Pestel Analysis of Automotive Domain in Germany

Brochure More information from http://www. researchandmarkets. com/reports/1202569/ PEST ANALYSIS – Automotive Sector in Germany Description: PEST analysis of any industry sector investigates the important factors that are affecting the industry and influencing the companies operating in that sector. PEST is an acronym for political, economic, social and technological analysis. Political factors include government policies relating to the industry, tax policies, laws and regulations, trade restrictions and tariffs etc. The economic factors relate to changes in the wider economy such as economic growth, interest rates, exchange rates and inflation rate, etc. Social factors often look at the cultural aspects and include health consciousness, population growth rate, age distribution, changes in tastes and buying patterns, etc. The technological factors relate to the application of new inventions and ideas such as R&D activity, automation, technology incentives and the rate of technological change. Synergyst’s PEST Analysis is a perfect tool for managers and policy makers; helping them in analyzing the forces that are driving their industry and how these factors will influence their businesses and the whole industry in general. Our product also presents a brief profile of the industry comprising of current market, competition in it and future prospects of that sector. Please note that the report compilation, presentation and dispatch may take 1-2 working days. Contents: SECTOR OVERVIEW — Current Market — Competition and Key Players — Market Forecast PEST ANALYSIS — Political Factors — Economic Factors — Social Factors — Technological Factors CONCLUSION Ordering: Order Online – http://www. researchandmarkets. com/reports/1202569/ Order by Fax – using the form below Order by Post – print the order form below and sent to Research and Markets, Guinness Centre, Taylors Lane, Dublin 8, Ireland. Page 1 of 2 Fax Order Form To place an order via fax simply print this form, fill in the information below and fax the completed form to 646-6071907 (from USA) or +353-1-481-1716 (from Rest of World). If you have any questions please visit http://www. researchandmarkets. com/contact/ Order Information Please verify that the product information is correct and select the format(s) you require. Product Name: Web Address: Office Code: PEST ANALYSIS – Automotive Sector in Germany http://www. researchandmarkets. com/reports/1202569/ OC8HKQNSORVUU Product Formats Please select the product formats and quantity you require: Quantity Hard Copy: CD ROM: Electronic: Electronic: EURO â‚ ¬482. 00 + Euro â‚ ¬50. 00 Shipping/Handling * EURO â‚ ¬482. 00 + Euro â‚ ¬50. 00 Shipping/Handling * EURO â‚ ¬321. 00 EURO â‚ ¬535. 00 * Shipping/Handling is only charged once per order. Contact Information Please enter all the information below in BLOCK CAPITALS Title: First Name: Email Address: * Job Title: Organisation: Address: City: Postal / Zip Code: Country: Phone Number: Fax Number: * Please refrain from using free email accounts when ordering (e. . Yahoo, Hotmail, AOL) Mr Mrs Dr Miss Last Name: Ms Prof Page 1 of 2 Payment Information Please indicate the payment method you would like to use by selecting the appropriate box. Pay by credit card: American Express Diners Club Master Card Visa Cardholder's Name Cardholder's Signature Expiry Date Card Number CVV Number Issue Date (for Diners Club only) Pay by check: Please post the check, accomp anied by this form, to: Research and Markets, Guinness Center, Taylors Lane, Dublin 8, Ireland. Please transfer funds to: Account number Sort code Swift code IBAN number Bank Address 833 130 83 98-53-30 ULSBIE2D IE78ULSB98533083313083 Ulster Bank, 27-35 Main Street, Blackrock, Co. Dublin, Ireland. Pay by wire transfer: If you have a Marketing Code please enter it below: Marketing Code: Please note that by ordering from Research and Markets you are agreeing to our Terms and Conditions at http://www. researchandmarkets. com/info/terms. asp Please fax this form to: (646) 607-1907 or (646) 964-6609 – From USA +353 1 481 1716 or +353 1 653 1571 – From Rest of World

