Does Vision And Mission Emerge Essay -- essays research papers fc

Does vision and mission rise up out of the specific culture of a firm or is it directed by procedure? Â Â Â Â Â The culture of a firm and the development of procedure are two very significant parts of an association. The world contains various individuals all with various qualities, thoughts and convictions. These distinctions make a various scope of societies inside associations, some having greater impacts than others. Systems inside associations are exceptionally powerful and complex, and can have positive and negative impacts on an association. Vision and crucial ideas that many accept are fundamental for an association to work successfully and as well as could be expected. Andrew Campbell (1991) , a noticeable essayist on vision and crucial, that both the way of life and the procedure of a firm met up one next to the other to assemble a general meaning of strategic a firm. The paper will be based around Campbell’s discernment and whether either culture or procedure has a larger part to play in an organisation’s mission. Â Â Â Â Â Culture and technique will be inspected in a setting applicable to the title question. Hofstede (1993) characterizes culture as ‘the aggregate programming of the psyche which recognizes one gathering or class of individuals from another’. Hofstedes exploration of various associations and nations permitted him to cause expectations in transit various social orders to work, including their administration forms and the speculations that would be utilized in the board. Morgan (1996) alludes to culture as ‘the example of advancement reflected in a society’s arrangement of information, belief system, qualities, laws, and everyday ritual’. In resulting works he develops authoritative culture as ‘self-sorting out and is consistently evolving’ and furthermore ‘ we are watching an advanced type of social practice that has been affected by numerous unpredictable collaborations between individuals, occ asions, circumstances, activities, and general circumstances’. These expansive meanings of hierarchical culture are significant bases and will be utilized all through. Â Â Â Â Â Defining system is a troublesome procedure since it is an intricate idea that has numerous structures and is continually evolving. Andrews (1987) endeavor is an extensive definition... ...(1980) Defining the Business – The Starting Point of Strategic Planning New Jersey, Prentice Hall Collins, J.C. and Porras, J.I. (1996) Built to Last – The Successful Habits of Visionary Companies Kent, Century Ltd. De Wit, B. and Meyer, R. (1998) Strategy – Process, Content, Context London, International Thomson Business Press Grovel, J. and Cox, B. (1985) Corporate Planning in Practice London, Kogan Page Ltd. Morgan, G. (1996) Images of Organizations London, Sage Publications Watchman, M.E. (1985) Competitive Advantage New York, The Free Press Diaries Baetz, M.C. and Bart, C.K. (1996) Developing Mission Statements Which Work Long Range Planning 29 (4), pp.526-533 Campbell, A. and Yeung, S (1991) Creating a Sense of Mission Long Range Planning August pp.10-20 Campbell, A. (1997) Mission Statements Long Range Planning 30 (6), pp.931-932 David, F.R. (1989) How Companies Define Their Mission Long Range Planning 22 (1),pp.90-97 Piercy, N.F. (1994) Mission Analysis: An operational methodology Journal of General Management 19 (3), pp.1-19 Hofstede, G (1993) Cultural Constraints in Management Theories Academy of Management Excutive 7 (1)

Essay on research How Apple Watch Can Spot Heart Issues

Article on inquire about How Apple Watch Can Spot Heart Issues For over a year, Apple Watch-based research was led by Stanford University so as to make sense of whether the gadget can distinguish heart issues. The outcomes gave off an impression of being promising despite the fact that they can’t be totally exact. 400,000 volunteers participated in the examination yet just 0.5 % of gadget clients were cautioned by Apple Watch that their heart rhythms are unpredictable. Later on, clinical experts uncovered that 84 % of the notices concerned scenes of atrial fibrillation, which are indications of the potential improvement of medical problems. At the end of the day, the innovation had all the earmarks of being equipped for keeping away from a plenitude of bogus positives, which are without a doubt the primary worry of the exploration. In addition, it gave off an impression of being solid enough for the volunteers to make a move and calendar a meeting with a specialist. The individuals who got makes concurred aware of wear an ECG fix for a week and 34 % out of them showed abnormalities. Around 57 % of the volunteers who saw the admonition talked with a specialist. It’s imperative to take note of that the investigation utilized Apple Watches from Series 3 and prior. Arrangement 4 showed up to some degree later so with its implicit ECG it couldn’t be utilized for the examination. The application was fundamentally focused on intermittent checks by methods for the pulse sensor for getting told if something doesn’t work appropriately. The outcomes that were uncovered over the span of the investigation can’t be completely depended upon. While 84 % is a figure sufficiently high to make the discoveries dependable, a 6th of the individuals who got cautioning notices may have no reason for concern. Additionally, it is likewise conceivable that individuals who have atrial fibrillation didn’t get a notice. Interestingly, the aftereffects of testing the Apple Watch gadget are sure and, ideally, Apple will keep pushing further in the wellbeing administration.

How hard is it, really

How hard is it, really I know, I know, its CPW season, but that doesnt mean that every blog entry has to be about CPW, so Im going to treat you to another story. I had very little physics experience when I came to MIT. Id gotten a 730 on my SAT II but only because Id crammed two days before the test by studying the test format and past questions, not the material. I took a physics class in high school but it was more hands on and not AP, nothing that could prepare me for MIT physics. There should have been indicators, MIT does its best to make sure you end up in a physics class that will push you but not break you. They do this by administering a math diagnostic when you first get to campus. The results of the diagnostic help you decide which physics class (8.01L, 8.01, 8.012 ranked from easiest to hardest) is best for you. The results of my math diagnostic said that I was prepared to take either 8.01L or 8.01. I opted for 8.01 because 8.01L continued through IAP in January, something I didnt want to do. The first day of 8.01 was quite fun, I enjoyed it, but we took a pretest and I didnt know how to answer any of the questions. Thats ok, I told myself, Ill learn. We had a couple of lectures and then had a problem set assigned. I worked on the problem set and realized that I wasnt able to do any of the problems. People all around me were blazing through them, claiming that it was easy and that it was stuff theyd seen in high school. This was discouraging, but again, I figured that with enough practice I could learn. You know, people say that physics is only learned through practice, but I quickly realized that I had an issue. The class was moving too fast for me to practice. The lectures didnt teach ANYTHING. They consisted entirely of powerpoint presentations that talked about theories, general concepts, and information that never actually taught me how to do physics. I asked dozens of people in class how they knew how to solve problems and the only thing I ever heard was In high sch ool ____ or I learned this last year. I learned nothing in 8.01 but was being testing over material I was expected to know. Its easy to memorize the equations for physics. Whats not easy is knowing how to manipulate them without practice. Physics is problem solving and I didnt have the background. I got an 80/100 on the first pset while all of my friends got high 90s. I thought that this meant I just had to work a little harder, but then a quiz was announced. I took the quiz and got completely dominated. I didnt know how to do any of it. The next day in class we got them returned to us. People around my table of nine began getting their quizzes back. Their scores were fine, high 80s and 90s. Some were complaining about how the only two points they lost were because of notation. I got my quiz back. 20/100. I got a 76 on the next pset, drowning in the work. Time for the next quiz, which was just as hard as the first one. Also, like the first one, everybody else at my table did fine. I didnt. 20/100. I began to take aggressive steps towards learning physics. I would practice back in the dorm, go to office hours, and do anything I could to learn, but it was extremely frustrating and I began to hate the subject. I began to panic. I would talk to my professor every day after class but he had nothing to offer me but to tell me to practice. At the start of the semester I had been assigned a seat at a table in the very back of the classroom, making it hard to see anything going on and interact/ask questions. I felt like crap, it was one of the worst feelings ever. I felt like MIT was just letting me fail. I showed up for class and was forced to sit in the back, I was failing quizzes, didnt understand the material, and the only advice the professor could give me was to practice. I NEED MORE THAN THAT! GIVE ME ADVICE THATS HELPFUL! YOUR JOB IS TO TEACH ME, DONT TELL ME TO GO READ A BOOK! TEACH ME PHYSICS! YOURE A FREAKING PROFESSOR AT FREAKING MIT AND YOURE LETTING ME FAIL YOUR CLASS. IM PUTTING IN THE EFFORT, I NEED HELP, DO YOUR JOB! Eventually I realized that I wasnt going to be able to do it. I couldnt pass physics, I was going to fail. Completely miserable, frustrated, and on the verge of tears, I could only think of one thing to do. I walked towards my advisors office and caught her in the hall. Can we talk? Absolutely, can you wait 5 minutes? Yes. We went into her office and I described my situation. We both agreed that I needed to not be in 8.01 anymore, that it wasnt feasible. She showed me my options, we checked schedules, e-mailed professors, and eventually got me switched into 8.01L. I went to my first 8.01L class and realized that it was the perfect class for me. The focus was on learning to problem solve, on slowly learning the basics and refining them. I actually learned how to solve problems, not just how to smash my way brutally through seemingly impossible obstacles. I ended up getting a B in 8.01L, even with the 20% plugged in for the first test I had missed while in 8.01. My loathing for physics has slightly decreased, but Im still extremely frustrated by how my obvious lack of experience was handled while in 8.01. Its my opinion that if a student is struggling in a class and is putting everything they can into getting a passing grade that the professor should do their best to help. My professor didnt, he didnt even suggest me switching into 8.01L, the solution that should have been obvious to him. This story is not really meant to be depressing, its meant to show that MIT can be too hard, absolutely, but youll know it. Many of my classes are hard (2.001, 8.02, 18.03, etc) but I can do them if I put in the work. Sometimes its not fun, but it is feasible. If you get a class that seems impossible but are still working through it and getting decent grades then MIT is doing its job. If you get to a class and find yourself extremely frustrated, failing everything, and cant see the light at the end of the tunnel, dont be scared to fix the problem. You are not a superhero, not everything is possible. MIT is hard and most of the time is manageable. Sometimes it isnt, the smart thing to do is to be able to recognize when it just isnt possible, admit that you arent the smartest kid in your class, and take action to fix it. Nobody will fault you for coming forward with your shortcomings and taking the steps necessary to correct your problem. Youll be much better off for it.

