almost every field and human endeavor, whether it's medicine, farming, the military, or any other thing that I brush up against. It's just all pervasive, it's changing the face of everything. And those that are not involved in it do so at their peril, in my opinion. InternetWeek: What advice do you have to IT executives who recognize their own company as an Internet laggard but have trouble convincing upper management that there will be serious consequences to that? Smith: I guess my advice to them would be to either convince them or get out. They're going to be toast if they don't. Big businesses, particularly big businesses that are involved in lots of different activities, have a very difficult time dealing with qualitative issues and, I guess as Wayne Gretzky would say, skating to where the puck is going to be rather than where it is now.

The Most Affordable Vacation for a College Student :: Research Papers Travel Essays

The Most Affordable Vacation for a College Student In December, my boyfriend is heading to Iraq to fight for his country and for all of us in the United States. We have decided together, that upon his arrival home, we will reward ourselves with a vacation. The question we both had though was where should we take this vacation? We both believe that next year will be a really hard time for both of us, being separated for at least a year. I have decided to take the initiative to research our possible destinations with the most affordable rates. Together, we narrowed our choices down and decided to vacation in Hawaii or an island in the Bahamas. So with this information, I decided to research the locations of our choice to help us finalize our decision. The first thing I did was decide to research Maui, Hawaii. I have heard many great things about Hawaii, and thought that this would make a great report. I gathered information online, comparing airline prices, hotel prices and also attractions in Maui. To gather even more information, I traveled to the travel agency in which I belong to, AAA’s where I received a lot of great catalogs on Maui. I did not know definitely where I was going with this project, but decided to research the information. I discovered that Hawaii was just a little bit out of a college student’s budget, even if saving for a year. So I thought I would compare and research the Bahamas, our other choice. I once again started online, researching much like I did with Hawaii. This time when I traveled to AAA’s, I talked to Sharon Biggs, a very helpful travel agent. She gave me brochures explaining about the two islands in the Bahamas. She told me that because I was under the age of 21, the better vacation choice was the Bahamas over Hawaii. When we travel on our vacation, my boyfriend will be over 21, but I will not. The age of 21 meaning, I could not drink, but he could. My final step was to call my aunt and uncle, who have traveled to the Bahamas, Mexico, Bermuda, and taken several cruises. I wanted to receive their insight on the location that I had chosen. They were very reliable in the sense that they travel often and I have not been to all the places that they have been to.

Forecasting Essay

1. Tupperware only uses both qualitative and quantitative forecasting techniques, culminating in a final forecast that is the consensus of all participating managers. False (Global company profile: Tupperware Corporation, moderate) 2. The forecasting time horizon and the forecasting techniques used tend to vary over the life cycle of a product. True (What is forecasting? moderate) 3. Sales forecasts are an input to financial planning, while demand forecasts impact human resource decisions. True (Types of forecasts, moderate) 4. Forecasts of individual products tend to be more accurate than forecasts of product families. False (Seven steps in the forecasting system, moderate) 5. Most forecasting techniques assume that there is some underlying stability in the system. True (Seven steps in the forecasting system, moderate) 6. The sales force composite forecasting method relies on salespersons’ estimates of expected sales. True (Forecasting approaches, easy) 7. A time-series model uses a series of past data points to make the forecast. True (Forecasting approaches, moderate) 8. The quarterly â€Å"make meeting† of Lexus dealers is an example of a sales force composite forecast. True (Forecasting approaches, easy) 9. Cycles and random variations are both components of time series. True (Time-series forecasting, easy) 10. A naive forecast for September sales of a product would be equal to the sales in August. True (Time-series forecasting, easy) 11. One advantage of exponential smoothing is the limited amount of record keeping involved. True (Time-series forecasting, moderate) 12. The larger the number of periods in the simple moving average forecasting method, the greater the method’s responsiveness to changes in demand. False (Time-series forecasting, moderate) 13. Forecast including trend is an exponential smoothing technique that utilizes two smoothing constants: one for the average level of the forecast and one for its trend. True (Time-series forecasting, easy) 14. Mean Squared Error and Coefficient of Correlation are two measures of the overall error of a forecasting model. False (Time-series