Cognitive linguistics Essay

The cognitive linguistics enterprise is characterized by two fundamental commitments (Lakoff 1990). These underlie both the orientation and approach adopted by practicing cognitive linguists, and the assumptions and methodologies employed in the two main branches of the cognitive linguistics enterprise: cognitive semantics, and cognitive approaches to grammar, discussed in further detail in later sections. The first key commitment is the Generalization Commitment (Lakoff 1990). It represents a dedication to characterizing general principles that apply to all aspects of human language. This goal is just a special subcase of the standard commitment in science to seek the broadest generalizations possible. In contrast to the cognitive linguistics approach, other approaches to the study of language often separate the language faculty into distinct areas such as phonology (sound), semantics (word and sentence meaning), pragmatics (meaning in discourse context), morphology (word structure), syntax (sentence structure), and so on. As a consequence, there is often little basis for generalization across these aspects of language, or for study of their interrelations. This is particularly true of formal linguistics. Formal linguistics attempts to model language by positing explicit mechanical devices or procedures operating on theoretical primitives in order to produce all the possible grammatical sentences of a given language. Such approaches typically attempt precise formulations by adopting formalisms inspired by computer science, mathematics and logic. Formal linguistics is embodied most notably by the work of Noam Chomsky and the paradigm of Generative Grammar, as well as the tradition known as Formal Semantics, inspired by philosopher of language Richard Montague. Within formal linguistics it is usually argued that areas such as phonology, semantics and syntax concern significantly different kinds of structuring principles operating over different kinds of primitives. For instance, a syntax ‘module’ is an area in the mind concerned with structuring words into sentences, whereas a phonology ‘module’ is concerned with structuring sounds into patterns permitted by the rules of any given language, and by human language in general. This modular view of mind reinforces the idea that modern linguistics is justified in separating the study of language into distinct sub-disciplines, not only on grounds of practicality, but because the components of language are wholly distinct, and, in terms of organization, incommensurable. Cognitive linguists acknowledge that it may often be useful to treat areas such as syntax, semantics and phonology as being notionally distinct. However, given the Generalization Commitment, cognitive linguists do not start with the assumption that the ‘modules’ or ‘subsystems’ of language are organized in significantly divergent ways, or indeed that wholly distinct modules even exist. Thus, the Generalization Commitment represents a commitment to openly investigating how the various aspects of linguistic knowledge emerge from a common set of human cognitive abilities upon which they draw, rather than assuming that they are produced in encapsulated modules of the mind. The Generalization Commitment has concrete consequences for studies of language. First, cognitive linguistic studies focus on what is common among aspects of language, seeking to re-use successful methods and explanations across these aspects. For instance, just as word meaning displays prototype effects – there are better and worse examples of referents of given words, related in particular ways – so various studies have applied the same principles to the organization of morphology (e.g., Taylor, 2003), syntax (e.g., Goldberg, 1995), and phonology (e.g., Jaeger & Ohala, 1984). Generalizing successful explanations across domains of language isn’t just a good scientific practice – it is also the way biology works; reusing existing structures for new purposes, both on evolutionary and developmental timescales. Second, cognitive linguistic approaches often take a ‘vertical’, rather than a ‘horizontal’ strategy to the study of language. Language can be seen as composed of a set of distinct layers of organisation – the sound structure, the set of words composed by these sounds, the syntactic structures these words are constitutive of, and so on. If we array these layers one on top of the next as they unroll over time (like layers of a cake), then modular approaches are horizontal, in the sense that they take one layer and study it internally – just as a horizontal slice of cake. Vertical approaches get a richer view of language by taking a vertical slice of language, which includes phonology, morphology, syntax, and of course a healthy dollop of semantics on top. A vertical slice of language is necessarily more complex in some ways than a horizontal one – it is more varied and textured – but at the same time it affords possible explanations that are simply unavailable from a horizontal, modular perspective. The second commitment is termed the Cognitive Commitment (Lakoff 1990). It represents a commitment to providing a characterization of the general principles for language that accord with what is known about the mind and brain from other disciplines. It is this commitment that makes cognitive linguistics cognitive, and thus an approach which is fundamentally interdisciplinary in nature. Just as the Generalization Commitment leads to the search for principles of language structure that hold across all aspects of language, in a related manner, the Cognitive Commitment represents the view that principles of linguistic structure should reflect what is known about human cognition from the other cognitive and brain sciences, particularly psychology, artificial intelligence, cognitive neuroscience, and philosophy. In other words, the Cognitive Commitment asserts that models of language and linguistic organization proposed should reflect what is known about the human mind, rather than purely aesthetic dictates such as the use of particular kinds of formalisms or economy of representation (see Croft 1998 for discussion of this last point). The Cognitive Commitment has a number of concrete ramifications. First, linguistic theories cannot include structures or processes that violate known properties of the human cognitive system. For instance, if sequential derivation of syntactic structures violates time constraints provided by actual human language processing, then it must be jettisoned. Second, models that use known, existing properties of human cognition to explain language phenomena are more parsimonious than those that are built from a priori simplicity metrics. For example, quite a lot is known about human categorization, and a theory that reduces word meaning to the same mechanisms responsible for categorization in other cognitive domains is simpler than one that hypothesizes a separate system for capturing lexical semantics. Finally, it is incumbent upon the cognitive linguistic researcher to find convergent evidence for the cognitive reality of components of any proffered model or explanation. Having briefly set out the two key commitments of the cognitive linguistics enterprise, we now briefly map out the two, hitherto, best developed areas of the field. Cognitive linguistics practice can be roughly divided into two main areas o research: cognitive semantics and cognitive (approaches to) grammar. The area of study known as cognitive semantics is concerned with investigating the relationship between experience, the conceptual system, and the semantic structure encoded by language. In specific terms, scholars working in cognitive semantics investigate knowledge representation (conceptual structure), and meaning construction (conceptualization). Cognitive semanticists have employed language as the lens through which these cognitive phenomena can be investigated. Consequently, research in cognitive semantics tends to be interested in modelling the human mind as much as it is concerned with investigating linguistic semantics. A cognitive approach to grammar is concerned with modelling the language system (the mental ‘grammar’), than the nature of mind per se. However, it does so by taking as its starting points the conclusions of work in cognitive semantics. This follows as meaning is central to cognitive approaches to grammar.4 It is critical to note that although the study of cognitive semantics and cognitive approaches to grammar are occasionally separate in practice, this by no means implies that their domains of inquiry are anything but tightly linked –most work in cognitive linguistics finds it necessary to investigate both lexical semantics and grammatical organization jointly. As with research in cognitive semantics, cognitive approaches to grammar have also typically adopted one of two foci. Scholars such as Ronald Langacker have emphasized the study of the cognitive principles that give rise to linguistic organization. In his theory of Cognitive Grammar, Langacker has attempted to delineate the principles that structure a grammar, and to relate these to aspects of general cognition. The second avenue of investigation, pursued by researchers including Fillmore and Kay, Lakoff),Goldberg and more recently Bergen and Chang (2005) and Croft (2002), aims to provide a more descriptively and formally detailed account of the linguistic units that comprise a particular language. These researchers attempt to provide a broad-ranging inventory of the units of language, from morphemes to words, idioms, and phrasal patterns, and seek accounts of their structure, compositional possibilities, and relations. Researchers who have pursued this line of investigation are developing a set of theories that are collectively known as construction grammars. This general approach takes its name from the view in cognitive linguistics that the basic unit of language is a form-meaning pairing known as a symbolic assembly, or a construction. Cognitive semantics, like the larger enterprise of which it is a part, is not a unified framework. Those researchers who identify themselves as cognitive semanticists typically have a diverse set of foci and interests. However, there are a number of guiding principles that collectively characterize a cognitive approach to semantics. In this section we identify these guiding principles (as we see them). In section 5 we explore some of the major theories and research areas which have emerged under the ‘banner’ of cognitive semantics. The four guiding principles of cognitive semantics are as follows: i) Conceptual structure is embodied (the ‘embodied cognition thesis’) ii) Semantic structure is conceptual structure iii) Meaning representation is encyclopaedic iv) Meaning construction is conceptualization Conceptual structure is embodied Due to the nature of our bodies, including our neuro-anatomical architecture, we have a species-specific view of the world. In other words, our construal of ‘reality’ is mediated, in large measure, by the nature of our embodiment. One example of the way in which embodiment affects the nature of experience is in the realm of color. While the human visual system has three kinds of photoreceptors (i.e., color channels), other organisms often have a different number. For instance, the visual system of squirrels, rabbits and possibly cats, makes use of two color channels, while other organisms, including goldfish and pigeons, have four color channels. Having a different range of color channels affects our experience of color in terms of the range of colors accessible to us along the color spectrum. Some organisms can see in the infrared range, such as rattlesnakes, which hunt prey at night and can visually detect the heat given off by other organisms. Humans are unable to see in this range. The nature of our visual apparatus – one aspect of our embodiment – determines the nature and range of our visual experience. The nature of the relation between embodied cognition and linguistic meaning is contentious. It is evident that embodiment underspecifies which color terms a particular language will have, and whether the speakers of a given language will be interested in ‘color’ in the first place (Saunders, 1995; Wierzbicka, 1996). However, the interest in understanding this relation is an important aspect of the view in cognitive linguistics that the study of linguistic meaning construction needs to be reintegrated with the contemporary study of human nature. The fact that our experience is embodied – that is, structured in part by the nature of the bodies we have and by our neurological organization – has consequences for cognition. In other words, the concepts we have access to and the nature of the ‘reality’ we think and talk about are a function of our embodiment. We can only talk about what we can perceive and conceive, and the things that we can perceive and conceive derive from embodied experience. From this point of view, the human mind must bear the imprint of embodied experience. This thesis, central to cognitive semantics, is known as the thesis of embodied cognition. This position holds that conceptual structure (the nature of human concepts) is a consequence of the nature of our embodiment and thus is embodied. Semantic structure is conceptual structure The second guiding principle asserts that language refers to concepts in the mind of the speaker rather than, directly, to entities which inhere in an objectively real external world. In other words, semantic structure (the meanings conventionally associated with words and other linguistic units) can be equated with conceptual structure (i.e., concepts). This ‘representational’ view is directly at odds with the ‘denotational’ perspective of what cognitive semanticists sometimes refer to as objectivist semantics, as exemplified by some formal approaches to semantics. However, the claim that semantic structure can be equated with conceptual structure does not mean that the two are identical. Instead, cognitive semanticists claim that the meanings associated with linguistic units such as words, for example, form only a subset of possible concepts. After all, we have many more thoughts, ideas and feelings than we can conventionally encode in language. For example, as Langacker (1987) observes, we have a concept for the place on our faces below our nose and above our mouth where moustaches go. We must have a concept for this part of the face in order to understand that the hair that grows there is called a moustache. However, there is no English word that conventionally encodes this concept (at least not in the non-specialist vocabulary of everyday language). It follows that the set of lexical concepts, the semantic units conventionally associated with linguistic units such as words is only a subset of the full set of concepts in the minds of speaker-hearers.