Media s Influence On The Media - 1078 Words

Seen as the heart of the political system, the media and its different portrayals of the presidency result in a quite unique relationship. At times, the media portrays the President positively and at other times the relationship may be a bit more negative. This relationship tends to make the flow of information and media spotlight a concern for the President to maintain. But, controlling the stream of information isn’t an easy task particularly when it is unfairly negative. The mass media retains unrealistic expectations of the President at times. Positive and negative portrayals and the constant effort to control the flow of information shape a distinctively complicated relationship with the media. The first aspect of the relationship is the positive depictions of the President by the media. In many cases, presidents are showed positively during a national event or in the midst of a crisis. A few great examples of this are the State of the Union address, international travel, or any instance where the President is a strong and representative figure of the nation. During devastating events such as attacks on U.S. soil, and crises, the media positively displays the President especially through his or her addresses. In fact, it is not just the media that looks at the president and general government in a supportive way during crises, but the American people as a whole. For instance, after the attack on 9/11, a Gallup poll showed that the support of Congress doubled inShow MoreRelatedMedia s Influence On The Media1637 Words   |  7 Pagesdisplayed in the mass media is conditioned by wealth and power, so as a result of the concentration of power and the official censorship done by the government and corporate sources; the media follows the ideas of the elite. In order to deliver messages that support the elite’s beliefs, the media goes through five different filters that determine the information presented, this are ownership of media, funding, sourcing, flak, and fear. First, when referring to the ownership of media, it is importantRead MoreThe Media s Influence On Media Essay1606 Words   |  7 PagesLusby English composition 12/1/2016 The Media s Influence    Can the media really persuade you into thinking a way about a person you have not even meet? The media can make influence you into thinking a certain way about some and also influence a choice that you could have to make about them that could change their life forever. To prove this I have researched into articles that could help me prove that the media can influence these things. First the media in the form of television can give you aRead MoreMedia s Influence On Media2111 Words   |  9 Pagestoday is communicated through media. Media is the most powerful and influential force in the country. The media are powerful agents of socialization and they set the standard that majority follow. The power giving to American media has allowed them to be very effective using propaganda as strategy, the media tend to say they serve to relieve social conflicts into minimum. We clearly see that the media promote social conflicts by separating class. The image that media has created in the mind of massesRead MoreMedia s Influence On The Media892 Words   |  4 Pages In today’s culture, it’s hard not to come across some form of media, whether that is an advertisement on a roadway, a commercial on the television, or even an ad on the portable games you play on your phone. The average 8-18-year-old experiences about 7.5 hours of some form of media a day. [1] Out of the 24 hours in a day over a quarter of it is spent looking at or listening to advertisements for products, the news, video games, television, movies, music, books, and the internet. A common way toRead MoreMedia s Influence On The Media1977 Words   |  8 Pagespushes their political view. News viewers tend to be oblivious when it comes to bias in the media because they would rather hear what they believe is right. There are many ways to find truth in journalism that everyone needs to be aware of for example, going to more than one source and conducting a SMELL test. Biased media has made a big impact on it’s viewers, creating a big division between the two sides. Media plays a big part on how people get everyday news, but ultimately, it is up to the viewerRead MoreMedia s Influence On The Media1986 Words   |  8 PagesWe are a world that revolves around our media outlets. This is because we depend on them to give to us the information that we need to be able to live our daily lives. Whether it is the news on politics or just events that are happening around your area. The real question though is has news changed? And the follow up question to that would be; how do historians think news has changed? The news media has changed throughout history because of the rise of technology. It is now possible to reach peopleRead MoreMedia s Influence On Media1928 Words   |  8 Pages V. New Media In the course of the most recent couple of decades, the media scene has changed drastically. The most essential change is from an old media model of television to another media model of narrowcasting. TV alludes to media speaking to the overall population and is exemplified by system TV, radio, and daily papers. Narrowcasting, made conceivable by television networks, Internet, and satellite radio, is focused to particular gatherings of people. The new media have various essentialRead MoreMedia s Influence On Media1543 Words   |  7 PagesSocial media publicizes a substantial amount of messages about identity and acceptable ways to express gender, sexuality and ones lifestyle, but at the same time, the viewers have their own differing feelings about the issues. The media may suggest certain feelings and actions, but the audiences feelings can never overpower self-expression completely. The media portrays certain things because it is what is being accepted. Neither parties, these being the media and its audience, have full power overRead MoreMedia s Influence On Media1703 Words   |  7 Pagescentury, mass media became widely recognized. In a period of mass availability, people today have entry to more media outlets than ever before. According to media scholar Jean Kilbourne,â€Å"the average American is exposed to over 3,000 advertisements a day and watches three years’ worth of television ads over the course of a lifetime† (back cover). It is all around us, from the shows we watch on television, the music we listen to on the radio, and to the books and magazines we read each day. Media is the numberRead MoreMedia s Influence On The Media Essay1172 Words   |  5 PagesMass media has a very influential part in today’s society. Consisting of radio broadcasting, books, the Internet, and television they allow information and entertainment to travel at a fast pace as well to a vast audience. This vast majority of information can easily manipulate and or persuade people to have certain stereotypes on specific genders. TV commercials are one of the most influential structures in the media. Looking back 20 to 30 years, stereotypes were clearly welcomed on TV and in

Negative Effects of Gender Discrimination at Workplaces in...

