Plagiarism Definition and Effects

One definition of plagiarism as offered by one web article quotes â€Å"plagiarize means to steal and pass off either ideas or words of another as one’s own. It is the use of another’s production without crediting the source and the committing of literal theft.† (What Is Plagiarism?).Advertising We will write a custom essay sample on Plagiarism Definition and Effects specifically for you for only $16.05 $11/page Learn More Many individuals view the topic of plagiarism as basic ‘copying and pasting’, which masks the reality of such a serious and prosecutable offense. Considering the nature of how such an act is committed, it has common occurrences; a vast amount being in schools, the music industry and even the workplace. There has been a rapid increase in the amount of plagiarism cases reported in schools recently, which is believed to have doubled compared to two years ago. The issue of cheating has caused a majority of schools to start utilizing plagiarism-detection software to catch up with students. (Williams). Despite having such software, some very tech savvy students still manage to evade detection. The occurrence of plagiarism in schools has significant effects on Education which may include; loss of a degree or job and poor critical thinking skills. Once plagiarized work is passed on as original, the student stands a great chance to lose their degrees and in addition to that, they develop a poor ability to engage in critical thinking which is important in being an individual. (Hall) Plagiarism in the workplace or professional plagiarism is common mainly in industries like marketing or any other that involves drawing, writing, taking pictures or just creative thinking. An example of such an incident took place at the Researcher’s place of work. The Researcher once explained a method of solution to her boss; her boss then relayed the information to her immediate superior and took cred it for it. In other words, this incident adequately defines the topic of plagiarism. Plagiarism in Music probably has the second highest number of occurrences behind Education. There have been numerous reports of celeb singers who have been accused of and prosecuted for plagiarism in their music. Lady Gaga who is a very popular pop singer was recently accused for plagiarism in her new hit single ‘Judas’ by singer/songwriter Rebecca Francesscatti. Francesscatti claims Gaga copied portions from her music and is now seeking recognition for her creation and an undisclosed amount in damages. (Lund) Whether intentional or unintentional, ‘Plagiarism’ is a prosecutable crime which occurs often in varying environments. As the saying goes â€Å"prevention is better than cure†, it is better to try and prevent committing such an act than to deal with the consequences. There are a number of measures one may take to prevent plagiarism.Advertising Looking for essay on education? Let's see if we can help you! Get your first paper with 15% OFF Learn More For individuals particularly involved in education, it is imperative that you know how to paraphrase, quote and cite sources properly. Once you know how to do that, you have significantly lowered your chances of such occurrences. In addition to that, there is a vast amount of software that helps you to correctly cite your sources and check for plagiarism. Furthermore, the Researcher believes that once an individual understands the concept of plagiarism and how unethical it is, they will try harder to produce an original paper. Works Cited Hall, Shane. â€Å"Effects of Plagiarism on Education† ehow.com. n.d. Web. Lund, Anthony. â€Å"Lady Gaga Faces Judas Plagiarism Claims†. Musicrooms. 2011. Web. What Is Plagiarism? Plagiarism.org. n.d. Web. Williams, Rachel. â€Å"Internet Plagiarism Rising in Schools†. guardian.co.uk. 2010. Web. This essay on Plagiarism Definition and Effects was written and submitted by user Korath to help you with your own studies. You are free to use it for research and reference purposes in order to write your own paper; however, you must cite it accordingly. You can donate your paper here.

Sociolinguistics Definition and Examples

Sociolinguistics Definition and Examples Sociolinguistics takes language samples from sets of random population subjects and looks at variables that include such things as pronunciation, word choice, and colloquialisms. The is data is then measured against socio-economic indices such as education, income/wealth, occupation, ethnic heritage, age, and family dynamics to better understand the relationship between language and society. Thanks to its dual focus, sociolinguistics is considered a branch of both linguistics and sociology.  However, the broader study of the field may also encompass anthropological linguistics, dialectology, discourse analysis, ethnography of speaking, geolinguistics, language contact studies, secular linguistics, the social psychology of language, and the sociology of language. The Right Words for the Given Situation Sociolinguistic competence means knowing which words to choose for a given audience and situation to get the desired effect. For instance, say you wanted to get someones attention. If you were a 17-year-old boy and you spotted your friend Larry walking out to his car, youd probably utter something loud and informal along the lines of: Hey, Larry! On the other hand, if you were that same 17-year-old boy and saw the school principal drop something in the parking lot as she was walking to her car, youd more likely utter something along the lines of, Excuse me, Mrs. Phelps! You dropped your scarf. This word choice has to do with societal expectations on the part of both the speaker and the person to whom he is speaking. If the 17-year-old hollered, Hey! You dropped something! in this instance, it could be considered rude. The principal has certain expectations with regard to her status and authority. If the speaker understands and respects those societal constructs, he will choose his language accordingly to make his point and express proper deference. How Language Defines Who We Are Perhaps the most famous example of the study of sociolinguistics comes to us in the form Pygmalion, the play by Irish playwright and author George Bernard Shaw that went on to become the basis for the musical My Fair Lady. The story opens outside Londons Covent Garden market, where the upper crust post-theater crowd is attempting to stay out of the rain. Among the group are Mrs. Eynsford, her son, and daughter, Colonel Pickering (a well-bred gentleman), and a Cockney flower girl, Eliza Doolittle (a.k.a Liza). In the shadows, a mysterious man is taking notes. When Eliza catches him writing down everything she says, she thinks he’s a policeman and loudly protests that she hasn’t done anything. The mystery man isn’t a cop- he’s a professor of linguistics, Henry Higgins. Coincidentally, Pickering is also a linguist. Higgins boasts that he could turn Eliza into a duchess or the verbal equivalent in six months, with no idea that Eliza has overheard him and is actually going to take him up on it. When Pickering bets Higgins he can’t succeed, a wager is made and the bet is on. Over the course of the play, Higgins does indeed transform Eliza from guttersnipe to grand dame, culminating with her presentation to the queen at a royal ball. Along the way, however, Eliza must modify not only her pronunciation but her choice of words and subject matter. In a wonderful third-act scene, Higgins brings his protà ©gà © out for a test run. She’s taken to tea at the home of Higgins very proper mother with strict orders: â€Å"She’s to keep to two subjects: the weather and everybody’s health- Fine day and How do you do, you know- and not to let herself go on things in general. That will be safe.† Also in attendance are the Eynsford Hills. While Eliza valiantly attempts to stick to the limited subject matter, it’s clear from the following exchange that her metamorphosis is as yet incomplete: MRS. EYNSFORD HILL:  I’m sure I hope it won’t turn cold. There’s so much influenza about. It runs right through our whole family regularly every spring. LIZA: [darkly] My aunt died of influenza- so they said. MRS. EYNSFORD HILL [clicks her tongue sympathetically] LIZA: [in the same tragic tone] But it’s my belief they done the old woman in. MRS. HIGGINS: [puzzled] Done her in? LIZA: Y-e-e-e-es, Lord love you! Why should she die of influenza? She come through diphtheria right enough the year before. I saw her with my own eyes. Fairly blue with it, she was. They all thought she was dead; but my father he kept ladling gin down her throat til she came to so sudden that she bit the bowl off the spoon. MRS. EYNSFORD HILL: [startled] Dear me! LIZA: [piling up the indictment] What call would a woman with that strength in her have to die of influenza? What become of her new straw hat that should have come to me? Somebody pinched it; and what I say is, them as pinched it done her in. Written just after the close of the Edwardian Era, when class distinction in British society was steeped in centuries-old traditions strictly delineated by a set of codes that related to family status and wealth as well as occupation and personal behavior (or morality), at the heart of the play is the concept that how we speak and what we say directly defines not only who we are and where we stand in society but also what we can hope to achieve- and what we can never achieve. A lady speaks like a lady, and a flower girl speaks like a flower girl and never the twain shall meet. At the time, this distinction of speech separated the classes and made it virtually impossible for someone from the lower ranks to rise above their station. While both a shrewd social commentary and an amusing comedy in its day, assumptions made on the basis of these linguistic precepts had a very real impact on every aspect daily life- economic and social- from what job you could take, to whom you could or could not marry. Such things matter much less today of course, however, it is still possible for some sociolinguistic experts to pinpoint who you are and where you come from by the way you speak.