Is tuition classes important to sudents? Essay

Students these days are increasingly pressured to churn out better and better academic results. As such, parents and the students themselves feel it is necessary to enrol in tuition classes. These after school classes, often conducted by school teachers trying to earn a little extra cash, provide an avenue to those who wish to improve on the subjects they are weak in, or secure a distinction for those they are already proficient in. Nowadays, it is actually rare to find a student, aside for those with economic difficulties, that does not attend tuition class. However, is tuition really necessary? Many students of yesteryear managed to achieve high grades in their examinations while bereft of tuition classes. Students who completed their studies in the 70s up to the early 90s typically did not attend tuition class, yet their grades were no less spectacular than those today. This is mainly because studying for exams, like any other activity, is intrinsically driven. One cannot force a student who is not motivated to swallow volumes of text and regurgitate it out during exams any more than one can force a tiger to change its stripes. Inversely, as happened of yesteryears, students who are motivated to study will do so even if the only illumination available is a guttering candle. Another unsettling aspect of modern tuition classes is that it is only available to those who can afford it. Popular teachers command top dollar for students to enrol in their classes, or have such a large class that one could mistake it for a school assembly. Tuition, unlike teachers teaching in school, is profit driven. Tuition centres allow teachers to rent their space for a percentage of the profits. If however the teacher under performs or is not economically viable, he or she will soon be given the boot. Due to its market driven structure, one can consider tuition classes a form of elitism. Students will proudly compare which of their teachers is better or how they managed to secure a position in a class with a famous teacher, while student who are from the poorer economic group can only stand by and watch. This, in turn, enforces the class divide later on. Tuition’s concept is to allow the student to practice a subject more than the given amount of hours in school. This theoretically helps the student  improve the said subject. For a majority, it really does work. Students do improve after participating in tuition classes. However, as pointed out above, studying is an intrinsic value. A student dragged kicking and screaming to do more exercises on a hated subject could potentially worsen the situation, rather than improve it. The same concept, however, has allowed for some students to truly unlock their potential. Given the vast kaleidoscope of human interactions, it is entirely possible that a student does not â€Å"click† with his or her school teacher, but gets merrily along with the tuition teacher. Any psychologist will attest to that when a person is affable to another, the quality and quantity of the message conveyed has increased potency. It is possible that a student dislikes a given subject in school, but pays complete attention in tuition classes due to the quality and charisma of the teacher. When this happens, the tuition class fulfils its objective of improving the student’s said subject. Tuition classes play a subtle, but highly important role that is not limited to the academic arena . Students from various schools often meet in tuition classes. This leads to interaction between students of different schools, classes and creeds. This lays an important foundation in the construction of a sociable person later on. The foundation of which a proper, functioning person who contributes back to society later on is built upon may, unlikely as it seems, be built during an Additional Mathematics tuition class. So is tuition necessary? My personal belief leans toward yes, it is necessary, but only for this day and age. Had this question been asked ten, twenty or even thirty years earlier, my answer would have been an unequivocal no.

Health care A TD #2 Essay Example | Topics and Well Written Essays - 250 words

Health care A TD #2 - Essay Example Texting while driving among the youth results in unpredictable driving behaviour such as speeding or lane weaving which increases the chances of hitting pedestrians or hitting other vehicles. Texting splits a driver’s reaction making him or her less able to react to sudden road perils. In order to reduce the rampant use of mobile phones by teenagers and other drivers, the best advocacy efforts would be to post visual images on the internet and set up bill boards along the streets to sensitize the youth on the dangers of texting while driving (Mason et al., 2011). On the internet, the best approach would be to offer the teenagers tips on how they can avoid texting while driving. The visual images can be posted on social media webpages such as Facebook, Twitter, Whatsapp and Google+. The webpage on the internet can also contain information such as how to keep their phones away when in a car or silence them. In order to make the advocacy more effective, the campaign slogan will be â€Å"you text, you call, you

Compare and Contrast the production in Nirvana's albums 'nevermind' Essay

Compare and Contrast the production in Nirvana's albums 'nevermind' and 'in utero' - Essay Example In contrast, In Utero was intended to have a much more primal sound than Nevermind. This was the intention from the first, and Cobain and producer Steve Albini made this sound come to life. One of the techniques in making the sound of In Utero more primal and natural than the sounds of Nevermind was that the band put microphones in the recording studios, therefore the sounds of the band performing in the studios were picked up in a natural way. The two albums were different, as well, according to Azzerrad (1993) in that Nevermind blended the influences in their songs – punk, pop and rock. However, in In Utero the songs tended to reflect one influence more than another. From the soft, Beatlesque pop song Dumb, to the pure punk sound of Milk It, the influences that inspired Nirvana were less blended in the third album than they were on the second album. This essay will examine the critical differences between the two albums, and will look at three songs in depth – In Bloo m from Nevermind; and Dumb and Heart Shaped Box from In Utero. Nevermind verses In Utero In the album Nevermind, which was Nirvana’s second studio album, the sound featured by the band would be characterized, for the most part, as rageful. As noted by Charles Cross (2001), in this biography of Kurt Cobain, titled Heavier than Heaven, many of the songs were written by Kurt Cobain in a period of despondency and rage after his girlfriend, Tobi, dumped him. Cobain wrote songs, such as â€Å"Aneurysm,† â€Å"Drain You,† â€Å"Lounge Act,† and â€Å"Lithium† during this period of despair, and these songs were all about Tobi. This rage was evident in the sound of the music from this album – as Rutherford (1991) puts it, songs like â€Å"Smells Like Teen Spirit,† with its stuttering chord progression to the thundering drums, displayed shades of metal, punk and pop at its heart. The song also featured the vocals which were in line with the gui tar. Classic Nirvana, according to Rutherford (1991) features heavy bridges, heavy choruses, and heavy drums and bass lines. Cohen (2009) specifically analyzed the song â€Å"In Bloom.† He states that the Nirvana sound was marked by distorted guitars with a thunderous sound, and singing that was more like screaming. This is the sound on the surface. The sound was also simplistic because, according to Cohen (2009), the harmonies were repetitive, the instrumental arrangements dogmatic, the rhythmic patterns were fixed, and the songs utilized basic strophic forms. Because of the seemingly simple level of the music, Cohen (2009) states that rock critics have been loathe to study the band, as they have been unable to find the complexity hidden beneath the simplistic surface of the band’s songs on Nevermind. That said, Cohen (2009) states that Nirvana did have complexity, in that they were unique and innovative, with harmonic idioms that hearkened back to traditional rock pa tterns with new harmonies. The trademarks of Nirvana, and its closely related cousins – Alice in Chains, Soundgarden and Pearl Jam – are marked by use of the Phygian mode (minor mode with a lowered second degree), power chords, basic harmonies that blur the definition between minor and major keys, cross relations between sequential chords, and melodies and harmony that clash chromatically. Cohen (2009) chose In Bloom for special analysis, as it has a wider harmonic palette