forecasting, easy) 15. In trend projection, the trend component is the slope of the regression equation. True (Time-series forecasting, easy) 16. In trend projection, a negative regression slope is mathematically impossible. False (Time-series forecasting, moderate) 17. Seasonal indexes adjust raw data for patterns that repeat at regular time intervals. True (Time-series forecasting, moderate) 18. If a quarterly seasonal index has been calculated at 1.55 for the October-December quarter, then raw data for that quarter must be multiplied by 1.55 so that the quarter can be fairly compared to other quarters. False (Time-series forecasting: Seasonal variation in data, moderate) 19. The best way to forecast a business cycle is by finding a leading variable. True (Time-series forecasting, moderate) 20. Linear-regression analysis is a straight-line mathematical model to describe the functional relationships between independent and dependent variables. True (Associative forecasting methods: Regression and correlation analysis, easy) 21. The larger the standard error of the estimate, the more accurate the forecasting model. False (Associative forecasting methods: Regression and correlation analysis, easy) 22. A trend projection equation with a slope of 0.78 means that there is a 0.78 unit rise in Y for every unit of time that passes. True (Time-series forecasting: Trend projections, moderate) 23. In a regression equation where Y is demand and X is advertising, a coefficient of determination (R2) of .70 means that 70% of the variance in advertising is explained by demand. False (Associative forecasting methods: Regression and correlation analysis, moderate) 24. Tracking limits should be within  ± 8 MADs for low-volume stock items. True (Monitoring and controlling forecasts, moderate) 25. If a forecast is consistently greater than (or less than) actual values, the forecast is said to be biased. True (Monitoring and controlling forecasts, moderate) 26. Focus forecasting tries a variety of computer models and selects the best one for a particular application. True (Monitoring and controlling forecasts, moderate) 27. Many service firms use point-of-sale computers to collect detailed records needed for accurate short-term forecasts. True (Forecasting in the service sector, moderate) MULTIPLE CHOICE 28. Tupperware’s use of forecasting a.involves only a few statistical tools b.concentrates on the low-level dealer, and is not aggregated at the company level c.relies on the fact that all of its products are in the maturity phase of the life cycle d.is a major source of its competitive edge over its rivals e.takes inputs from sales, marketing, and finance, but not from production d (Global company profile, moderate) 29. Which of the following statements regarding Tupperware’s forecasting is false? a.Tupperware’s fifty profit centers generate the basic set of projections. b.Tupperware uses at least three quantitative forecasting techniques. c.Tupperware uses only quantitative forecasting techniques. d.†Sales per active dealer† is one of three key forecasting variables (factors). e.†Jury of executive opinion† is the ultimate forecasting tool used at Tupperware. c (Global company profile, moderate) 30. Forecasts a.become more accurate with longer time horizons b.are rarely perfect c.are more accurate for individual items than for groups of items d.all of the above e.none of the above b (What is forecasting? moderate) 31. One use of short-range forecasts is to determine a.production planning b.inventory budgets c.research and development plans d.facility location e.job assignments e (What is forecasting? moderate) 32. Forecasts are usually classified by time horizon into three categories a.short-range, medium-range, and long-range b.finance/accounting, marketing, and operations c.strategic, tactical, and operational d.exponential smoothing, regression, and time series e.departmental, organizational, and industrial a (What is forecasting? easy) 33. A forecast with a time horizon of about 3 months to 3 years is typically called a a.long-range forecast b.medium-range forecast c.short-range forecast d.weather forecast e.strategic forecast b (What is forecasting? moderate) 34. Forecasts used for new product planning, capital expenditures, facility location or expansion, and R&D typically utilize a a.short-range time horizon b.medium-range time horizon c.long-range time horizon d.naive method, because there is no data history e.all of the above c (What is forecasting? moderate) 35. The three major types of forecasts used by business organizations are a.strategic, tactical, and operational b.economic, technological, and demand c.exponential smoothing, Delphi, and regression d.causal, time-series, and seasonal e.departmental, organizational, and territorial b (Types of forecasts, moderate) 36. Which of the following is not a step in the forecasting process? a.Determine the use of the forecast. b.Eliminate any assumptions. c.Determine the time horizon. d.Select forecasting model. e.Validate and implement the results. b (The strategic importance of forecasting, moderate) 37. The two general approaches to forecasting are a.qualitative and quantitative b.mathematical and statistical c.judgmental and qualitative d.historical and associative e.judgmental and