Recently, gender inequality is being emphasized as an acute and persistent problem. In the USA, this is predominantly due to that fact that women are demanding their rights at workplaces. Mostly, they try harder to be appropriate and successful in their careers rather than men. ‘Differential treatment within the labor market is what we refer to as labor market discrimination’ (Ehrenberg and Smith, 2012, p398). Gender discrimination against women in the market place reduces the available talent in workplaces and has negative consequences on the country. Gender discrimination in the United States can lead to damages to the effectiveness of labor market such as unequal employment regulations, less promotion chances and unfair wage†¦show more content†¦The current orthodoxy on that delicate issue is that females are less likely to manage several people or run a company rather than males. As proposed by Catalyst (cited in Berry, D., and Bell, M., 2012, p238) males ar e dominating the top in case of inequality in economics and social authority. ‘Gender inequality in the workplace is related to the differential distribution of men and women across positions of power, prestige and responsibilities, and studies show that women are less likely than men to hold authority positions’ (Rosenfeld, et al. cited in Birkelund, G., and Sandnes, T., 2003, p204). To conclude, discernibility in promoting workers in accordance with their gender is one of the most harmful aspects of discrimination. Ultimately, unfair wage distribution is associated to be the last effect of sex discrimination. A number of studies analyse wage disparity as a consequence of occupational discrimination. ‘A recent study, for instance, finds that a woman working in an occupation where at least 75 percent of the coworkers are women earns about 14 percent less than a comparable woman working in an occupation where more than 75 percent of the coworkers are men’ (Borjas, 2010, p403). Moreover, Borjas states that in the United States, wage differential is produced by constituting a number of females in few occupations and at the same time, it diminishes the gainings of so-called women jobs (2010, p403).Show MoreRelatedWhen People Think Of Discrimination, They Tend To Think1254 Words   |  6 Pagespeople think of discrimination, they tend to think back to older times of slavery, racism, and an underdeveloped country. Sadly, discrimination actual plays a large role in the workplace of today. Discrimination is defined as â€Å"treating a person or particular group of people differently, especially in a worse way from the way in which you treat other people, because of their skin color, sex, sexuality, etc.† according to the Cambridge Dictionary (Cambridge University Press 1). Discrimination comes in manyRead MoreThe Underlying Reasons For The Lack Of Female Politicians1433 Words   |  6 Pagestypes of discrimination they face because of their gender. The first premise of the op-ed will be to show that social inequality continues to exist in the form of the gender wage gap. Secondly, negative media portrayal contributes to the lack of support for female politicians. Both of these premises are useful in showing that discrimination makes it difficult for women in the political arena because they have less monetary support as well as populace rallying behind them. Lastly, gender and partyRead MoreDiscrimination Based On Sexual Orientation Essay1492 Words   |  6 Pagesjust system should view all people no matter what their race, gender, or social class is. In a world where Lesbians, gays, bisexuals, and transgender people are being slandered, it is hard for them to get jobs because of their sexual preferences or how they identify. Gays and lesbians who choose to come out around â€Å"straight† people they work with will probably face at least some (if not much) discrimination. During work, â€Å"discrimination based on sexu al orientation must not be tolerated in any formRead MoreLegal Protection Has Not Prevented Women And Girls Being Discriminated Essay1493 Words   |  6 PagesLegal Protection has not Prevented Women and Girls Being Discriminated Again People are very judgmental, and they make decision based on some appearance. They treat women and girls differently based on their gender. We have been seeing discrimination over female throughout the history. Women are as strong as men, and they are equally intelligence. Women and girls are being discriminated because of their sex, and their roles, the role that were known for female when human race has started. InRead MoreEssay Dispelling the Myths of Ageism3422 Words   |  14 Pagesand judged solely on the basis of their chronological age (Berger, 2008). Our western culture has embedded ageism into our everyday lives, and we may not be able to identify the ageist behavior when it occurs. Older adults are frequently given negative labels such as senile, sad, lonely, poor, sexless, ill, dependent, demented, and disabled. (McGuire, Klein, Shu-Li, 2008) It is inevitable that we will experience decline in physical and mental capacities. However, the timing, quality, and degreeRead MoreVarious Aspects and Various Types of Discrimination Essay3444 Words   |  14 Pagespeople some individual doesn’t treat some people equally and behaves very badly and think in a negative way about other people, this is called discrimination. This is one of the major problems which the whole world is facing. The purpose of my research is to study the va rious aspects and various types of discrimination and then find out who all are and how they are getting effected by this discrimination and then I will come up with dome recommendations to solve this problem. People don’t treat otherRead MoreWorkplace Diversity : The Visible Or Invisible Differences Among Employees Of An Organization1653 Words   |  7 PagesWorkforce Diversity defines the visible or invisible differences among employees of an organization and could be divided in to three major components such as Primary, Secondary and Organizational and Community. Factors such as Age, Gender, Sexual Orientation, Ethnicity represents Primary while Appearance, Educational Background, Marital Status, Work experience represents Secondary and Job position, Specialization, Nationality, Life stage represents Organizational and Community. In recent years, organizationsRead More Affirmative Action Essay1664 Words   |  7 Pageswell. Every sector in America would be equal and unprejudiced - or so proponents say. On the other hand, adopting affirmative action would force many employers to replace hard-working employees with those of less qualification simply due to their gender or ethnic background. Many people feel that affirmative action would be very beneficial to our society. They have many thought-inspiring arguments. Some claim that we owe blacks for what we took from them in the past. We gave them a setback in ourRead MorePolicy Background : Pay Fairness Act2592 Words   |  11 Pagespaper contains a background about the pay fairness act which is legislation that aims to reduce the gender wage gap in the United States. The first part will describe how the legislation started and how it has changed over time. Also, I will be showing the importance of the act and the obstacles it has faced in congress. The second part of this paper will highlight some alternatives to solving the gender wage gap problem, and I will be describing why I think the Pay Fairness Act is the best solutionRead MoreAdolescent Bullying : A Survey Measuring Adolescent s Responses Regarding Self Confidence2289 Words   |  10 Pagestext messages and the identity of the stu dent remained anonymous to the surveyor and other respondents. Students coming from Hispanic and Asian ethnicities responded being with victims of bullying due to discrimination. Key words: Adolescence bullying High school students Discrimination Victim Self-confidence Supportiveness Secureness Integrity Judgement How is it to be a target of adolescent bullying? The importance of human morale, integrity, and respect is known when a person goes