How can time management increase efficiency Essay

How can time management increase efficiency - Essay Example Periodic events and periodic motion have been used as standard for units of time. The motion of sun across the sky, the phases of the moon, and the swing of a pendulum are all examples of such events. The unit of time interval is defined as a certain number of hyperfine transitions in Cesium atoms. Time is a prime motivation in astronomy. Time has economic value as people value time in terms of money. Time has social significance and time influences decisions in everyday life. Time has personal value as people are aware of the limited time that each one has at disposal in a human lifetime. A question that often arises in the mind is how does time flow? Is time understood only by those who have time? Is there no passage of time for beings that do not have mind? Can time be defined relative to the instrument that measures time? We perceive time as flowing in smooth and perpetual continuous motion. The passage of time appears to us humans as a flow. Can this flow change? Everything moves all the time. It was initially measured by the movements of the sun and the moon. The passage of time is measured by hours, days, weeks, months and years. According to physics, time is measured based on the revolutions and rotations of the planets or the heavenly bodies (Heller, 2006). The current time measurement can be dated back to the Sumerian civilization of approximately 2000 B.C. This is known as the Sumerian Sexagesimal System based on the number 60. There are sixty seconds in a minute and sixty minutes in an hour. Number twelve also has importance in the definition of time as there are twelve hours of the day and twelve hours of night. There are twelve months in a year. The passage of time is supposed to change us in significant ways. Human have been measuring time since the beginning of civilizations all over the world. In ancient days time was measured with the help of sundials placed above the doorways which could identify the mid-day

Bast fibers and glass fibers Coursework Example | Topics and Well Written Essays - 750 words - 1

Bast fibers and glass fibers - Coursework Example Data from various literature sources was compiled and used to a Life Cycle Inventory for the production of flax fibres. Three scenario were studied for the production of different fibers including natural bast fiber flax, glass fiber and china reed. The best method for agriculture practice was identified for the fibre production from the research. It is found out that flax fibre environmental characteristics can be enhanced with the use of biological control pests and organic fertilizers. Also, another most energy intensive fiber processing operation is spinning. This eliminates the energy use and eliminated associated environmental impacts. According to the energy analysis carried out, the reinforcement of glass fiber was found to be more effective in flax yarn. Similar amounts of Flax siver and glass fiber have same amount of energies quantities. The format chosen for reinforcement determines the environmental benefit arising from substitution of glass fibres by natural fibre. The most important factor to consider is the use of spun fibers as effective reinforcements in polymer matrix composites. In various engineering applications it is often the case that a given homogenous core material is reinforced using another material that is stiffer and also stronger to achieve required mechanical and material property. Usually the constituent material used for the reinforcement is fibrous. From a broad perspective fiber materials used for material composite reinforcement can be natural or synthetic.

Research on Financial Statement Restatement and Ethics Paper

On Financial Statement Restatement and Ethics - Research Paper Example The company restated its position on august 1, 2012 stating that the financial records were not fit for their purpose in compliance with general accounting and reporting standards (Cubic Corporation, 2008). One of the impending reasons that can lead to the restatement as witnessed by the recent restatement is correcting errors. It forms one of the most common reasons for financial restatement (Cubic Corporation, 2008). It happens after the release of the financial statements. Consequently, errors are found by the company or the auditor. If the error margin is observed as material to financial statements, the statements are to be corrected and re-issued to the users. Materiality is determined if the statements will lead to the users coming to incorrect conclusions in their analysis (Taub, 2006). Another reason is the changes in GAAP. If a change in the current accounting methods brings about a change in the prior year’s statements in the case of retroactive application, the statements are required to be restated (Taub, 2005). As such, it ensures that no statement is changed over the course arises from a change in the accounting policy in use. For instance, the company chose to switch from a first-in-first-out inventory costing policy to a last-in-first-out. Consequently, the statements in the previous period must be restated in order to follow the new policy. Changes in reporting entity also cause restatement of financial statements (Lee et al., 2006). In the case of a company transition from one set of ownership to a new one or the structure of ownership change in the current year and the change has an impact in the current financial statements reporting or disclosures, the prior period comparative statements must be restated. The restatement is made to facilitate a smooth transition such that the implication will be that the change occurred in the beginning of the current year (Plumlee & Yohn, 2008b). Restatement brings about

Summarize the article, Essay Example | Topics and Well Written Essays - 250 words