associative a (Forecasting approaches, easy) 38. Which of the following uses three types of participants: decision makers, staff personnel, and respondents? a.executive opinions b.sales force composites c.the Delphi method d.consumer surveys e.time series analysis c (Forecasting approaches, moderate) 39. The forecasting model that pools the opinions of a group of experts or managers is known as the a.sales force composition model b.multiple regression c.jury of executive opinion model d.consumer market survey model e.management coefficients model c (Forecasting approaches, moderate) 40. Which of the following is not a type of qualitative forecasting? a.executive opinions b.sales force composites c.consumer surveys d.the Delphi method e.moving average e (Forecasting approaches, moderate) 41. Which of the following techniques uses variables such as price and promotional expenditures, which are related to product demand, to predict demand? a.associative models b.exponential smoothing c.weighted moving average d.simple moving average e.time series a (Forecasting approaches, moderate) 42. Which of the following statements about time series forecasting is true? a.It is based on the assumption that future demand will be the same as past demand. b.It makes extensive use of the data collected in the qualitative approach. c.The analysis of past demand helps predict future demand. d.Because it accounts for trends, cycles, and seasonal patterns, it is more powerful than causal forecasting. e.All of the above are true. c (Time-series forecasting, moderate) 43. Time series data may exhibit which of the following behaviors? a.trend b.random variations c.seasonality d.cycles e.They may exhibit all of the above. e (Time-series forecasting, moderate) 44. Gradual, long-term movement in time series data is called a.seasonal variation b.cycles c.trends d.exponential variation e.random variation c (Time-series forecasting, moderate) 45. Which of the following is not present in a time series? a.seasonality b.operational variations c.trend d.cycles e.random variations b (Time-series forecasting, moderate) 46. The fundamental difference between cycles and seasonality is the a.duration of the repeating patterns b.magnitude of the variation c.ability to attribute the pattern to a cause d.all of the above e.none of the above a (Time-series forecasting, moderate) 47. In time series, which of the following cannot be predicted? a.large increases in demand b.technological trends c.seasonal fluctuations d.random fluctuations e.large decreases in demand d (Time-series forecasting, moderate) 48. What is the approximate forecast for May using a four-month moving average? 49. Which time series model below assumes that demand in the next period will be equal to the most recent period’s demand? a.naive approach b.moving average approach c.weighted moving average approach d.exponential smoothing approach e.none of the above a (Time-series forecasting, easy) 50. Which of the following is not a characteristic of simple moving averages? a.It smoothes random variations in the data. b.It has minimal data storage requirements. c.It weights each historical value equally. d.It lags changes in the data. e.It smoothes real variations in the data. b (Time-series forecasting, moderate) 51. A six-month moving average forecast is better than a three-month moving average forecast if demand a.is rather stable b.has been changing due to recent promotional efforts c.follows a downward trend d.follows a seasonal pattern that repeats itself twice a year e.follows an upward trend a (Time-series forecasting, moderate) 52. Increasing the number of periods in a moving average will accomplish greater smoothing, but at the expense of a.manager understanding b.accuracy c.stability d.responsiveness to changes e.All of the above are diminished when the number of periods increases. d (Time-series forecasting, moderate) 53. Which of the following statements comparing the weighted moving average technique and exponential smoothing is true? a.Exponential smoothing is more easily used in combination with the Delphi method. b.More emphasis can be placed on recent values using the weighted moving average. c.Exponential smoothing is considerably more difficult to implement on a computer. d.Exponential smoothing typically requires less record keeping of past data. e.Exponential smoothing allows one to develop forecasts for multiple periods, whereas weighted moving averages does not. d (Time-series forecasting, moderate) 54. Which time series model uses past forecasts and past demand data to generate a new forecast? a.naive b.moving average c.weighted moving average d.exponential smoothing e.regression analysis d (Time-series forecasting, moderate) 55. Which is not a characteristic of exponential smoothing? a.smoothes random variations in the data b.easily altered weighting scheme c.weights each historical value equally d.has minimal data storage requirements e.none of the above; they are all characteristics of exponential smoothing c (Time-series forecasting, moderate) 56. Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast? a.0 b.1 divided by the number of periods c.0.5 d.1.0 e.cannot be determined d (Time-series forecasting, moderate) 57. Given an actual demand of 103, a previous forecast value of 99, and an alpha of .4, the exponential smoothing forecast for the next period would be a.94.6 b.97.4 c.100.6 d.101.6 e.103.0 c (Time-series forecasting, moderate) 58. A forecast based on the previous forecast plus a percentage of the forecast error is a(n) a.qualitative forecast b.naive forecast c.moving average forecast d.weighted moving average forecast e.exponentially smoothed forecast e (Time-series forecasting, moderate) 59. Given an actual demand of 61, a previous forecast of 58, and an of .3, what would the forecast for the next period be using simple exponential smoothing? a.45.5 b.57.1 c.58.9 d.61.0 e.65.5 c (Time-series forecasting, moderate) 60. Which of the following values of alpha would cause exponential smoothing to respond the most slowly to forecast errors? a.0.10 b.0.20 c.0.40 d.0.80 e.cannot be determined a (Time-series forecasting, moderate) 61. A forecasting method has produced the following over the past five months. What is the mean absolute deviation? 62. The primary purpose of the mean absolute deviation (MAD) in forecasting is to a.estimate the trend line b.eliminate forecast errors c.measure forecast accuracy d.seasonally adjust the forecast e.all of the above c (Time-series forecasting, moderate) 63. Given forecast errors of -1, 4, 8, and -3, what is the mean absolute deviation? a.2 b.3 c.4 d.8 e.16 c (Time-series forecasting, moderate) 64. The last four months of sales were 8, 10, 15, and 9 units. The last four forecasts were 5, 6, 11, and 12 units. The Mean Absolute Deviation (MAD) is a.2 b.-10 c.3.5 d.9 e.10.5 c (Time-series forecasting, moderate) 65. A time series trend equation is 25.3 + 2.1 X. What is your forecast for period 7? a.23.2 b.25.3 c.27.4 d.40.0 e.cannot be determined d (Time-series forecasting, moderate) 66. For a given product demand, the time series trend equation is 53 – 4 X. The negative sign on the slope of the equation a.is a mathematical impossibility b.is an indication that the forecast is biased, with forecast values lower than actual values c.is an indication that product demand is declining d.implies that the coefficient of determination will also be negative e.implies that the RSFE will be negative c (Time-series forecasting, moderate) 67. In trend-adjusted exponential smoothing, the forecast including trend (FIT) consists of a.an exponentially smoothed forecast and an estimated trend value b.an exponentially smoothed forecast and a smoothed trend factor c.the old forecast adjusted by a trend factor d.the old forecast and a smoothed trend factor e.a moving average and a trend factor b (Time-series forecasting, moderate) 68. Which of the following is true regarding the two smoothing constants of the Forecast Including Trend (FIT) model? a.One constant is positive, while the other is negative. b.They are called MAD and RSFE. c.Alpha is always smaller than beta. d.One constant smoothes the regression intercept, whereas the other smoothes the regression slope. e.Their values are determined independently. e (Time-series forecasting, moderate) 69. Demand for a certain product is forecast to be 800 units per month, averaged over all 12 months of the year. The product follows a seasonal pattern, for which the January monthly index is 1.25. What is the seasonally-adjusted sales forecast for January? a.640 units b.798.75 units c.800 units d.1000 units e.cannot be calculated with the information given a (Time-series forecasting, moderate) 70. A seasonal index for a monthly series is about to be calculated on the basis of three years’ accumulation of data. The three previous July values were 110, 150, and 130. The average over all months is 190. The approximate seasonal index for July is a.0.487 b.0.684 c.1.462 d.2.053 e. cannot be calculated with the information given b (Time-series forecasting, moderate) 71. A fundamental distinction between trend projection and linear regression is that a.trend projection uses least squares while linear regression does not b.only linear regression can have a negative slope c.in trend projection the independent variable is time; in linear regression the independent variable need not be time, but can be any variable with explanatory power d.linear regression tends to work better on data that lack trends e.trend projection uses two smoothing constants, not just one c (Associative forecasting methods: Regression and correlation analysis, moderate) 72. The percent of variation in the dependent variable that is explained by the regression equation is measured by the a.mean absolute deviation b.slope c.coefficient of determination d.correlation coefficient e.intercept c (Associative forecasting methods: Regression and correlation analysis, moderate) 73. The degree or strength of a linear relationship is shown by the a.alpha b.mean c.mean absolute deviation d.correlation coefficient e.RSFE d (Associative forecasting methods: Regression and correlation analysis, moderate) 74. If two variables were perfectly correlated, the correlation coefficient r would equal a.0 b.less than 1 c.exactly 1 d.