Rhetorical Analysis of “Pink Think” Free Essays

Women have been told what to do since the beginning of time. â€Å"Pink Think† furthers that idea. This article by Lynn Peril explains what influences have impacted the way females act and think. We will write a custom essay sample on Rhetorical Analysis of â€Å"Pink Think† or any similar topic only for you Order Now Emotional appeal, the use of the theory pink think and her use of specific examples from history all come together to establish her case that women have been expected to fit into a specific mold in order to be a successful woman in life. Every woman feels the need to fit in with society. By fitting in, the woman would get the perfect guy, be successful in life and feel included. Lynn Peril shows how the attitude of Pink think made women feel the need to fit in. There were articles that showed the joys of housewifery. Women who read these articles felt that if they were a housewife and enjoyed the aspects of it written in the article, they fit in. It is a trait in women that all women want to fit in. We look in magazines and wish to look and dress like the models. This was what women thought about Pink think. It was the â€Å"in† way to act and think. Women who thought this way fit in and those who did no wanted to so that they could fit in. Lynn Peril shows how Pink think made women want to fit in, and it worked. The theory of Pink Think is a set of ideas and attitudes about what constitutes proper female behavior. It was very popular from the 1940s to the 1970s. The theory of Pink think is the main argument of this essay. The cultural mindset of Pink think touched every female. The women read about it in articles, teens learned about it in their home economics textbooks, and little girls learned the feminine behaviors in games such as Miss. Popularity. With all the aspects of a woman’s life having some type of Pink think, it is no wonder women felt the need to fit into this mold. Pink think also told women that femininity was the only way to get and marry a man. And that was the only way to have a child, which was what women were supposed to do. Pink think also â€Å"made beauty, charm, and submissive behavior of mandatory importance to women of all ages in order to win a man’s attention and hold his interest after marriage. † It made women believe the only thing to do in life was to please a man. Pink think took over the way women act and thought in order to fit into what society thought a woman should be like. The use of specific examples in â€Å"Pink Think† helped Lynn Peril show that this theory influenced women in any stage of their life. Pink think influenced women from the way they put on their bathing suit to the choice of contraception. The example that had a real influence on me was the Miss America competition of 1961. Nancy Fleming’s answer to just kick both of her heals off and continue down the runway was a good one, but her answer that too many women were working and they should just be at home was shocking. Also the fact that she won after that answer really surprised me. Fleming was putting women throughout the country down and saying they should just stay at home and have no place in the workforce. Women should have the choice to work or stay at home. I do not think the role model for America should have told the world that women are over powering men and her place is in the home. Peril’s use of several specific examples allows her to connect to deferent readers. By having several examples, Lynn Peril expands the audience that she affects. By using emotional appeal, the use of Pink think, and several specific examples Lynn Peril shows readers how women were influenced to act and think a certain way. Some of these attitudes are still looming around today. Just because Pink think was popular from 1940-1970, does not mean the idea does not show up today. Women are still expected to act and think a certain way. Lynn Peril showed how women were supposed to act back then, and it has changed in present day, but some ideas are still around. How to cite Rhetorical Analysis of â€Å"Pink Think†, Papers

“the American Criminal Justice System” free essay sample

â€Å"The American Criminal Justice System† Timeline: Key events in the historical development of capital and corporal punishment In 1692, the Salem Witch Trials began and is considered a great tragedy in history involving religion and beliefs. Many people suffered during this time if they were identified as a witch and the consequences were enforced by the minister of Salem, Samuel Parris, and his followers. A few punishments involved unlawful search and seizure’s, trials, and if convicted, executed. The Salem witch trials continued for eight months after Cotton Mather argued the mass convictions against the accused suspects and after the clergy began to question the evidence, Governor Phips, put a stop to the executions and all accused. A total of twenty people and two dogs lost their lives during this event in time. In 1934, the military prison closes on Alcatraz Island due to a rise in operational cost and the Federal government opens a Federal Prison on the Island to incarcerate high profile inmates. This location was ideal to hold unmanageable offenders in isolation and officials hope it would deter committed crime to those individuals in society. Fourteen attempted escapes occurred within the twenty-nine years of operation. In March 1963, Alcatraz closed due to the building slowly deterring and security measures diminished due to budget cuts. After the escape of Frank Morris and the Angelin Brothers, many scrutinized the prison for its security. In 1987, the United States created the sentencing guidelines under the Sentencing Reform Act in 1987. The guidelines resulted in a criminalization and sentencing process allows the prosecution control and Judges would have to follow these guidelines with little discretion on the decision. Congress would have the responsibility of creating a structure to avoid â€Å"Unwarranted sentencing disparity among defendants who held similar records who have been found guilty. † The sentencing guidelines initiated a debate involving the legal and social conflicts and consequences. Capital Punishment Methods and Procedures: Lethal Gas and Lethal Injection are the two methods used in the State of California for capital punishment. The procedure for a Legal Injection execution involves strapping the inmate onto a gurney with restraints in an execution chamber. A cardiac monitor is connected to the inmate and a printer outside the execution chamber. Two I. V. ’s are inserted in the veins and once the Warden signals to start, 5. 0 grams of sodium pentothal is injected. The line is then flushed with sterile saline solution and followed by 50 cc of pancuronium bromide. The line is flushed once more and the last injection consists of 50 cc of potassium chloride. The procedure for Lethal Gas execution is the inmate is restrained in a steel airtight chamber. Cyanide pellets are held in a container underneath the inmate. A container on the floor contains sulfuric acid and three executors turn one key. The electronic switch causes the container on the floor to open and allows the cyanide to fall in the sulfuric acid to create the lethal gas. The inmate is monitored so the warden knows when the inmate is no longer breathing. Exhaust fans and ammonia are pumped into the chamber, along with two scrubbers that contain water. This process takes about 30 minutes and the death normally occurs within six to eighteen minutes. Scott Peterson was sentenced to lethal injection in March 2005 for the murder of his wife, Laci Peterson and is currently incarcerated in San Quentin Prison. Scott Peterson is a white male, thirty eight years of age. Richard Ramirez is known as the â€Å"Night Stalker† and sentenced to death row in 1989 for killing 14 or more people. Richard Ramirez is a fifty-one years old, Mexican male. Richard Davis was sentenced to death row for the murder of Polly Klaas in 1996 and other convictions include: robbery, burglary, kidnapping and lewd acts upon a child. Richard Davis is a white male, fifty-six years old. Constitutional Amendments which safeguard inmates: The Eighth, Fifth, and Fourth Amendment protects inmates from cruel and unusual punishment, discrimination, allows inmates eligibility for parole, as well as enforcing due process. The Eighth Amendment requires jails and prisons to adhere to standard living conditions in a humane environment, involving the necessities to live and protection against physical abuse. The Fifth Amendment offers inmates the right to a speedy and unbiased trial, along with a public defender to represent their case in court. Lastly, the Fourteenth Amendment grants inmates protection and equal discipline from correctional officers. Prison architecture designs in America: The New York State’s Auburn System and the Pennsylvania System were two primary architecture designs in America. Eastern State Penitentiary is known as the first penitentiary designed by John Haviland which opened on October of 1829. The center tower was located in the middle of the penitentiary which attached to single story cell blocks. The single unit cells were 8 x 12 feet x 10 feet high and included running water, a toilet, and asmall individual exercise yard which was the same width as the cell. The primary purpose for the penitentiary was to place criminals in solitary confinement in hopes that the inmates would use the time to reflect on their deviant ways and change their behaviors which would lead them on the road to redemption. With the inmates in solitary confinement, inmates were unable to provoke or fight with each other while incarcerated and I would hope some would feel intimidated with living conditions with no intentions of returning. Some argued the prison was costly and majority of the guards would torture the inmate physically and psychologically making the living conditions inhumane. The prison eventually closed and was abandoned in 1971 due to the prison falling apart and currently is operating as a museum. Prison classification is used to asses an inmate’s risk and program needs. The levels are classified as close, medium, minimum 1, minimum 2, and minimum 3. Inmates classified as â€Å"close† present the highest risk of and minimum 3 inmates present the lowest risk. Authorities in the division of prisons are responsible for assigning inmate’s classification/level. These measures have been put in place for the purpose of maintaining order, protecting staff, and inmate safety. Differences between Parole and Probation duties and where the process is used in the Criminal Justice System: Probation is the sentencing during and after incarceration for an individual who committed a crime. Depending on the extent of the crime, a judge orders how much time an individual is incarcerated and once released, orders conditions that need to be followed while on probation. Individuals are responsible for following the rules and a probation officer will closely monitor the individual’s rehabilitation process. Parole is known as an early release from prison and is offered to individuals who follow the rules while incarcerated. The parole board will make the decision if the inmate is ready to be released back into society and evaluate his behavior while incarcerated. Conditions still do apply to a parole and our closely monitored by a Parole Officer. Probation occurs after a person has committed a crime and Parole occurs while a person is incarcerated. A few responsibilities or duties for a Probation Officer include: Evaluating the offender’s progress and behavior; Assist offenders in finding and maintain work; and responsible for all case management. A Parole Officer monitor’s the released individual to ensure they are following the conditions and restrictions. Some duties include: maintaining contact with the parole and their families; evaluate and report the parole’s progress; may work with both Juveniles and adults. Differences between rehabilitation and punishment and who might favor each concept: Rehabilitation gives an individual a chance to learn about his actions and offers individuals’ assistance for reentry into society. Rehabilitation programs are available to both adult and juvenile offenders. Drug Addiction Rehab, Alcohol Addiction Rehab, Violent Behavior Rehab are a few programs used in rehabilitating a person. Incarceration confines an inmate to a small cell with little to no privileges. Unless an inmate is in the process of rehabilitation, Incarceration does not offer help. Incarceration is in place for both juvenile and adult offenders. In California, the average cost for each prisoner is $35,000 per year and $70,000 per year for elderly inmates who require more attention to their health care. Offenders who regret their actions and have strong morals would favor the rehabilitation concept. While repeat offenders who learn to adapt to the prison environment will favor incarceration.