Thinking and Decision Making Paper Essay Example | Topics and Well Written Essays - 750 words

Thinking and Decision Making Paper - Essay Example Sound decisions are made without violating religious or cultural beliefs. This Paper will focus on the spiritually guided, the realist, and the analyst. Although most people use only one style when making a decision it is possible to train oneself to use other styles in different situations. The Spiritually Guided individual depends on guidance from God and believes that any result is because: that's what God wants for that person. These individuals believe that anyone can receive guidance from God. When decisions have consequences the Spiritually Guided individual believes that it is the will of God. In addition, these individuals are deeply committed and are confident in their ability to makes decisions based upon their spiritual beliefs. These individuals are hard to sway to another point of view because their beliefs/values are held deeply in the core of the individual. The Spiritually Guided also believes that they can change other's lives for the better by sharing their beliefs with others. The Realist believes that facts are facts2. They make decisions based upon the facts available. They also make decisions based upon observation and personal experiences. The realist is often perceived as hard headed or stubborn. They rarely change their opinion based on another's input. And, the Realist has a "let's get going" point of view and is aggravated when a project is delayed. The Analyst is the thoughtful one who takes his/her time making a decision. They have a "let's take a look at the situation" point of view. Their results are predictable because of the thought and information gathering put into the solution. The analyst is the information gatherer who seeks out as much information as possible to make a logical decision. This type of decision maker takes a great sense of pride in the results of his decision making and views his way as the best way. These three types of decision makers would be a nightmare in a boardroom meeting where an actual decision had to be made. The Realist would already have the decision made, the Spiritualist would be evaluating the situation based upon his beliefs, and the Analyst would be asking for more information so that they carefully plan their decision. The realist would be arguing that he has done this many times and that his approach will work. The Spiritualist would be looking for spiritual guidance (praying) and would act as mentor or arbiter in the meeting. The meeting would end with the Analyst asking for a follow-up meeting to allow time for information gathering and analysis. 2. It is easy to imagine different scenarios and what would happen if a different type of decision maker were in charge. In the corporate world the best way to approach problem solving at the corporate level is to have as many different style decision makers present. Remember that boardroom There is one person that sits and listens to all parties and guides the group through the decision making process. That person is most likely the chairman of the board. He spends most of his time listening and writing questions down on his tablet. Another member of this team is the scribe/note-taker who writes

Discuss the rationale for Pfizers bid,and explain why Kraft-Cadbury Essay

Discuss the rationale for Pfizers bid,and explain why Kraft-Cadbury has influenced the governments stance on mergers and acquisitions - Essay Example Such kind of mutiny looks scary whereby the fund managers from both Axa and Jupiter will be so disappointed according to the steps taken by AstraZeneca’s board although cannot cause an alarm to an extend resulting to resignation. Mr Lelf Johansson who is the AstraZeneca chairman said that all independent directors in most cases are paid to give their opinion, in case the shareholders seem not to like such opinion; the most likely repercussion is for them to appoint new set of directors. While at Axa together with co sulk, it is clear that Mr. Johansson and his staff should be appreciated because of the wise decision they took on behalf of their company and being calm despite of the difficult times very few company Bosses can behave the way he did to succeed. It has now become clear if not impossible that AstraZeneca’s board could manage to think of any option apart from the  £55 bid if it has to maintain its credibility-at least before the set up date expires for the takeover panel. And since now Pfizer has the ability to bid even higher than  £55 under the prevailing panel’s rules, the whole drama seems to be over now (Depamphilis, 2005). There is no correct explanation as to the reason leading to  £60-ish would stand to be the right tag price. It’s all obvious that all manner of bid battles of which at the end the winner takes it all. The starting point for Pfizer was poor and not impressive, few considered its opening target of  £50 a long shot at the moment but the offer rapidly dropped to  £48 leading to the share drop down of about 6% meaning that the value of its cash together with the share proposal were affected. Under the terms it was supposed to be that the offer for each AstraZeneca shareholder to walk away with 1.845 shares as a group while  £15.98 placed in cash. According to the Pfizer closing share stood at the price worthy $31.15, this means that the offer comes to about GBP50 per each available AstraZeneca