-1 or +1 e.greater than 1 d (Associative forecasting methods: Regression and correlation analysis, moderate) 75. The last four weekly values of sales were 80, 100, 105, and 90 units. The last four forecasts were 60, 80, 95, and 75 units. These forecasts illustrate a.qualitative methods b.adaptive smoothing c.slope d.bias e.trend projection d (Monitoring and controlling forecasts, easy) 76. The tracking signal is the a.standard error of the estimate b.running sum of forecast errors (RSFE) c.mean absolute deviation (MAD) d.ratio RSFE/MAD e.mean absolute percentage error (MAPE) d (Monitoring and controlling forecasts, moderate) 77. Computer monitoring of tracking signals and self-adjustment if a signal passes a preset limit is characteristic of a.exponential smoothing including trend b.adaptive smoothing c.trend projection d.focus forecasting e.multiple regression analysis b (Monitoring and controlling forecasts, moderate) 78. Many services maintain records of sales noting a.the day of the week b.unusual events c.weather d.holidays e.all of the above e (Forecasting in the service sector, moderate) 79. Taco Bell’s unique employee scheduling practices are partly the result of using a.point-of-sale computers to track food sales in 15 minute intervals b.focus forecasting c.a six-week moving average forecasting technique d.multiple regression e.a and c are both correct e (Forecasting in the service sector, moderate) 96. A skeptical manager asks what short-range forecasts can be used for. Give her three possible uses/purposes. Any three of: planning purchasing, job scheduling, work force levels, job assignments, production levels. (What is forecasting? moderate) 97. A skeptical manager asks what long-range forecasts can be used for. Give her three possible uses/purposes. Any three of: planning new products, capital expenditures, facility location or expansion, research and development. (What is forecasting? moderate) 98. Describe the three forecasting time horizons and their use. Forecasting time horizons are: short range—generally less than three months, used for purchasing, job scheduling, work force levels, production levels; medium range—usually from three months up to three years, used for sales planning, production planning and budgeting, cash budgeting, analyzing operating plans; long range—usually three years or more, used for new product development, capital expenditures, facility planning, and R&D. (What is forecasting? moderate) 99. List and briefly describe the three major types of forecasts. The three types are economic, technological, and demand; economic refers to macroeconomic, growth and financial variables; technological refers to forecasting amount of technological advance, or futurism; demand refers to  product demand. (Types of forecasts, moderate) 100. List the seven steps involved in forecasting. 1. Determine the use of the forecast. 2. Select the items that are to be forecast. 3. Determine the time horizon of the forecast. 4. Select the forecasting model(s). 5. Gather the data needed to make the forecast. 6. Make the forecast. 7. Validate the forecasting mode and implement the results. (Seven steps in the forecasting process, moderate) 101. What are the realities of forecasting that companies face? First, forecasts are seldom perfect. Second, most forecasting techniques assume that there is some underlying stability in the system. Finally, both product family and aggregated forecasts are more accurate than individual product forecasts. (Seven steps in the forecasting system, moderate) 102. What are the differences between quantitative and qualitative forecasting methods? Quantitative methods use mathematical models to analyze historical data. Qualitative methods incorporate such factors as the decision maker’s intuition, emotions, personal experiences, and value systems in determining the forecast. (Forecasting approaches, moderate) 103. List four quantitative forecasting methods. The list includes naive, moving averages, exponential smoothing, trend projection, and linear regression. (Forecasting approaches, moderate) 104. What is a time-series forecasting model? A time series forecasting model is any mathematical model that uses historical values of the quantity of interest to predict future values of that quantity. (Forecasting approaches, easy) 105. What is the difference between an associative model and a time-series model? A time series model uses only historical values of the quantity of interest to predict future values of that quantity. The associative model, on the other hand, attempts to identify underlying causes or factors that control the variation of the quantity of interest, predict future values of these factors, and use these predictions in a model to predict future values of the specific quantity of interest. (Forecasting approaches, moderate) 106. Name and discuss three qualitative forecasting methods. Qualitative forecasting methods include: jury of executive opinion, where high-level managers arrive at a group estimate of demand; sales force composite, where salespersons’ estimates are aggregated; Delphi method, where respondents provide inputs to a group of decision makers; the group of decision makers, often experts, then make the actual forecast; consumer market survey, where consumers are queried about their future purchase plans. (Forecasting approaches, moderate) 107. List the four components of a time series. Which one of these is rarely forecast? Why is this so? Trend, seasonality, cycles, and random variation. Since random