Descartes Essays (4719 words) - Epistemologists, Metaphysicians

Descartes How does Descartes try to extricate himself from the sceptical doubts that he has raised? Does he succeed? by Tom Nuttall [All page references and quotations from the Meditations are taken from the 1995 Everyman edition] In the Meditations, Descartes embarks upon what Bernard Williams has called the project of 'Pure Enquiry' to discover certain, indubitable foundations for knowledge. By subjecting everything to doubt Descartes hoped to discover whatever was immune to it. In order to best understand how and why Descartes builds his epistemological system up from his foundations in the way that he does, it is helpful to gain an understanding of the intellectual background of the 17th century that provided the motivation for his work. We can discern three distinct influences on Descartes, three conflicting world-views that fought for prominence in his day. The first was what remained of the mediaeval scholastic philosophy, largely based on Aristotelian science and Christian theology. Descartes had been taught according to this outlook during his time at the Jesuit college La Flech_ and it had an important influence on his work, as we shall see later. The second was the scepticism that had made a sudden impact on the intellectual world, mainly as a reaction to the scholastic outlook. This scepticism was strongly influenced by the work of the Pyrrhonians as handed down from antiquity by Sextus Empiricus, which claimed that, as there is never a reason to believe p that is better than a reason not to believe p, we should forget about trying to discover the nature of reality and live by appearance alone. This attitude was best exemplified in the work of Michel de Montaigne, who mockingly dismissed the attempts of theologians and scientists to understand the nature of God and the universe respectively. Descartes felt the force of sceptical arguments and, while not being sceptically disposed himself, came to believe that scepticism towards knowledge was the best way to discover what is certain: by applying sceptical doubt to all our beliefs, we can discover which of them are indubitable, and thus form an adequate foundation for knowledge. The third world-view resulted largely from the work of the new scientists; Galileo, Copernicus, Bacon et al. Science had finally begun to assert itself and shake off its dated Aristotelian prejudices. Coherent theories about the world and its place in the universe were being constructed and many of those who were aware of this work became very optimistic about the influence it could have. Descartes was a child of the scientific revolution, but felt that until sceptical concerns were dealt with, science would always have to contend with Montaigne and his cronies, standing on the sidelines and laughing at science's pretenses to knowledge. Descartes' project, then, was to use the tools of the sceptic to disprove the sceptical thesis by discovering certain knowledge that could subsequently be used as the foundation of a new science, in which knowledge about the external world was as certain as knowledge about mathematics. It was also to hammer the last nail into the coffin of scholasticism, but also, arguably, to show that God still had a vital r_le to play in the discovery of knowledge. Meditation One describes Descartes' method of doubt. By its conclusion, Descartes has seemingly subjected all of his beliefs to the strongest and most hyberbolic of doubts. He invokes the nightmarish notion of an all-powerful, malign demon who could be deceiving him in the realm of sensory experience, in his very understanding of matter and even in the simplest cases of mathematical or logical truths. The doubts may be obscure, but this is the strength of the method - the weakness of criteria for what makes a doubt reasonable means that almost anything can count as a doubt, and therefore whatever withstands doubt must be something epistemologically formidable. In Meditation Two, Descartes hits upon the indubitable principle he has been seeking. He exists, at least when he thinks he exists. The cogito (Descartes' proof of his own existence) has been the source of a great deal of discussion ever since Descartes first formulated it in the 1637 Discourse on Method, and, I believe, a great deal of misinterpretation (quite possibly

Free Essays on The Culture Of Different Times

The Culture of Different Times The legendary story of â€Å"Beowulf† is a classic example of a heroic warrior class figure in an Old English/Anglo Saxon society. He is tough and brutal, noble and heroic. Reading â€Å"Beowulf,† we get a sense of a less human society where fighting for ones honor is what matters the most. On the flip side of that, we see a sort of revolution, a progression towards the â€Å"is† world, in literature. One example of this is â€Å"The Canterbury Tales† by Geoffrey Chaucer. The culture presented in the â€Å"Canterbury Tales† differs from that presented in â€Å"Beowulf† because society has become more humanistic. These people don’t want to start a war, the want to stay alive, start families and raise children. In Chaucer’s time men took pilgrimages, in the time of Beowulf, they fought war. Anglo Saxon culture was a culture of honor, brutality and war. For example, Beowulf was a man who fought because he had to weather it is for his own survival, the survival of his tribe or the strength of his country. Although he knew he was mortal, he fought and fought as if he were sub human. Men with that type of mentality did not speak of their feelings of love or even the feeling of emotional pain unless it was pain caused in a battle, and only that pain was worth mention. This type of behavior was typical of Anglo Saxon times in that men fought till the bitter end and lived in a shame filled culture unlike the guilt culture that became of the Medieval Ages. Medieval culture was quite different compared to Anglo Saxon culture. People in Medieval times seemed more human, and more willing to share their feelings. From reading literature from that time we gain insight into the lives of characters, which in turn allow us to gain insight into the culture of the middle ages. For example, the characters in Chaucer’s â€Å"Canterbury Tales† call to our attention the fact that something has changed. Fighting was not t... Free Essays on The Culture Of Different Times Free Essays on The Culture Of Different Times The Culture of Different Times The legendary story of â€Å"Beowulf† is a classic example of a heroic warrior class figure in an Old English/Anglo Saxon society. He is tough and brutal, noble and heroic. Reading â€Å"Beowulf,† we get a sense of a less human society where fighting for ones honor is what matters the most. On the flip side of that, we see a sort of revolution, a progression towards the â€Å"is† world, in literature. One example of this is â€Å"The Canterbury Tales† by Geoffrey Chaucer. The culture presented in the â€Å"Canterbury Tales† differs from that presented in â€Å"Beowulf† because society has become more humanistic. These people don’t want to start a war, the want to stay alive, start families and raise children. In Chaucer’s time men took pilgrimages, in the time of Beowulf, they fought war. Anglo Saxon culture was a culture of honor, brutality and war. For example, Beowulf was a man who fought because he had to weather it is for his own survival, the survival of his tribe or the strength of his country. Although he knew he was mortal, he fought and fought as if he were sub human. Men with that type of mentality did not speak of their feelings of love or even the feeling of emotional pain unless it was pain caused in a battle, and only that pain was worth mention. This type of behavior was typical of Anglo Saxon times in that men fought till the bitter end and lived in a shame filled culture unlike the guilt culture that became of the Medieval Ages. Medieval culture was quite different compared to Anglo Saxon culture. People in Medieval times seemed more human, and more willing to share their feelings. From reading literature from that time we gain insight into the lives of characters, which in turn allow us to gain insight into the culture of the middle ages. For example, the characters in Chaucer’s â€Å"Canterbury Tales† call to our attention the fact that something has changed. Fighting was not t...