India automobile industry Essay Example for Free

India automobile industry Essay Is this the worst period for the automobile industry that youve witnessed? The sales of petrol-fuelled cars have been declining month after month and it went unnoticed. However, diesel car sales started declining only since the last six months. Sales of petrol cars have been declining for the past two years. This is certainly the worst period, I cant think of any period in recent history thats been anything like this. The correction thats happening now is of diesel-powered car sales. If you look back at the diesel car sales, it had a very rapid growth. Beginning of 2011, growth has been very high, till the end of last year. Carmakers had expanded capacity. There is now not much attraction for diesel cars with increasing fuel prices. There has been substantial cut-back in petrol car production, but the cut-back in diesel car production was seen only from last month. Analysts are talking about a huge inventory pile-up at stockyards and dealerships. Is this one of the major reasons for plant shut downs? Shut downs are happening, because there is no point producing cars which are not selling. If I have a capacity to sell 40,000 cars and the demand today in the market is for only 30,000 cars, what do I do now? I have to scale back production. I have two ways of doing it. Either I reduce production each day by 25% or work on less days and produce 25% less. It is more economical to choose the second option, to work for a fewer number of days at maximum capacity. Because this will help reduce overheads such as electricity, transport, water charges, etc. Lot of money goes into these. What companies are doing is working for a fewer number of days but at maximum capacity on those days.

The Benefits And Challenges Of Elogistics

The Benefits And Challenges Of Elogistics The increased competition in all sectors due to globalization has forced the companies to reduce business costs. To reduce cost the companies are forming new management strategies like supply chain management and e- logistics. E- logistics means applying the concept of logistics electronically via the internet in order to conduct the business electronically. According to AMR Research( Challenger,2001), E- logistics helps to reduce cost by 10%. According to Sahay,B.S.(2003) logistics contribute to 10 to 12% of GDP . Based on two references e-logistics helps to save about 1.2% of GDP. That is why the companies like Dell, Compaq, Hewlett Packard is favourable to the E- logistics and supply chain management. E- commerce logistics are the activities that ensures that customers get what they need at right time at right place and at minimum cost. E-commerce logistics face many challenges in developing countries like higher tariffs, Complicated global trade rules, Global Terrorism and Geographical Barriers. The lack of knowledge of total cost in online merchant is the main cause for the failure of many electronic commerce in past decades. According to Hesse 2002, Gunasekaran et al 2003, E- logistics is an internet enabled logistics value chain that offer competitive logistics services like contract warehousing, public warehousing, distribution management, target consolidation and transport management.E- logistics consist of four components like one stop value added services, information management, automation in warehousing network and a transportation network. The one stop value added services helps to improve customer satisfaction .For example in government service one stop value added services like making queries and application, to search council services and to make payment can be done all at one time. Information management is where the information is exchanged through electronic media`s like WWW, Internet and EDI. Automation in warehousing operation will reduce human involvement in updating consolidation, loading and unloading. A Transportation network helps to increase flexibility and reduce transportation costs. The difference between traditional logistics and e- logistics are as follows. In case of traditional logistics the volume is very low because large amounts of goods are sent to lesser location like retail stores. But in case of e- logistics the lesser amount of materials are sent to many customers quickly. In case of traditional logistics the objective is that it is efficient and cost effective but in case of e- logistics it is more speed and can meet customer expectation.In case of traditional logistics the information is gathered through fax, paperwork and Management Information System(MIS) but in case of e- logistics the information is gathered through Internet, Electronic Data Interchange (EDI), Radio Frequency Identification (RFID) and Integrated IS. The E- logistics is more reliable and fast than traditional logistics. In Traditional logistics accountability of shipments is linked to limited supply chain but in e- logistics the accountability is expanded in whole supply chain. In E- logistics the customers have high expectation about quality of service and delivery of shipments but in case of traditional logistics the delivery of products is taking long time as the expectation of customer is not met because today`s customer needs faster delivery of goods. In case of traditional logistics there is less complexity in fulfilling international trade but in case of e- logistics there is larger complexity in fulfilling international trade. In e- logistics it is possible to place direct orders with distributors and producers and also helps in accessing more sellers globally but in case of traditional logistics it is mainly done through fax and paper works as it takes expensive and takes long time in getting reply from producers and distributors. In case of E- logistics the demand for shipment is lacking s tability and also not predictable due to huge number of customers but in case of traditional logistics the demand for shipment is predictable . The challenges of e- logistics in developing countries are economic and educational barriers, lack of infrastructure, security, trust and privacy, channel conflicts, delivery logistics, security problems and global terrorism and geographical barriers. In economic and educational barriers the main obstacles for e- logistics is the lack of economic resources, internet usage and standard of education. Most of e- logistics are used by advanced users in developing countries because of lack of resources to use the internet and shop online. Lack of infrastructure means developing countries poor telecommunication infrastructure and short access of computers create barrier in e- logistics .Internet access cost also result in barrier to e- logistics. Security,privacy and trust is different in different countries and there is no provision in many countries govt rules that e- logistics should be legally binding and trust worthy as such it create barriers in E- logistics. According to survey conducted by Forrester Research said that they turned off global trade because of difficulty in process in e- logistics. The main barrier for e- logistics for this is the language and cultural barriers that destroys the different stages of internet infrastructure and adoption and also incapable to deal with direct global orders. According to Leung et al 2000, Gunasekaran and Ngai (2004b), the benefits of e- logistics are enhancing customer service, minimizing cost and also meeting product delivery deadlines. It helps to develop web based inventory control and also helps in building relationship with large logistics companies like DHL,UPS and FedEx. It help to concentrate and understand the customers more readily. It helps in B2B2C à ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬commerce for third part logistics (3PL) , it helps to SME`S to develop strategic alliance and also help to meet growing demand. In conclusion due to globalisation the importance of e- logistics is growing eventhough there are many challenges in e- logistics the significance of e- logistics cannot be ignored. In order to use e- logistics effectively we should understand the strength and weakness of e- logistics effectively and we should use in a proper way and should not misuse it. Last but not least in the coming future the importance of e- logistics will be higher and it will be used by many people in the future.