variations follow no discernible pattern, they cannot be predicted, and thus are not forecast. (Time-series forecasting, moderate) 108. Compare seasonal effects and cyclical effects. A cycle is longer (typically several years) than a season (typically days, weeks, months, or quarters). A cycle has variable duration, while a season has fixed duration and regular repetition. (Time-series forecasting, moderate) 109. Distinguish between a moving average model and an exponential smoothing model. Exponential smoothing is a weighted moving average model wherein previous values are weighted in a specific manner–in particular, all previous values are weighted with a set of weights that decline exponentially. (Time-series forecasting, moderate) 110. Describe three popular measures of forecast accuracy. Measures of forecast accuracy include: (a) MAD (mean absolute deviation). This is a sum of the absolute values of individual errors divided by the  number of periods of data. (b) MSE (mean squared error). This is the average of the squared differences between the forecast and observed values. (c) MAPE (mean absolute percent error) is independent of the magnitude of the variable being forecast. (Forecasting approaches: Measuring forecast error, moderate) 111. Give an example—other than a restaurant or other food-service firm—of an organization that experiences an hourly seasonal pattern. (That is, each hour of the day has a pattern that tends to repeat day after day.) Explain. Answer will vary. However, two non-food examples would be banks and movie theaters. (Time-series forecasting, moderate) 112. Explain the role of regression models (time series and otherwise) in forecasting. That is, how is trend projection able to forecast? How is regression used for causal forecasting? For trend projection, the independent variable is time. The trend projection equation has a slope that is the change in demand per period. To forecast the demand for period t, perform the calculation a + bt. For causal forecasting, the independent variables are predictors of the forecast value or dependent variable. The slope of the regression equation is the change in the Y variable per unit change in the X variable. (Time-series forecasting, diff icult) 113. List three advantages of the moving average forecasting model. List three disadvantages of the moving average forecasting model. Two advantages of the model are that it uses simple calculations, it smoothes out sudden fluctuations, and it is easy for users to understand. The disadvantages are that the averages always stay within past ranges, that they require extensive record keeping of past data, and that they do not pick up on trends very well. (Time-series forecasting, moderate) 114. What does it mean to â€Å"decompose† a time series? To decompose a time series means to break past data down into components of trends, seasonality, cycles, and random blips, and to project them forward. (Time-series forecasting, easy) 115. Distinguish a dependent variable from an independent variable. The  independent variable causes some behavior in the dependent variable; the dependent variable shows the effect of changes in the independent variable. (Associative forecasting methods: Regression and correlation, moderate) 116. Explain, in your own words, the meaning of the coefficient of determination. The coefficient of determination measures the amount (percent) of total variation in the data that is explained by the model. (Associative forecasting methods: Regression and correlation, moderate) 117. What is a tracking signal? How is it calculated? Explain the connection between adaptive smoothing and tracking signals. A tracking signal is a measure of how well the forecast actually predicts. Its calculation is the ratio of RSFE to MAD. The larger the absolute tracking signal, the worse the forecast is performing. Adaptive smoothing sets limits to the tracking signal, and makes changes to its forecasting models when the tracking signal goes beyond those limits. (Monitoring and controlling forecasts, moderate) 118. What is focus forecasting? It is a forecasting method that tries a variety of computer models, and selects the one that is best for a particular application. (Monitoring and controlling forecasts, easy) 124. A management analyst is using exponential smoothing to predict merchandise returns at an upscale branch of a department store chain. Given an actual number of returns of 154 items in the most recent period completed, a forecast of 172 items for that period, and a smoothing constant of 0.3, what is the forecast for the next period? How would the forecast be changed if the smoothing constant were 0.6? Explain the difference in terms of alpha and responsiveness. 166.6; 161.2 The larger the smoothing constant in an exponentially smoothed forecast, the more responsive the forecast. (Time-series forecasting, easy) 126. The following trend projection is used to predict quarterly demand: Y = 250 – 2.5t, where t = 1 in the first quarter of 2004. Seasonal (quarterly) relatives are Quarter 1 = 1.5; Quarter 2 = 0.8; Quarter 3 = 1.1; and Quarter 4 = 0.6. What is the seasonally adjusted forecast for the four quarters of 2006? PeriodProjectionAdjusted 9 227.5341.25 10 225180.00 11222.5224.75 12220132.00 (Time-series forecasting, moderate) 127. Jim’s department at a local department store has tracked the sales of a product over the last ten weeks. Forecast demand using exponential smoothing with an alpha of 0.4, and an initial forecast of 28.0. Calculate MAD and the tracking signal. What do you recommend? 