I.T and society Essay Example | Topics and Well Written Essays - 750 words

I.T and society - Essay Example I hope to explore these themes in a way that transcends the terms of the well-known debates over the normative and historical ramifications of the late Weber's theorizing of charisma and Fuhrerdemokratie. (Feldman, 2005, 60) However, what must be addressed in the course of this analysis is the fact that Lukacs and Schmitt themselves -- each in their own way, to be sure -- endorsed twentienth-century political mythologies that most vigorously championed political will: left- and right-wing authoritarianism in the forms of, respectively, Soviet Communism and National Socialism. In Weber the neutrality and technological innovation does not however prevent the emergence of a prejudiced disposition over historical specificity: that is, the melancholy of the conclusion of The Protestant Ethic and the "Science" lecture which fuels the call for responsible personal stands in the "Politics" and "Parliament and Government" lectures. Lukacs's early writings betray a similar lament over, and desire to actively transcend, the alienation brought on by a rationalized modernity. In this regard he frequently' exhibits an existential pathos derived often explicitly from Kierkegaard, Nietzsche, and Dostoyevsky. (Portis, 1990, 759) In both works the phenomenon of rationalization is... As Schmitt observes its influence is nearly all-pervasive: "In almost every discussion one can recognize the extent to which the methodology of the natural-technical sciences dominates contemporary thinking". But again he now attributes the genesis of this rationality to a Marxian category and no longer a Weberian one: "The modern modes of thought already eroded by the reifying effects of the dominant commodity form" encourages purely "quantitative" analyses of society and not "qualitative" ones ((Feldman, 2005, 60)). Its common ground is a concept of nature that has found its realization in a world transformed by technology and industry. Nature appears today as the polar antithesis of the mechanistic world of big cities whose stone, iron and glass structures lie on the face of the earth like colossal Cubist creations. The antithesis of this empire of technology is nature untouched by civilization, wild and barbarian -- a reservation into which "man with his affliction does not set foot." (Feldman, 2005, 60) The old gods rise from their graves and fight their old battles once again, but now disenchanted and now, as should be added, with new means of struggle which are no longer mere weapons but terrifying means of annihilation and extermination -- dreadful products of value-free science and the industrialism and technology that it serves. What is for one the devil is for the other the god. That the old gods have become disenchanted and become merely accepted values makes the conflict specter-like and the antagonists hopelessly polemical. References Chekki, Dan A. Western Sociologists on Indian Society: Marx, Spencer, Weber, Durkheim, Pareto. Social Forces, Mar81, Vol. 59 Issue 3, p848-849 Feldman, Leonard. Max Weber's

Thailand's Economic Development and Growth Research Paper

Thailand's Economic Development and Growth - Research Paper Example The research strategy is mainly deductive in nature. The findings from this paper suggest that Thailand has experienced a steady surge in the GDP over the years. There has been positive impact of this growth on the social factors like, level of literacy, conditions of Health and finally, the impact on poverty of the nation. This work has identified two areas of shortcomings for Thailand to improve: it has to focus on the quality of secondary education and reduce the inequality of income between the rich and the poor. Thailand has been a success story and the policies followed by it have been taken as a lesson for other developing economies to follow. Thailand’s incredible growth has put it in the league of the other Asian tigers. This work has found out that the regional pockets of poverty in Thailand needs serious attention from the Government. The government must follow inclusive growth policies to include the poorest of the poor into the formal structure of the labor market in order to reduce the income inequality. Introduction The economic progress witnessed by Thailand is perhaps one of the most interesting economic development literatures that continue to interest economists all over the world. Such in fact has been the pace of growth of Thailand’s economy, that in a recent study by the World Bank, Thailand has been upgraded to the status of a high middle-income economy from a low middle-income one in 2011 (The World Bank, 2013). So, the pertinent question revolves around the wide economic changes during the period of 1980’s to 2000 that had led the economy to achieve the status that it had achieved today. It was observed that during the specified time period, Thailand had experienced an increasing trend of capital inflows by opening up its economy and integrating it with the world economy (Beja, Junvith and Ragusett, n.d.). About two decades ago, Thailand experienced a growth that had become exemplary worldwide and since then, its su ccess story acts as a benchmark for the other economies to follow. However, right at the end of the millennium, the noteworthy rise came to a grinding halt due to the unfavorable conditions of the world economy, better known as the Asian crisis. This paper aims to understand the factors which were responsible for such high rates of growth in Thailand. For the research purpose, the period from 1980’s to 2000 has been considered. The main objective is to evaluate the growth of the economy along with studying the growth of population, in this period. Then, the impact of growth on the level of literacy and conditions of health will be analyzed in details. An inductive methodology is used for the purpose. Based on the findings, the results are drawn accordingly. The paper mainly tries to understand the reasons behind the economic growth admitting the fact that there was indeed a huge growth (Chuenchoksan and Nakornthab, 2008). The most astonishing aspect, inspite of the definite i ncome growth, is the spectacular levels of income inequality.