Simple Linear Regression

Simple linear regression is the statistic method used to make summary of and provide the association between variables that are continues and quantitative ,basically it deals with two measures that describes how strong the linear relationship we can compute in data .Simple linear regression consist of one variable known as the predictor variable and the other variable denote y known as response variable . It is expected that when we talk of simple linear regression to touch on deterministic relationship and statistical relationship, the concept of least mean square .the interpretation of the b0 and b1 that they are used to interpret the estimate regression . There is also what is known as the population regression line and the estimate regression line . This linearity is measured using the correlation coefficient (r), that can be -1,0,1.The strength of the association is determined from the value of r .( https://onlinecourses.science.psu.edu/stat501/node/250). History of simple linear regression Karl Pearson established a demanding treatment of Applied statistical measure known as Pearson Product Moment Correlation . This come from the thought of Sir Francis Galton ,who had the idea of the modern notions of correlation and regression ,Sir Galton contributed in science of Biology ,psychology and Applied statistics . It was seen that Sir Galton is fascinated with genetics and heredity provided the initial inspiration that led to regression and Pearson Product Moment Correlation . The thought that encouraged the advance of the Pearson Product Moment Correlation began with vexing problem of heredity to understand how closely features of generation of living things exhibited in the next generation. Sir Galton took the approach of using the sweet pea to check the characteristic similarities. ( Bravais, A. (1846). The use of sweet pea was motivated by the fact that it is self- fertilize ,daughter plants shows differences in genetics from mother with-out the use of the second parent that will lead to statistical problem of assessing the genetic combination for both parents .The first insight came about regression came from two dimensional diagram plotting the size independent being the mother peas and the dependent being the daughter peas. He used this representation of data to show what statisticians call it regression today ,from his plot he realised that the median weight of daughter seeds from a particular size of mother seed approximately described a straight line with positive slope less than 1. â€Å"Thus he naturally reached a straight regression line ,and the constant variability for all arrays of character for a given character of second .It was ,perhaps best for the progress of the correlational calculus that this simple special case should promulgated first .It so simply grabbed by the beginner (Pearson 1930,p.5). Then it was later generalised to more complex way that is called the multiple regression. Galton, F. (1894),Importance of linear regressionStatistics usually uses the term linear regression in interpretation of data association of a particular survey, research and experiment .The linear relationship is used in modelling .The modelling of one explanatory variable x and response variable y will require the use of simple linear regression approach . The simple linear regression is said to be broadly useful in methodology and the practical application. This method on simple linear regression model is not used in statistics only but it is applied in many biological, social science and environmental research. The simple linear regression is worth importance because it gives indication of what is to be expected, mostly in monitoring and amendable purposes involved on some disciplines(April 20, 2011 , plaza ,). Description of linear regression The simple linear regression model is described by Y=(?0 + ?1 +E), this is the mathematical way of showing the simple linear regression with labelled x and y .This equation gives us a clear idea on how x is associated to y, there is also an error term shown by E. The term E is used to justification for inconsistency in y, that we can be able to detect it by the use of linear regression to give us the amount of association of the two variables x and y . Then we have the parameters that are use to represent the population (?0 + ?1x) .We then have the model given by E(y)= (?0 + ?1x), the ?0 being the intercept and ?1 being the slope of y ,the mean of y at the x values is E(y) . The hypothesis is assumed is we assume that there is a linear association between the two variables ,that being our H0 and H1 we assume that there is no linear relationship between H0 and H1. Background of simple linear regression Galton used descriptive statistics in order for him to be able to generalise his work of different heredity problems . The needed opportunity to conclude the process of analysing these data, he realised that if the degree of association between variables was held constant,then the slope of the regression line could be described if variability of the two measure were known . Galton assumed he estimated a single heredity constant that was generalised to multiple inherited characteristics . He was wondering why, if such a constant existed ,the observed slopes in the plot of parent child varied too much over these characteristics .He realise variation in variability amongst the generations, he attained at the idea that the variation in regression slope he obtained were solely due to variation in variability between the various set of measurements . In resent terms ,the principal this principal can be illustrated by assuming a constant correlation coefficient but varying the standard deviations of the two variables involved . On his plot he found out that the correlation in each data set. He then observe three data sets ,on data set one he realised that the standard deviation of Y is the same as that of X , on data set two standard deviation of Y is less than that of X ,third data set standard deviation of Y is great than that of X . The correlation remain constant for three sets of data even though the slope of the line changes as an outcome of the differences in variability between the two variables.The rudimentary regression equation y=r(Sy / Sx)x to describe the relationship between his paired variables .He the used an estimated value of r , because he had no knowledge of calculating it The (Sy /Sx) expression was a correction factor that helped to adjust the slope according to the variability of measures . He also realised that the ratio of variability of the two measures was the key factor in determining the slope of the regression line .The uses of simple linear regression Simple linear regression is a typical Statistical Data Analysis strategy. It is utilized to decide the degree to which there is a direct connection between a needy variable and at least one free factors. (e.g. 0-100 test score) and the free variable(s) can be estimated on either an all out (e.g. male versus female) or consistent estimation scale. There are a few different suppositions that the information must full fill keeping in mind the end goal to meet all requirements for simple linear regression. Basic linear regression is like connection in that the reason for existing is to scale to what degree there is a direct connection between two factors. The real contrast between the two is that relationship sees no difference amongst the two variables . Specifically, the reason for simple linear regression â€Å"anticipate† the estimation of the reliant variable in light of the estimations of at least one free factors. https://www.statisticallysignificantconsulting.com/RegressionAnalysis.htm ReferenceBravais, A. (1846), â€Å"Analyse Mathematique sur les Probabilites des Erreurs de Situation d'un Point,† Memoires par divers Savans, 9, 255-332.Duke, J. D. (1978),â€Å"Tables to Help Students Grasp Size Differences in Simple Correlations,† Teaching of Psychology, 5, 219-221.FitzPatrick, P. J. (1960),â€Å"Leading British Statisticians of the Nineteenth Century,† Journal of the American Statistical Association, 55, 38-70.Galton, F. (1894),Natural Inheritance (5th ed.), New York: Macmillan and Company.https://onlinecourses.science.psu.edu/stat501/node/250.https://www.statisticallysignificantconsulting.com/RegressionAnalysis.htmGhiselli, E. E. (1981),Measurement Theory for the Behavioral Sciences, San Francisco: W. H. Freeman.Goldstein, M. D., and Strube, M. J. (1995), â€Å"Understanding Correlations: Two Computer Exercises,† Teaching of Psychology, 22, 205-206.Karylowski, J. (1985),â€Å"Regression Toward the Mean Effect: No Statistical Backgrou nd Required,† Teaching of Psychology, 12, 229-230.Paul, D. B. (1995),Controlling Human Heredity, 1865 to the Present, Atlantic Highlands, N.J.: Humanities Press.Pearson, E. S. (1938),Mathematical Statistics and Data Analysis (2nd ed.), Belmont, CA: Duxbury.Pearson, K. (1896),â€Å"Mathematical Contributions to the Theory of Evolution. III. Regression, Heredity and Panmixia,† Philosophical Transactions of the Royal Society of London, 187, 253-318.Pearson, K. (1922),Francis Galton: A Centenary Appreciation, Cambridge University Press.Pearson, K. (1930),The Life, Letters and Labors of Francis Galton, Cambridge University Press.Williams, R. H. (1975), â€Å"A New Method for Teaching Multiple Regression to Behavioral Science Students,† Teaching of Psychology, 2, 76-78. Simple Linear Regression Stat 326 – Introduction to Business Statistics II Review – Stat 226 Spring 2013 Stat 326 (Spring 2013) Introduction to Business Statistics II 1 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 2 / 47 Review: Inference for Regression Example: Real Estate, Tampa Palms, Florida Goal: Predict sale price of residential property based on the appraised value of the property Data: sale price and total appraised value of 92 residential properties in Tampa Palms, Florida 1000 900 Sale Price (in Thousands of Dollars) 800 700 600 500 400 300 200 100 0 0 100 200 300 400 500 600 700 800 900 1000 Appraised Value (in Thousands of Dollars)Review: Inference for Regression We can describe the relationship between x and y using a simple linear regression model of the form  µy = ? 