Summarize the article, - Essay Example The time for the study was four weeks, which was the estimated duration for each treatment application to the sample population. The research on phase III trial of patients contributed in gaining additional evidence that patients with bone metastases and breast cancer would be treated effectively with denosumab than with zoledronic acid. Such aids in maintaining quality of life, preventing hypercalcemia of malignancy and delaying bone radiation, as well as SREs (Martin, Bell and Bourgeois et al., 2012). Denosumab treatment also aids in minimizing renal toxicity risks, all forms of acute-phase reactions and establishing a convenience of the subcutaneous administration. These outcomes of denosumab are also evidenced in preventing skeletal based complications among patients that have bone metastases and breast cancer. As such, the study was able to identify the effective treatment, which breast cancer patients should be given for better

Soap opera Essay Example for Free

Soap opera Essay Every soap uses lots of different camera shots and angles. First of all there is the reverse shot, this is used when two people are talking, tends to be head and shoulders shot. The second one is the medium shot; this is usually head, shoulders and half of a body on the shot. This is normally used when three people are talking, example, at the bar in the Rovers Return. The third shot is the tracking shot, this is used when the characters are moving, i. e. walking or running. This is usually a full body shot. It can be of more than one person. This was used on the live 40th anniversary episode when it first started. The fourth shot is the zoom, this is when the camera gets closer or further away from a character, e. g. when Sarah-Lou found out that she was pregnant. The fifth shot is called a pan; this is to ensure a long broad view of a whole area. It can also move side to side. This was used in the very first episode of Coronation Street. When Kens dad passed his wife a cup of tea, it didnt just switch from one person to another. The fifth shot is called the tilt, this is when the camera tilts up and down. In the 40th year episode of Coronation Street they did a live episode. The two main narrative strands were, Whether they were going to save the cobbles, and Whether Vera was going to survive. I think they used these storylines because the producers wanted the whole of the cast to appear in the episode. Some of the speeches had double meanings. When Ken said, Long live coronation street, he is talking about the street but I know that he wants the programme to go on for another forty years. Also when the whole cast is singing, We shall not, we shall not be moved, they are trying to save the cobbles but also they want to stay in Coronation street as there characters. Ken Barlows son came and we know this is going to be the start of a new story line. Then Vera wakes, this is a storyline resolved. When Curly went into the pub to tell all the characters that Vera had woken up, they all cheered, this shows that Vera is a well-respected character. At the end of the episode, when Ken says, We did it, he means that they have saved the cobbles, but also he means that they had finished the episode with success. The actors seemed genuinely happy, not acting. For Australian soap operas, theme tunes are accompanied by lyrics. They have unrealistic storylines. Neighbours and Home and Away have lots of teenagers who do not seem to have parents. For British soap operas the theme tune is easily recognisable type of music. This lets you know when the programme is on if you are in another room. The brass band type of music lets you know it is northern. Piano and drums let you know that it is cockney, i. e. Eastenders. In every soap, which has, adverts every time before a break it has a question. E. g. when the break came on, on the live episode the question before the break was whether Vera was going to survive or not. Advertisers like to put their adverts on during prime time television i. e. coronation street because they know that millions of people will be watching the programme. In coronation street the producers and directors deliberately uses the northern dialect. This makes it easily identifiable to a Lancashire place. Standard English is a way of speaking without using a regional accent or dialect. The reason we have Standard English is to make sure every country understands each other. I have been looking at a 1995 coronation street script, and the differences in the language of the script compared with Standard English are completely different. The script is written in everyday speech for it to show that it is typical northern town talk. The unusual thing about the script is that it uses typical northern accent and dialect. At the start of the script Vera says, I could do with some crudities. Then Jack says, its a Christening not a flaming stag night. Now Jack and Vera had a different meaning to the word crudities. Vera means raw vegetables but Jack thought she meant things like strippers. This shows that Jack is common because he misunderstood the word crudities. Also when Vera says, who were that on phone? this shows a strong, northern phrase. This phrase is dramatically incorrect. In the 1940s films they used strong, sharp, clip Standard English. The queen, how she speaks now was old Standard English. Zoi Ball says, Rilly Gid instead of, Really Good. This is Received Pronunciation and this is starting to filter up throughout the country. Despite soap operas being popular because it is centred on women, there is some evidence to show more, that soaps are focussing more frequently on storylines outside the domestic plots such as crime. Additionally the storylines have recently focused on male characters as in Eastenders, Ian Beale fought to get his children back and in Coronation Street Jim McDonald had to face the consequences when his son got in a lot of trouble with drug dealer, Jed Quigley. Finally, other possible reasons for soap operas popularity could be escapism or relaxation. At the end of the day the viewer can sit down, relax and watch an episode of Coronation Street, and escape from the problems of reality, and think about characters problems instead. Most significantly though, soap operas concern with the everyday people and their problems, big and small, appears to be one of the main reasons why this genre is so popular.