130. A small family-owned restaurant uses a seven-day moving average model to determine manpower requirements. These forecasts need to be seasonalized because each day of the week has its own demand pattern. The seasonal relatives for each day of the week are: Monday, 0.445; Tuesday, 0.791; Wednesday, 0.927; Thursday, 1.033; Friday, 1.422; Saturday, 1.478; and Sunday 0.903. Average daily demand based on the most recent moving average is 194 patrons. What is the seasonalized forecast for each day of next week? The average value multiplied by each day’s seasonal index. Monday: 194 x .445 = 86; Tuesday: 194 x .791 = 153; Wednesday: 194 x .927 = 180; Thursday: 194 x 1.033 = 200; Friday: 194 x 1.422 = 276; Saturday: 194 x 1.478 = 287; and Sunday: 194 x .903 = 175. (Associative forecasting methods: Regression and correlation, moderate) 131. A restaurant has tracked the number of meals served at lunch over the last four weeks. The data shows little in terms of trends, but does display substantial variation by day of the week. Use the following information to determine the seasonal (daily) index for this restaurant. 132. A firm has modeled its experience with industrial accidents and found that the number of accidents per year (Y) is related to the number of employees (X) by the regression equation Y = 3.3 + 0.049*X. R-Square is 0.68. The regression is based on 20 annual observations. The firm intends to employ 480 workers next year. How many accidents do you project? How much confidence do you have in that forecast? Y = 3.3 + 0.049 * 480 = 3.3 + 23.52 = 26.52 accidents. This is not a time series, so next year = year 21 is of no relevance. Confidence comes from the coefficient of determination; the model explains 68% of the variation in number of accidents, which seems respectable. (Associative forecasting methods: Regression and correlation, moderate) 133. Demand for a certain product is forecast to be 8,000 units per month, averaged over all 12 months of the year. The product follows a seasonal pattern, for which the January monthly index is 1.25. What is the seasonally-adjusted sales forecast for January? 8,000 x 1.25 = 10,000 (Time-series forecasting, easy) 134. A seasonal index for a monthly series is about to be calculated on the basis of three years’ accumulation of data. The three previous July values were 110, 135, and 130. The average over all months is 160. The approximate seasonal index for July is  (110 + 135 + 130)/3 = 125; 125/160 = 0.781 (Time-series forecasting,  moderate) 135. Marie Bain is the production manager at a company that manufactures hot water heaters. Marie needs a demand forecast for the next few years to help decide whether to add new production capacity. The company’s sales history (in thousands of units) is shown in the table below. Use exponential smoothing with trend adjustment, to forecast demand for period 6. The initial forecast for period 1 was 11 units; the initial estimate of trend was 0. The smoothing constants are = .3 and  · = .3 136. The quarterly sales for specific educational software over the past three years are given in the following table. Compute the four seasonal factors. 137. An innovative restaurateur owns and operates a dozen â€Å"Ultimate Low-Carb† restaurants in northern Arkansas. His signature item is a cheese-encrusted beef medallion wrapped in lettuce. Sales (X, in millions of dollars) is related to Profits (Y, in hundreds of thousands of dollars) by the regression equation Y = 8.21 + 0.76 X. What is your forecast of profit for a store with sales of $40 million? $50 million? Students must recognize that sales is the independent variable and profits is dependent; the problem is not a time series. A store with $40 million in sales: 40 x 0.76 = 30.4; 30.4 + 8.21 = 38.61, or $3,861,000 in profit; $50 million in sales is estimated to profit 46.21 or $4,621,000. (Associative forecasting methods: Regression and correlation, moderate) 138. Arnold Tofu owns and operates a chain of 12 vegetable protein â€Å"hamburger† restaurants in northern Louisiana. Sales figures and profits for the stores are in the table below. Sales are given in millions of dollars; profits are in hundreds of thousands of dollars. Calculate a regression line for the data. What is your forecast of profit for a store with sales of $24 million? $30 million? Students must recognize that â€Å"sales† is the independent variable and profits is dependent. Store number is not a variable, and the problem is not a time series. The regression equation is Y = 5.936 + 1.421 X (Y = profit, X = sales). A store with $24 million in sales is estimated to profit 40.04 or $4,004,000; $30 million in sales should yield 48.566 or $4,856,600 in profit. (Associative forecasting methods: Regression and correlation, moderate) 139. The department manager using a combination of methods has forecast sales of toasters at a local department store. Calculate the MAD for the manager’s forecast. Compare the manager’s forecast against a naive forecast. Which is better?