0 + ? 1 x 1000 900 Sale Price (in Thousands of Dollars) 800 700 600 500 400 300 200 100 0 0 100 200 300 400 500 600 700 800 900 1000 Appraised Value (in Thousands of Dollars) response variable y : sale price explanatory variable x: appraised value relationship between x and y : linear strong positive We can estimate the simple linear regression model using Least Squares (LS) yielding the following LS regression line: y = 20. 94 + 1. 069x Stat 326 (Spring 2013) Introduction to Business Statistics II / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 4 / 47 Review: Inference for Regression Interpretation of estimated intercept b0 : corresponds to the predicted value of y , i. e. y , when x = 0 Review: Inference for Regression Interpretation of estimated slope b1 : corresponds to the change in y for a unit increase in x: when x increases by 1 unit y will increase by the value of b1 interpretation of b0 is not always meaningful (when x cannot take values close to or equal to zero) here b0 = 20. 94: when a property is appraised at zero value the predicted sales price is $20,940 — meaningful?!Stat 326 (Spring 2013) Introduction to Business Statistics II 5 / 47 b1 < 0: y decreases as x increases (negative association) b1 > 0: y increases as x increases (positive association) here b1 = 1. 069: when the appraised value of a property increases by 1 unit, i. e. by $1,000, the predicted sale price will increase by $1,069. Stat 326 (Spring 2013) Introduction to Business Statistics II 6 / 47 Review: Inference for Regression Measuring strength and adequacy of a linear relationship correlation coe? cient r : measure of strength of linear relationship ? 1 ? r ? 1 here: r = 0. 9723 Review: Inference for RegressionPopulation regression line Recall from Stat 226 Population regression line The regression model that we assume to hold true for the entire population is the so-called population regression line where  µy = ? 0 + ? 1 x, coe? cient of determination r 2 : amount of variation in y explained by the ? tted linear model 0 ? r2 ? 1 here: r 2 = (0. 9723)2 = 0. 9453 ? 94. 53% of the variation in the sale price can be explained through the line ar relationship between the appraised value (x) and the sale price (y ) Stat 326 (Spring 2013) Introduction to Business Statistics II 7 / 47  µy — average (mean) value of y in population for ? xed value of x ? — population intercept ? 1 — population slope The population regression line could only be obtained if we had information on all individuals in the population. Stat 326 (Spring 2013) Introduction to Business Statistics II 8 / 47 Review: Inference for Regression Based on the population regression line we can fully describe relationship between x and y up to a random error term ? y = ? 0 + ? 1 x + ? , where ? ? N (0, ? ) Review: Inference for Regression In summary, these are important notations used for SLR: Description x y Parameters ? 0 ? 1  µy ? Stat 326 (Spring 2013) Introduction to Business Statistics II 9 / 47 Stat 326 (Spring 2013)Description Estimates b0 b1 y e Description Introduction to Business Statistics II 10 / 47 Review: Inference for Regre ssion Review: Inference for Regression Validity of predictions Assuming we have a â€Å"good† model, predictions are only valid within the range of x-values used to ? t the LS regression model! Predicting outside the range of x is called extrapolation and should be avoided at all costs as predictions can become unreliable. Why ? t a LS regression model? A â€Å"good† model allows us to make predictions about the behavior of the response variable y for di? rent values of x estimate average sale price ( µy ) for a property appraised at $223,000: x = 223 : y = 20. 94 + 1. 069 ? 223 = 259. 327 ? the average sale price for a property appraised at $223,000 is estimated to be about $259,327 What is a â€Å"good† model? — answer to this question is not straight forward. We can visually check the validity of the ? tted linear model (through residual plots) as well as make use of numerical values such as r 2 . more on assessing the validity of regression model wi ll follow. 11 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 12 / 47 Stat 326 (Spring 2013)Introduction to Business Statistics II Review: Inference for Regression What to look for: Review: Inference for Regression Regression Assumptions residual plot: Assumptions SRS (independence of y -values) linear relationship between x and  µy for each value of x, population of y -values is normally distributed (? ? ? N) r2 : for each value of x, standard deviation of y -values (and of ? ) is ? In order to do inference (con? dence intervals and hypotheses tests), we need the following 4 assumptions to hold: Stat 326 (Spring 2013) Introduction to Business Statistics II 13 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 14 / 47Review: Inference for Regression †SRS Assumption† is hardest to check The †Linearity Assumption† and †Constant SD Assumption† are typically checked visually through a residual plot. Recall: residua l = y ? y = y ? (b0 + b1 x) The †Normality Assumption† is checked by assessing whether residuals are approximately normally distributed (use normal quantile plot) plot x versus residuals any pattern indicates violation Review: Inference for Regression Stat 326 (Spring 2013) Introduction to Business Statistics II 15 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 16 / 47 Review: Inference for RegressionReturning to the Tampa Palms, Florida example: 100 50 Residual 0 -50 -100 -150 0 100 200 300 400 500 600 700 800 900 1000 Review: Inference for Regression Going one step further, excluding the outlier yields 0. 2 0. 1 0. 0 -0. 1 -0. 2 -0. 3 4 4. 5 5 5. 5 log Appraised 6 6. 5 7 Residual Appraised Value (in Thousands of Dollars) Note: non-constant variance can often be stabilized by transforming x, or 0. 5 y , or both: Residual 0. 0 -0. 5 -1. 0 -1. 5 4 4. 5 5 5. 5 log Appraised 6 6. 5 7 outliers/in? uential points in general should only be excluded from an analysis if they can be explained and their exclusion can be justi? ed, e. g. ypo or invalid measurements, etc. excluding outliers always means a loss of information handle outliers with caution may want to compare analyses with and without outliers Stat 326 (Spring 2013) Introduction to Business Statistics II 17 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 18 / 47 Review: Inference for Regression normal quantile plots Tampa Palms example Residuals Sale Price (in Thousands of Dollars) 100 .01 . 05 . 10 . 25 . 50 . 75 . 90 . 95 . 99 Review: Inference for Regression Residuals log Sale 50 Regression Inference Con? dence intervals and hypotheses tests -3 -2 -1 0 1 2 3 Normal Quantile Plot -50 -100 Need to assess whether linear relationship between x and y holds true for entire population. .01 . 05 . 10 . 25 . 50 . 75 . 90 . 95 . 99 Residuals log Sale without outlier 0. 2 0. 1 0 -0. 1 -0. 2 -0. 3 -3 -2 -1 0 1 2 3 This can be accomplished through testing H0 : ? 1 = 0 vs. H0 : ? 1 = 0 based on the estimates slope b1 . For simplicity we will work with the untransformed Tampa Palms data. Normal Quantile Plot Stat 326 (Spring 2013) Introduction to Business Statistics II 19 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 20 / 47 Review: Inference for RegressionReview: Inference for Regression Example: Find 95% CI for ? 1 for the Tampa Palms data set Con? dence intervals We can construct con? dence intervals (CIs) for ? 1 and ? 0 . General form of a con? dence interval estimate  ± t ? SEestimate , where t ? is the critical value corresponding to the chosen level of con? dence C t ? is based on the t-distribution with n ? 2 degrees of freedom (df) Interpretation: Stat 326 (Spring 2013) Introduction to Business Statistics II 21 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 22 / 47 Review: Inference for Regression Review: Inference for RegressionTesting for a linear relationship between x and y If we wish to tes t whether there exists a signi? cant linear relationship between x and y , we need to test H0 : ? 