Development of Insulin using Recombinant DNA Technologies

Development of Insulin using Recombinant DNA Technologies Alistair Jones The use of biotechnology within medicine; diabetes and development of insulin using recombinant DNA technologies Abstract Proteins act as a catalyst for metabolic reactions and responsible for inter and intracellular reactions and signalling events essential for life(Ferrer-Miralles, et al., 2009) Diabetes mellitus is a metabolic disorder with numerous aetiologies; it can be defined by chronic hyperglycaemia which will cause an effect on the metabolism of carbohydrates, fats and proteins. This detrimental effect is from the lack of insulin action, insulin secretion or a combination of them both. Diabetes causes long term damage, dysfunction and failure of a range of major organs. (Consulation, 1999) Through the use of clinical administration missing proteins can be sourced from external sources to reach normal concentrations within the tissular or systemic level. As a number of important studies have all confirmed the importance of the use of strengthened insulin treatment for the reduction and minimisation of long term diabetic complications; it is of great importance and pharmaceutical value that huma n proteins can be sourced (Lindholm, 2002) Through the use of biochemical and genetic knowledge the production of insulin has become available and this industrial scale of therapeutic protein production is the first true application of recombinant DNA technology. (Swartz, 2001, Walsh, 2003) E.coli can be considered as the first microorganism for the production of proteins and is primarily used for genetic modification, cloning and small-scale production for research purposes. Many historical developments within molecular genetics and microbial physiology have been based within this species which has results in a collection of both information and molecular tools. (Ferrer-Miralles, et al., 2009) Discussion Proteins act as a catalyst for metabolic reactions and responsible for inter and intracellular reactions and signalling events essential for life; consequently , a deficiency in the production of polypeptides or production of non-functional of relevant proteins will derive in pathologies which can range from mild to severe (Ferrer-Miralles, et al., 2009). Diabetes mellitus is a metabolic disorder with numerous aetiologies; it can be defined by chronic hyperglycaemia which will cause an effect on the metabolism of carbohydrates, fats and proteins. This detrimental effect is from the lack of insulin action, insulin secretion or a combination of them both. Diabetes causes long term damage, dysfunction and failure of a range of major organs. The characteristics presented with diabetes are weight loss, polyuria, blurring of vision and thirst; the more severe cases will cause ketoacidosis or a non-ketotic hypersmolar state which will lead onto comas, stupor and left untreated death. As the symptoms are often not severe and go undetected for long periods of time, hyperglycaemia can cause pathological and functional changes before a diagnosis can be made. Diabetes causes a multitude of long term affects which include, but not limited to; the failure of the renal system, a two to four times increased risk of cardiovascular disease and potentia l blindness. There are a number of pathogenetic processes which can be involved in the development of diabetes; these will include the processes which destroy the insulin creating beta cells within the pancreas and the creation of a resistance to insulin action ( Alberti, et al., 2006, Consulation, 1999) A combination of metabolic disorders known as metabolic syndrome (MetS) is the combination of hyperglycaemia, hypertension and gout and other cardiovascular risk factors which predict a high risk of developing diabetes. People who have MetS are of the highest risk of the development of type 2 diabetes as it is present up to five times higher within people with this syndrome; this is due to the fact that glucose dysregulation is already present (Alberti, et al., 2006). Type 2 diabetes and atherosclerotic cardiovascular disease can be seen to be of similar ascendants. Inflammation markers have been associated with the development of type 2 diabetes in adults; although this may be part of the autoimmune response they will also reflect the pathogenesis (Schmidt, et al., 1999) Abnormal metabolism of proteins, fats and carbohydrates is caused by the deficient insulin action on target tissues due to the insensitivity or lack of insulin. (Consulation, 1999) Through the use of clinical administration missing proteins can be sourced from external sources to reach normal concentrations within the tissular or systemic level. As a number of important studies have all confirmed the importance of the use of strengthened insulin treatment for the reduction and minimisation of long term diabetic complications; with human insulin being the first line of treatment; it is of great importance and pharmaceutical value that human proteins can be sourced, as this is difficult to do from natural sources (Lindholm, 2002) . We are far past the times of animal sourced insulin’s and we are reaching the turning point in the use of recombinant DNA technologies; which were developed during the late 70’s and uses E.coli as a biological framework for the production of pr oteins of interest through relatively inexpensive procedures. Recombinant DNA technology not only offers the ability to create straightforward proteins but also provides the tools to produce protein molecules with alternative and modified features. (Mariusz, 2011) There are several obstacles in the production of proteins through the use of E.coli however, as it lacks the ability to make post-translational modifications (PTMs) present within the majority of eukaryotic proteins (Ferrer-Miralles, et al., 2009). Recombinant DNA insulin’s are, therefore, gradually being replaced by the more highly efficient insulin analogues (Bell, 2007, Ferrer-Miralles, et al., 2009). Clinically, insulin analogues have been used since the late 1990s, the reason behind insulin modification for subcutaneous injection is to produce absorption properties that better suit the rate of supply from the injection to the physiological need. (Jonassen, et al., 2012) Insulin analogues have the properties of being able to be either rapid acting such as glusine, aspart or lispro or be a long lasting molecule such as glargine and detemir, these can also be used in combination with protamine, these premixed insulin’s provide a more sustained action (Bell, 2007). The combination of biotechnology and the pharmaceutical industry is a product of an evolution within technology and product innovation; which has become a result in advances within science and business practices. The biotechnology based products are thought of as intelligent pharmaceuticals as they often provide new modes and mechanisms in the action and approach to disease control with improved success rate and better patient care. (Evens Kaitin, 2014) Through the use of biochemical and genetic knowledge the production of insulin has become available and this industrial scale of therapeutic protein production is the first true application of recombinant DNA technology. (Swartz, 2001, Walsh, 2003) Although, as insulin is required in such high volumes the product yields of the vast amount of the currently available secretory systems are not currently sufficient enough to make it fully competitive. The current ideas and strategies being used to help improve the efficiency and producti vity of secretion are numerous. (Schmidt, 2004) Cultivation of insulin can be done conveniently within microbial cells such as bacteria and yeast. During the 80’s the FDA approved the use of human insulin produced from recombinant E.coli for the treatment of diabetes, this was the first recombinant protein pharmaceutical to enter the market. Thanks to the versatility and possibilities created through the use of recombinant protein production a large sector of opportunities for pharmaceutical companies opened up. (Ferrer-Miralles, et al., 2009) Since the approval of insulin in 1982 there are now currently more than 200 biotech products available commercially and research has expanded this to over 900 products being tested within clinical trials. Pharmaceuticals are engaged within the development of these products substantially as well as their commercialisation (Evens Kaitin, 2014). This acknowledges the fact that although the microbial systems lack the post translational modifications they are able to efficiently and conve niently produce functional mammalian recombinant proteins. Specific strains of many microbial species have now been created and adapted towards protein production; and the incorporation of yeasts and eukaryotic systems is now in place for protein production. (Ferrer-Miralles, et al., 2009). The use of E.coli expression system is the preferable choice for production of therapeutic proteins, amongst the 151 pharmaceuticals licensed in January 2009 30% where obtained in E.coli, this is due its ability to allow for efficient and economical production of proteins on both a lab scale and within industry (Mariusz, 2011, Swartz, 2001). During insulin production within E.coli the gene is fused with a synthetic fragment encoding for two IgG binding domains which have been derived from staphylococcal protein A. This product is then secreted into the growth medium of E.coli and purified using the IgG affinity. (Moks, et al., 1987) E.coli can be considered as the first microorganism for the production of proteins and is primarily used for genetic modification, cloning and small-scale production for research purposes. Many historical developments within molecular genetics and microbial physiology have been based within this species which has results in a collection of both information and molecular tools. (Ferrer-Miralles, et al., 2009) E.coli flourishes at a temperature of 37Â °C but the proteins are in insoluble form. Fusion protein technology has been able to increase the solubility of over expressed proteins, through the modification of selected amino acid residues allowing for the collection of soluble proteins (Zhang, et al., 1998). Due to the lack of the mechanisms to enable PTMs in bacterial cells protein maturation and disulfide bridges can be, to an extent overcome through the use of protein engineering (Mariusz, 2011). PTMs are crucial in protein folding, stability, processing and activity; therefore, proteins lacking the PMTs may be unstable, insoluble or inactive. However it is possible to synthetically bind PTMs to products, and through genetic engineering of DNA, the amino acid sequence of the polysaccharide can be changed to alter its properties this has been observed within insulin. (Ferrer-Miralles, et al., 2009) For more sophisticated modifications the genetic fusion of two proteins is required (Mariusz, 2011) An increase number of proteins being produced are engineered and tailored to display altered pharmacokinetic profiles and reduce immunogenicity. (Walsh, 2003) Even with the pharmaceutical market progressively producing more protein drugs from non-microbial systems; cell-free protein synthesis and oxidative cytoplasmic folding offers alternatives to the standard recombinant production techniques, it has not effect or impaired the development and progression of products developed within microbial systems proving the robustness of the microbial systems. (Ferrer-Miralles, et al., 2009, Swartz, 2001) In the future Radio Frequency Identification technology will play an important role; however there are some barriers in place for the pharmaceutical supply chain, as there have been concerns raised concerning the potential detrimental effect on the proteins due to the electromagnetic exposure. Alterations have been detected after the RFID however the effect and damages to the protein remain unknown (Acierno, et al., 2010) Works Cited Acierno, R. et al., 2010. Potential effects of RFID systems on biotechnology insulin preparation: A study using HPLC and NMR spectroscopy. Complex Medical Engineering (CME), pp. 198 203. Alberti, K. G. M. M., Zimmet, P. Shaw, J., 2006. Metabolic syndrome—a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabetic Medicine, 23(5), pp. 469-480. Bell, D., 2007. Insulin therapy in diabetes mellitus: how can the currently available injectable insulins be most prudently and efficaciously utilised?. Drugs, 67(13), pp. 1813-1827. Consulation, 1999. Definition, diagnosis and classification of diabetes mellitus and its complications. W. H. O., Volume 1. Evens, R. Kaitin, K., 2014. The Biotechnology Innovation Machine—A Source of Intelligent Biopharmaceuticals for the Pharma Industry: Mapping Biotechnology’s Success. [Pre press] submitted to: Clinical Pharmacology Therapeutics, Volume Last excessed, 27/03/2014, p. Avalible from: http://www.nature.com/clpt/journal/vaop/naam/abs/clpt201414a.html. Ferrer-Miralles, N. et al., 2009. Microbial factories for recombinant pharmaceuticals. Microbial Cell Factories , 8(7). Jonassen, I. et al., 2012. Design of the Novel Protraction Mechanism of Insulin Degludec, an Ultra-long-Acting Basal Insulin. [Online] Available at: http://link.springer.com/article/10.1007/s11095-012-0739-z/fulltext.html [Accessed 2014 March 27]. Lindholm, A., 2002. New insulins in the treatment of diabetes mellitus.. Best Pract Res Clin Gastroenterol, 16(3), pp. 475-92. Mariusz, K., 2011. Engineering of Therapeutic Proteins Production in Escherichia coli. Current Pharmaceutical Biotechnology, 12(2), pp. 268-274. Moks, T. et al., 1987. Large–Scale Affinity Purification of Human Insulin–Like Growth Factor I from Culture Medium of Escherichia Coli. Nature Biotechnology, Volume 5, pp. 379-382. Schmidt, F., 2004. Recombinant expression systems in the pharmaceutical industry. Applied Microbiology and Biotechnology, 65(4), pp. 363-372. Schmidt, M. et al., 1999. Markers of inflammation and prediction of diabetes mellitus in adults (Atherosclerosis Risk in Communities study): a cohort study. The Lancet, 353(9165), p. 1649–1652. Swartz, J., 2001. Advances in Escherichia coli production of therapeutic proteins. Current Opinion in Biotechnology, 12(2), pp. 195-201. Walsh, G., 2003. Pharmaceutical biotechnology products approved within the European Union. European Journal of Pharmaceutics and Biopharmaceutics, 55(1), pp. 3-10. Zhang, Y. et al., 1998. Expression of Eukaryotic Proteins in Soluble Form in Escherichia coli. Protein Expression and Purification, 12(2), pp. 159-165.