A Study On Business Forecasting Statistics Essay

A Study On Business Forecasting Statistics Essay The aim of this report is to show my understanding of business forecasting using data which was drawn from the UK national statistics. It is a quarterly series of total consumer credit gross lending in the UK from the second quarter 1993 to the second quarter 2009. The report answers four key questions that are relevant to the coursework. In this section the data will be examined, looking for seasonal effects, trends and cycles. Each time period represents a single piece of data, which must be split into trend-cycle and seasonal effect. The line graph in Figure 1 identifies a clear upward trend-cycle, which must be removed so that the seasonal effect can be predicted. Figure 1 displays long-term credit lending in the UK, which has recently been hit by an economic crisis. Figure 2 also proves there is evidence of a trend because the ACF values do not come down to zero. Even though the trend is clear in Figure 1 and 2 the seasonal pattern is not. Therefore, it is important the trend-cycle is removed so the seasonal effect can be estimated clearly. Using a process called differencing will remove the trend whilst keeping the pattern. Drawing scattering plots and calculating correlation coefficients on the differenced data will reveal the pattern repeat. Scatter Plot correlation The following diagram (Figure 3) represents the correlation between the original credit lending data and four lags (quarters). A strong correlation is represented by is showed by a straight-line relationship. As depicted in Figure 3, the scatter plot diagrams show that the credit lending data against lag 4 represents the best straight line. Even though the last diagram represents the straightest line, the seasonal pattern is still unclear. Therefore differencing must be used to resolve this issue. Differencing Differencing is used to remove a trend-cycle component. Figure 4 results display an ACF graph, which indicates a four-point pattern repeat. Moreover, figure 5 shows a line graph of the first difference. The graph displays a four-point repeat but the trend is still clearly apparent. To remove the trend completely the data must differenced a second time. First differencing is a useful tool for removing non-stationary. However, first differencing does not always eliminate non-stationary and the data may have to be differenced a second time. In practice, it is not essential to go beyond second differencing, because real data generally involve non-stationary of only the first or second level. Figure 6 and 7 displays the second difference data. Figure 6 displays an ACF graph of the second difference, which reinforces the idea of a four-point repeat. Suffice to say, figure 7 proves the trend-cycle component has been completely removed and that there is in fact a four-point pattern repeat. Question 2 Multiple regression involves fitting a linear expression by minimising the sum of squared deviations between the sample data and the fitted model. There are several models that regression can fit. Multiple regression can be implemented using linear and nonlinear regression. The following section explains multiple regression using dummy variables. Dummy variables are used in a multiple regression to fit trends and pattern repeats in a holistic way. As the credit lending data is now seasonal, a common method used to handle the seasonality in a regression framework is to use dummy variables. The following section will include dummy variables to indicate the quarters, which will be used to indicate if there are any quarterly influences on sales. The three new variables can be defined: Q1 = first quarter Q2 = second quarter Q3 = third quarter Trend and seasonal models using model variables The following equations are used by SPSS to create different outputs. Each model is judged in terms of its adjusted R2. Linear trend + seasonal model Data = a + c time + b1 x Q1 + b2 x Q2 + b3 x Q3 + error Quadratic trend + seasonal model Data = a + c time + b1 x Q1 + b2 x Q2 + b3 x Q3 + error Cubic trend + seasonal model Data = a + c time + b1 x Q1 + b2 x Q2 + b3 x Q3 + error Initially, data and time columns were inputted that displayed the trends. Moreover, the sales data was regressed against time and the dummy variables. Due to multi-collinearity (i.e. at least one of the variables being completely determined by the others) there was no need for all four variables, just Q1, Q2 and Q3. Linear regression Linear regression is used to define a line that comes closest to the original credit lending data. Moreover, linear regression finds values for the slope and intercept that find the line that minimizes the sum of the square of the vertical distances between the points and the lines. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .971a .943 .939 3236.90933 Figure 8. SPSS output displaying the adjusted coefficient of determination R squared Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 17115.816 1149.166 14.894 .000 time 767.068 26.084 .972 29.408 .000 Q1 -1627.354 1223.715 -.054 -1.330 .189 Q2 -838.519 1202.873 -.028 -.697 .489 Q3 163.782 1223.715 .005 .134 .894 Figure 9 The adjusted coefficient of determination R squared is 0.939, which is an excellent fit (Figure 8). The coefficient of variable ‘time, 767.068, is positive, indicating an upward trend. All the coefficients are not significant at the 5% level (0.05). Hence, variables must be removed. Initially, Q3 is removed because it is the least significant variable (Figure 9). Once Q3 is removed it is still apparent Q2 is the least significant value. Although Q3 and Q2 is removed, Q1 is still not significant. All the quarterly variables must be removed, therefore, leaving time as the only variable, which is significant. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 16582.815 866.879 19.129 .000 time 765.443 26.000 .970 29.440 .000 Figure 10 The following table (Table 1) analyses the original forecast against the holdback data using data in Figure 10. The following equation is used to calculate the predicted values. Predictedvalues = 16582.815+765.443*time Original Data Predicted Values 50878.00 60978.51 52199.00 61743.95 50261.00 62509.40 49615.00 63274.84 47995.00 64040.28 45273.00 64805.72 42836.00 65571.17 43321.00 66336.61 Table 1 Suffice to say, this model is ineffective at predicting future values. As the original holdback data decreases for each quarter, the predicted values increase during time, showing no significant correlation. Non-Linear regression Non-linear regression aims to find a relationship between a response variable and one or more explanatory variables in a non-linear fashion. (Quadratic) Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate 1 .986a .972 .969 2305.35222 Figure 11 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 11840.996 1099.980 10.765 .000 time 1293.642 75.681 1.639 17.093 .000 time2 -9.079 1.265 -.688 -7.177 .000 Q1 -1618.275 871.540 -.054 -1.857 .069 Q2 -487.470 858.091 -.017 -.568 .572 Q3 172.861 871.540 .006 .198 .844 Figure 12 The quadratic non-linear adjusted coefficient of determination R squared is 0.972 (Figure 11), which is a slight improvement on the linear coefficient (Figure 8). The coefficient of variable ‘time, 1293.642, is positive, indicating an upward trend, whereas, ‘time2, is -9.079, which is negative. Overall, the positive and negative values indicate a curve in the trend. All the coefficients are not significant at the 5% level. Hence, variables must also be removed. Initially, Q3 is removed because it is the least significant variable (Figure 9). Once Q3 is removed it is still apparent Q2 is the least significant value. Once Q2 and Q3 have been removed it is obvious Q1 is under the 5% level, meaning it is significant (Figure 13). Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 11698.512 946.957 12.354 .000 time 1297.080 74.568 1.643 17.395 .000 time2 -9.143 1.246 -.693 -7.338 .000 Q1 -1504.980 700.832 -.050 -2.147 .036 Figure 13 Table 2 displays analysis of the original forecast against the holdback data using data in Figure 13. The following equation is used to calculate the predicted values: QuadPredictedvalues = 11698.512+1297.080*time+(-9.143)*time2+(-1504.980)*Q1 Original Data Predicted Values 50878.00 56172.10 52199.00 56399.45 50261.00 55103.53 49615.00 56799.29 47995.00 56971.78 45273.00 57125.98 42836.00 55756.92 43321.00 57379.54 Table 2 Compared to Table 1, Table 2 presents predicted data values that are closer in range, but are not accurate enough. Non-Linear model (Cubic) Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate 1 .997a .993 .992 1151.70013 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 17430.277 710.197 24.543 .000 time 186.531 96.802 .236 1.927 .060 time2 38.217 3.859 2.897 9.903 .000 time3 -.544 .044 -2.257 -12.424 .000 Q1 -1458.158 435.592 -.048 -3.348 .002 Q2 -487.470 428.682 -.017 -1.137 .261 Q3 12.745 435.592 .000 .029 .977 Figure 15 The adjusted coefficient of determination R squared is 0.992, which is the best fit (Figure 14). The coefficient of variable ‘time, 186.531, and time2, 38.217, is positive, indicating an upward trend. The coefficient of ‘time3 is -.544, which indicates a curve in trend. All the coefficients are not significant at the 5% level. Hence, variables must be removed. Initially, Q3 is removed because it is the least significant variable (Figure 15). Once Q3 is removed it is still apparent Q2 is the least significant value. Once Q3 and Q2 have been removed Q1 is now significant but the ‘time variable is not so it must also be removed. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 18354.735 327.059 56.120 .000 time2 45.502 .956 3.449 47.572 .000 time3 -.623 .017 -2.586 -35.661 .000 Q1 -1253.682 362.939 -.042 -3.454 .001 Figure 16 Table 3 displays analysis of the original forecast against the holdback data using data in Figure 16. The following equation is used to calculate the predicted values: CubPredictedvalues = 18354.735+45.502*time2+(-.623)*time3+(-1253.682)*Q1 Original Data Predicted Values 50878.00 49868.69 52199.00 48796.08 50261.00 46340.25 49615.00 46258.51 47995.00 44786.08 45273.00 43172.89 42836.00 40161.53 43321.00 39509.31 Table 3 Suffice to say, the cubic model displays the most accurate predicted values compared to the linear and quadratic models. Table 3 shows that the original data and predicted values gradually decrease. Question 3 Box Jenkins is used to find a suitable formula so that the residuals are as small as possible and exhibit no pattern. The model is built only involving a few steps, which may be repeated as necessary, resulting with a specific formula that replicates the patterns in the series as closely as possible and also produces accurate forecasts. The following section will show a combination of decomposition and Box-Jenkins ARIMA approaches. For each of the original variables analysed by the procedure, the Seasonal Decomposition procedure creates four new variables for the modelling data: SAF: Seasonal factors SAS: Seasonally adjusted series, i.e. de-seasonalised data, representing the original series with seasonal variations removed. STC: Smoothed trend-cycle component, which is smoothed version of the seasonally adjusted series that shows both trend and cyclic components. ERR: The residual component of the series for a particular observation Autoregressive (AR) models can be effectively coupled with moving average (MA) models to form a general and useful class of time series models called autoregressive moving average (ARMA) models,. However, they can only be used when the data is stationary. This class of models can be extended to non-stationary series by allowing differencing of the data series. These are called autoregressive integrated moving average (ARIMA) models. The variable SAS will be used in the ARIMA models because the original credit lending data is de-seasonalised. As the data in Figure 19 is de-seasonalised it is important the trend is removed, which results in seasonalised data. Therefore, as mentioned before, the data must be differenced to remove the trend and create a stationary model. Model Statistics Model Number of Predictors Model Fit statistics Ljung-Box Q(18) Number of Outliers Stationary R-squared Normalized BIC Statistics DF Sig. Seasonal adjusted series for creditlending from SEASON, MOD_2, MUL EQU 4-Model_1 0 .485 14.040 18.693 15 .228 0 Model Statistics Model Number of Predictors Model Fit statistics Ljung-Box Q(18) Number of Outliers Stationary R-squared Normalized BIC Statistics DF Sig. Seasonal adjusted series for creditlending from SEASON, MOD_2, MUL EQU 4-Model_1 0 .476 13.872 16.572 17 .484 0 ARMA (3,2,0) Original Data Predicted Values 50878.00 50335.29843 52199.00 50252.00595 50261.00 50310.44277 49615.00 49629.75233 47995.00