1 = 0 Why? If we fail to reject the null hypothesis (i. e. stick with H0 = ? 1 = 0), the LS regression model reduces to  µy = ? 1 =0 versus Ha : ? 1 = 0 ?0 + ? 1 x ? 0 + 0  · x ? 0 (constant) Introduction to Business Statistics II 24 / 47 = = implying that  µy (and hence y ) is not linearly dependent on x. Stat 326 (Spring 2013) Introduction to Business Statistics II 23 / 47 Stat 326 (Spring 2013) Review: Inference for Regression Review: Inference for RegressionExample (Tampa Palms data set): Test at the ? = 0. 05 level of signi? cance for a linear relationship between the appraised value of a property and the sale price Stat 326 (Spring 2013) Introduction to Business Statistics II 25 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 26 / 47 Inference about Prediction Why ? t a LS regression model? The purpose of a LS regression model is to 1 Inference about Predi ction 2 estimate  µy – average/mean value of y for a given value of x, say x ? e. g. estimate average sale price  µy for all residential property in Tampa Palms appraised at x ? $223,000 predict y – an individual/single future value of the response variable y for a given value of x, say x ? e. g. predict a future sale price of an individual residential property appraised at x ? =$223,000 Keep in mind that we consider predictions for only one value of x at a time. Note, these two tasks are VERY di? erent. Carefully think about the di? erence! Stat 326 (Spring 2013) Introduction to Business Statistics II 27 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 28 / 47 Inference about Prediction To estimate  µy and to predict a single future y value for a given level of x = x ? we can use the LS regression line y = b0 + b1 x Simply substitute the desired value of x, say x ? , for x: y = b0 + b1 x ? Inference about Prediction In addition we need to know how much variability is associated with the point estimator. Taking the variability into account provides information about how good and reliable the point estimator really is. That is, which range potentially captures the true (but unknown) parameter value? Recall from 226 ? construction of con? dence intervals Stat 326 (Spring 2013) Introduction to Business Statistics II 29 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 0 / 47 Inference about Prediction Much more variability is associated with estimating a single observation than estimating an average — individual observations always vary more than averages!! Inference about Prediction Therefore we distinguish a con? dence interval for the average/mean response  µy and a prediction interval for a single future observation y Both intervals use a t ? critical value from a t-distribution with df = n ? 2. the standard error will be di? erent for each interval: While the point estimator for the average  µ y and the future individual value y are the same (namely y = b0 + b1 x ? , the of the two con? dence intervals ! Stat 326 (Spring 2013) Introduction to Business Statistics II 31 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 32 / 47 Inference about Prediction Con? dence interval for the average/mean response  µy Width of the con? dence interval is determined using the standard error SE µ (from estimating the mean response) SE µ can be obtained in JMP Keep in mind that every con? dence interval is always constructed for one speci? c given value x ? A level C con? dence interval for the average/mean response  µy , when x takes the value x? is given by y  ± t ?SE µ , where SE µ is the standard error for estimating a mean response. Stat 326 (Spring 2013) Introduction to Business Statistics II 33 / 47 Inference about Prediction Prediction interval for a single (future) value y Again, Width of the con? dence interval is determined using the standard error SE µ (from estimating the mean response) SEy can be obtained in JMP Keep in mind that every prediction interval is always constructed for one speci? c given value x ? A level C prediction interval for a single observation y , when x takes the value x ? is given by y  ± t ? SEy , where SEy is the standard error for estimating a single response.Stat 326 (Spring 2013) Introduction to Business Statistics II 34 / 47 Inference about Prediction The larger picture: Inference about Prediction The larger picture cont’d. Stat 326 (Spring 2013) Introduction to Business Statistics II 35 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 36 / 47 Inference about Prediction Example: An appliance store runs a 5-month experiment to determine the e? ect of advertising on sales revenue. There are only 5 observations. The scatterplot of the advertising expenditures versus the sales revenues is shown below: Bivariate Fit of Sales Revenues (in Dollars) By Advertising expenditur eInference about Prediction Example cont’d: JMP can draw the con? dence intervals for the mean responses as well as for the predicted values for future observations (prediction intervals). These are called con? dence bands: Bivariate Fit of Sales Revenues (in Dollars) By Advertising expenditure 5000 5000 Sales Revenues (in Dollars) 4000 3000 2000 1000 Sales Revenues (in Dollars) 4000 3000 2000 1000 0 0 0 100 200 300 400 500 600 Advertising expenditure (in Dollars) 0 100 200 300 400 500 600 Advertising expenditure (in Dollars) Linear Fit Linear Fit Sales Revenues (in Dollars) = -100 + 7 Advertising expenditure (in Dollars)Stat 326 (Spring 2013) Introduction to Business Statistics II 37 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 38 / 47 Inference about Prediction Inference about Prediction Estimation and prediction (for the appliance store data) Estimation and prediction – Using JMP For each observation in a data set we can get from JMP: y , SEy , and also SE µ . In JMP do: 1 2 We wish to estimate the mean/average revenue of the subpopulation of stores that spent x ? = 200 on advertising. Suppose that we also wish to predict the revenue in a future month when our store spends x ? = 200 on advertising.The point estimate in both situations is the same: y = ? 100 + 7 ? 200 ? 1300 the corresponding standard errors of the mean and of the prediction however are di? erent: SE µ ? 331. 663 SEy ? 690. 411 40 / 47 Choose Fit Model From response icon, choose Save Columns and then choose Predicted Values, Std Error of Predicted, and Std Error of Individual. Stat 326 (Spring 2013) Introduction to Business Statistics II 39 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II Inference about Prediction Estimation and prediction (cont’d) Note that in the appliance store example, SEy > SE µ (690. 411 versus 331. 63). This is true always: we can estimate a mean value for y for a given x ? much more precisely than we can predict the value of a single y for x = x ?. In estimating a mean  µy for x = x ? , the only uncertainty arises because we do not know the true regression line. In predicting a single y for x = x ? , we have two uncertainties: the true regression line plus the expected variability of y -values around the true line. Inference about Prediction Estimation and prediction (cont’d) It always holds that SE µ < SEy Therefore a prediction interval for a single future observation y will always be wider than a con? ence interval for the mean response  µy as there is simply more uncertainty in predicting a single value. Stat 326 (Spring 2013) Introduction to Business Statistics II 41 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 42 / 47 Inference about Prediction Example cont’d: JMP also calculates con? dence intervals for the mean response  µy as well as prediction intervals for single future observations y. (For instructions follow the handout o n JMP commands related to regression CIs and PIs. ) Inference about Prediction Example cont’d: To construct both a con? ence and/or prediction interval, we need to obtain SE µ and SEy in JMP for the value x ? that we are interested in: Month Ad. Expend. Sales Rev. Pred. Sales Rev. StdErr Pred Sales Revenues StdErr Indiv Sales Revenues Let’s construct one 95% CI and PI by hand and see if we can come up with the same results as JMP: In the second month the appliance store spent x = $200 on advertising and observed $1000 in sales revenue, so x = 200 and y = 1000 Using the estimated LS regression line, we predict: y = ? 100 + 7 ? 200 = 1300 Stat 326 (Spring 2013) Introduction to Business Statistics II 43 / 47 Need to ? nd t ? ?rst:Stat 326 (Spring 2013) Introduction to Business Statistics II 44 / 47 Inference about Prediction A 95% CI for the mean response  µy , when x ? = 200: Inference about Prediction A 95% PI for a single future observation of y , when x ? = 200: S tat 326 (Spring 2013) Introduction to Business Statistics II 45 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 46 / 47 Inference about Prediction Example cont’d: Advertising exp. Sales Rev. Lower 95% Mean Upper 95% Mean Sales Rev. Sales Rev. Lower 95% Indiv Sales Rev. Upper 95% Indiv Sales Rev. Month Stat 326 (Spring 2013) Introduction to Business Statistics II 47 / 47