Organic food Essay

We have all heard the phrase â€Å"What you don’t know won’t hurt you† and it has undoubtedly applied to many situations in our lives that we are still unaware of. We like to toss around this phrase without worrying too much about what it implies because that is the whole point of the phrase, not to worry. When it comes to what we are putting into our bodies, though, what we do not know can indeed hurt us immensely. In the United States, we have grown accustomed to not thinking much about what we are consuming. The main factors we look for in food are taste and price. We live in a consumer society where money rules our nation, it rules our lives, and it rules us. Money has become the main focus for every decision we make, but when it comes to something as important as our health, should we look at a few other factors? With societies concerns focusing on wealth and profit, there is no surprise that the food industry finds the cheapest ways to produce the most food. Consequently, this produces many negative effects on aspects of our lives such as our health and the environment. When choosing what foods to consume, we should begin to pay more attention to factors other than the price tag. The food industry obviously plays a big role in this epidemic of processed food, but they are not the only ones to blame. Yes they are the ones taking advantage of our ignorance by mass-producing cheap food that they know we will not think twice about, but the ignorance is our fault. Author of The Omnivore’s Dilemma, Michael Pollan, describes the current foundation of the food industry, â€Å"Our food system depends on consumers’ not knowing much about it beyond the price disclosed by the checkout scanner. Cheapness and ignorance are mutually reinforcing† (Pollan 245). Pollan is correct in his assumption that most Americans do not know much about their food besides how much it cost. Most of them are not even aware that they do not know what is in their food. They subconsciously assume that chicken is chicken and cheese is cheese, but unfortunately that is hardly ever the case. Many people choose to live along these guidelines of â€Å"ignorance is bliss† by not paying attention to the horror stories of the food industry; they turn their heads from documentaries on animal treatment and plug their ears at the mention of the real ingredients of their precious snacks. As long as the food they are eating tastes good and did not cost a lot of money, they are content with not knowing how unhealthy it might be. Pollan further explains another reason people buy the cheapest available food: It makes good economic sense that people with limited money to spend on food would spend it on the cheapest calories they can find, especially when the cheapest calories—fats and sugars—are precisely the ones offering the biggest neurobiological rewards. (Pollan 108) People with lower incomes are confined to buying cheap food, typically the most processed and unhealthy food, because with their limited funds they cannot afford to care about the quality of what they are eating. They buy what is cheapest because that is all they can get. As long as they have food in their stomachs, they do not complain or worry too much about the side affects. Eating food that may not be very healthy definitely outweighs the alternative of eating nothing and starving. Americans are ignorant of the food that they purchase either because they choose not to educate themselves or because they really have no choice. Either way, they are missing out on other possibilities of obtaining food that have many advantages. Not knowing what our food is made of also prevents us from knowing what alternative food options are available to us. Because we see no problems with our current food choices, we see no reason to discover new ones. The processed food at the supermarket is all we know because it is the most convenient and affordable from of nourishment we can obtain. Pollan’s book includes the testimony of someone who buys food from a local, organic farmer, â€Å"†¦for me it’s all about the taste, which is just so different—this is a chickinier chicken. Art’s chickens just taste cleaner, like the chicken I remember when I was a kid† (Pollan 252). The food available from local farmers is not only better for our health and the environment but it also tastes better. We have grown accustomed to the artificially flavored food we buy from grocery stores and do not realize that the food we eat could taste better and more natural. The locally grown food tastes healthier and more natural because that is precisely what it is. The artificially engineered taste of chicken in a common chicken nugget is not what a chicken should taste like. Besides enhanced taste, buying from local farmers offers many other benefits as well. An organic farmer interviewed in The Omnivore’s Dilemma explains some more benefits of buying locally, With our food all of the costs are figured into the price. Society is not bearing the cost of water pollution, of antibiotic resistance, of food-borne illnesses, of crop subsidies, of subsidized oil and water—of all the hidden costs to the environment and the taxpayer that make cheap food seem cheap. (Pollan 243) One of the main reasons why people do not want to look into these alternative methods of eating is because they are more expensive. People overlook these opportunities because the organic food appears overpriced, but when you evaluate all these factors it might not be as overpriced as you might think. Yes the food is more expensive but it stands true that you get what you pay for. When paying more, you are receiving a whole lot more that benefits your health, community, and environment. The extra money that would be spent on food, you might save on your medical bills and taxes. Locally produced food is healthier for you and it carries a much less chance of containing disease and illness. Another bonus of buying from local farms: there is less pollution created than in the factories and slaughterhouses of the globalized food industry. If people became aware of alternative food options and the benefits associated with them, they would be more inclined to pay better attention to what they are buying. This would not only improve ones personal health, but also the environment. Although money remains a very important role in deciding what we purchase, it would benefit us to consider a few other aspects of the food that we buy. Paying attention to details such as what goes into the food, where it is produced, and how it is produced would lead us to make healthier decisions. More often than not, a satisfying answer to these questions will not be found in the food at our local supermarkets, but rather a local farmer. Buying from these farmers would mean supporting a healthy environment and body. Their production methods are healthier and much more environmentally friendly than any factories in a big-name food industry. While it may seem that this is a simple choice, many Americans will continue to ignore these truths. When it comes down to it, money rules everything and it will take a lot more than the promise of better health for people to overlook a price tag. They say ignorance is bliss, but when that ignorance leads to decisions that contaminate our bodies and our environment, the bliss will be short lived.