Psychological disorders and physical illness Essay

Irrational fear of common things leads to their inability to cope with life because the things that they fear have to be faced everyday. The most common disorders include phobias, In the medical field, many factors are attributed to the various conditions that people experience. Although genetic and biological factors which constitute of the natural causes and the life experiences which are the nurture factors contribute to ones physical wellbeing, it is also possible that a majority of illnesses that people suffer from are brought about by psychological factors. Heart diseases, chronic headaches, insomnia, hypertension, ulcers, eating disorders among others are some of the diseases that can be caused by psychological factors (Stoudemire A. 1995). It has been established that when one’s stress levels are very high, it reduces the activity of the lymphocytes leading to an increased likelihood of illness (Sadock B J. , Kaplan H. I. & Sadock V. A. ). A person suffering from a psychological disorder will most likely have feelings of helplessness in any given situation causing them to suffer even from common illnesses more than others will. To aid the patients suffering from these disorders, one can only recommend relaxation techniques while providing anti-depressants to control their response to life’s normal occurrences. A person with a disorder tends to react in a more intense manner than other would in similar situations. This causes an imbalance in their bodily functions leading to their contracting illnesses that would have otherwise been avoided. It also affects their chances of getting better from any other illness that they may be suffering from. The disorders include anxiety, obsessive-compulsive disorders and panic (Ketterer M. W. , Mahr G`. & Goldberg A. D. ). These render a person powerless against their fears and in the process affect their wellbeing.

The untold Story of Theseus

The untold Story of Theseus The Untold Story of TheseusThe road ran along the edge of the cliff above the burning blue sea. Theseus turned a bend in the road and saw a man sitting on a rock. The man held a great battle-ax in his hand; he was so large that the ax seemed like a hatchet.Before the enormous man could say a thing, a thunderous voice shook the cliffs. The projected voice was heard to say, 'Bumble Bee Tuna, Look to la Luna.' Suddenly, Theseus disappeared in a flash of blue light.This is all I can tell you for I know no more. You shall have to ask the spirit of Theseus to continue...The Traveling(The spirit of Theseus Speaks)'I found myself in a dark place void of any light. I spent very little time in this manner. The dark place was soon illuminated by two men with fire sticks, that gave white light in stead of yellow.'Sciron beaten by Theseus. Detail of the side A fro...'These men that shone their fire sticks at me were dressed strangely; all in blue. They spoke in an unfamiliar tong and shoved mysel f into what I could only describe at the time as a horseless chariot.''After many days there slow minds concluded that I was a stranger in this land, and sent me to this house with many other young men my age.'(TimÂÆ'Â © PasÂÆ'Â ¡)'Many moons have passed. Over this time I was taught their language and concepts. I found that they call themselves Americans, more specifically 'Michiganders'. I have always been quick to learn and they labeled me as an adequate student. Me. Theseus. Son of Poseidon. Labeled as adequate? Sheeeshh. Anyway, I was told that tomorrow I would be going to a 'High School', specificly, Nth grade.'High School'High school was...

Remember the Simple Days of Reading

Remember the Simple Days of Reading Reach back in your memories, and remember how you read books. As a chid, as a teen, as a young adult, then now. Which book, when you see the title again, springs a memory back to life, reminding you how intensely you fell into that story and didnt want to climb out? I built a tree house for my grandsons second birthday. Yeah, I know its a little over-the- top nuts, but I designed it and had it built so its a place hell retreat to long into college. Right now its all about climbing the stairs and peering at the tree limbs and over at the chickens. In a few years, itll be about Swiss Family Robinson, Diary of a Wimpy Kid, or something along the line of Maximum Ride. Im running power to it in the future so that, yes, he can drag his laptop up there and maybe even spend the night, maybe bring his friends along. While I have a selfish motive, that of having him around more, I did it because what kid hasnt wished for a tree house? On Facebook, I showed pictures of the house (see one at the top of this newsletter cool, huh?), and was dumbfounded at the 200+ responses from adults. Men and women who recalled their tree houses, or pined over never having one. So I asked them why theres such an attraction to tree houses? Privacy, a place to read, a place to write, a place to feel closer to nature. If we reach back again in those reading memories mentioned earlier, thats what we wanted from the time we could read Dick and Jane books. Its an escape into our deep, true selves, gifting ourselves with permission to reach far. That is what a book is supposed to do. Sometimes, in our frenzy to learn how to publish, or our yearning to make money, we forget that feeling were supposed to be offering to readers. Maybe we need to climb up into a tree house to remember.

The Relationship Between Sharia Law and International Commercial Dissertation

The Relationship Between Sharia Law and International Commercial Arbitration - Dissertation Example ration. Procedural issues, such as: access to justice, service of process, standards of fair trial, evidence, independence and impartiality of arbitrators, joinder, intervention and consolidation, confidentiality of the arbitral proceedings, arbitral interim measures, requirements for an arbitral award. Substantial issues involving the merits of the dispute, such as the application of uniform law and mandatory rules. Acceptance of prior or intervening court judgments/ arbitral awards that may be recognized as res judicata and similar issues.4 Overall, the central debate in resolving issues about the merits of a dispute revolves about whether arbitrators can or should rely upon the general principles of law instead of the legal system of any one State. The debate gains significance when the issue is addressed differently by the general principles of law and the legal system of the state, and controversy exists in the degree to which the clashing principles of sovereignty and pacta sun t servanda should apply. b) Source of general principles of law in international commercial arbitration The phrase ‘general principles of law’ conveys the impression of a set of rules spontaneously arrived at by international businessmen; the truth, however, is that they are rules grounded in national legal systems. It must be clarified that the general principles of international law are not always applicable in all situations. In those instances that the parties have stipulated in their agreement their choice of law or rules of law that is to govern their relationship, there is no instance when general principles of law shall apply. Arbitrators are bound to respect the choice of the parties. The following are the more popular ICA rules according to institution: i) International Centre for Settlement of Investment Disputes (ICSID) Convention, Regulations and Rules ii) ICSID Additional Facility Rules iii) London Court of International Arbitration (LCIA) Arbitration Rule s iv) International Chamber of Commerce (ICC) Dispute Resolution Rules v) (United Nations Commission on International Trade Law (UNCITRAL) Arbitration Rules vi) Permanent Court of Arbitration Rules vii) American Arbitration Association (AAA) International Arbitration Rules viii) International Bar Association (IBA) Rules on the Taking of Evidence in International Commercial Arbitration 2) Islam and its Legal System a) Overview of Sharia The word â€Å"Shari’a† is Arabic for â€Å"the path† or â€Å"the way,†5 or more descriptively, â€Å"a path or way to a water hole in the desert.†6 The more figurative meaning would be the path Allah or God had designated for humankind to follow in

Economic developments in Germany and their impact on the EU economy Essay

Economic developments in Germany and their impact on the EU economy - Essay Example This paper demonstrates the main two economic pillars behind the unification were the theories of ‘Regional Trading Block’ and ‘Optimum Currency Area’. The former deals with a free trade area with a two-tier tariff system i.e. zero tariffs within the union members and some positive rate of tariff for the non union-members. (Robson 1999: 109-110) The latter deals with the introduction of a unique currency; it would enable the member states to enjoy the benefit of a fixed exchange rate system with the facility of full currency convertibility. (Krugman and Obstfeld, 1997: 631-33) The simultaneous functioning of a trading block and the optimum currency area was the main theoretical standpoint of the European Union and economic integration was the result of that. Economic integration is desired for the transfer of the benefit of economic development of one country to another. (Czinkota, Rivoli, Ronkainen 1989: 14-16) Let us consider the economic development of Germany and its influence on the European Union. Here our analysis would be concentrated on the economic development of Germany after the establishment of EU. German economy marks itself as a social market economy as the government undertakes a wide array of social services. As German economy is highly export oriented it advocated for European economic integration. After unification German commercial policies have been highly centred towards union. The social reform policies adopted by Germany for the welfare of the society and the structural industrial reform enhanced the performance of the economy and its global competitiveness. (US Dept of State 2008)