The Ethical Imperative †Contrarieties

The Ethical Imperative – Contrarieties â€Å"A global ethic is only practicable as a personal commitment,† says the author, Dalla Costa. He explains that for businesspeople, this does not mean valuing profit less, but instead valuing people more. Throughout the article, the author shows that business reflects who we are as a society and the beliefs that we live by as individuals. He uses several examples of organizations that have been hurt by unethical behavior to support his statement.Business leaders must assess their values and make appropriate changes since they operate in a global economy where market forces have left the human aspect weaker and the profit element skyrocketed. Dalla Costa attempts to convince businesses to pursue moral and ethical policies. He addresses the principle of right and wrong but emphasizes the importance of ethical behavior to long-term survival and profit. The article dissects the different characteristics attributed to those optimisti c and pessimistic.It describes the institutional pessimism of business, and explains how it is a product of fear – the fear of making mistake and of trying something new. The author argues that today's universal interdependence requires a global ethic – concern for the consumers, workers, and the environment of the overall community. He also discusses the pressures that lead to unethical behavior by individuals and organizations. He develops on five core fallacies that ground the pessimists' antipathy and prevent correction.In the article, Dalla Costa outlines the process for incorporating ethical principles to the direct benefit of customers, shareholders, employees and profits. The author makes clear why corporate ethics must be a fundamental component of any firm. As managers and consumers, many people are concerned about issues like discrimination in the workplace, and are struggling to integrate their beliefs into their jobs. The Ethical Imperative links these per sonal values to business performance. ’Costly though they may be, ethics are not an expenditure but an investment’’ (Dalla Costa, 1998). This article can be related to any business. [From Tesco’s point of view] as trust is essential among network actors, we believe to be optimistic is the best way to achieve ethical practices and reach trust between the firm and the market. Since industry, employer, and peer pressure are important factors influencing employees’ decisions, and since they do what they think is expected from them, we will work on modifying our business culture to build ethic and trust.Teams will be built to assess unethical issues, gather feedbacks and comments. This will in turn create a positive feedback loop. Also, Tesco will co-create supply chain transparency by 1. Demanding full transparency from its suppliers, 2. Working together with Tesco-Motorola-Food suppliers-Customers, and 3. Allowing customers to be true to their respect ive code of ethics.

Om Heizer Om10 Ism 04

Chapter FORECASTING Discussion Questions 1.? Qualitative models incorporate subjective factors into the forecasting model. Qualitative models are useful when subjective factors are important. When quantitative data are difficult to obtain, qualitative models may be appropriate. 2.? Approaches are qualitative and quantitative. Qualitative is relatively subjective; quantitative uses numeric models. 3.? Short-range (under 3 months), medium-range (3 months to 3 years), and long-range (over 3 years). 4.? The steps that should be used to develop a forecasting system are: (a)?Determine the purpose and use of the forecast (b)? Select the item or quantities that are to be forecasted (c)? Determine the time horizon of the forecast (d)? Select the type of forecasting model to be used (e)? Gather the necessary data (f)? Validate the forecasting model (g)? Make the forecast (h)? Implement and evaluate the results 5.? Any three of: sales planning, production planning and budgeting, cash budgeting, analyzing various operating plans. 6.? There is no mechanism for growth in these models; they are built exclusively from historical demand values. Such methods will always lag trends. .? Exponential smoothing is a weighted moving average where all previous values are weighted with a set of weights that decline exponentially. 8.? MAD, MSE, and MAPE are common measures of forecast accuracy. To find the more accurate forecasting model, forecast with each tool for several periods where the demand outcome is known, and calculate MSE, MAPE, or MAD for each. The smaller error indicates the better forecast. 9.? The Delphi technique involves: (a)? Assembling a group of experts in such a manner as to preclude direct communication between identifiable members of the group (b)?Assembling the responses of each expert to the questions or problems of interest (c)? Summarizing these responses (d)? Providing each expert with the summary of all responses (e)? Asking each expert to study the summary of the responses and respond again to the questions or problems of interest. (f)? Repeating steps (b) through (e) several times as necessary to obtain convergence in responses. If convergence has not been obtained by the end of the fourth cycle, the responses at that time should probably be accepted and the process terminated—little additional convergence is likely if the process is continued. 0.? A time series model predicts on the basis of the assumption that the future is a function of the past, whereas an associative model incorporates into the model the variables of factors that might influence the quantity being forecast. 11.? A time series is a sequence of evenly spaced data points with the four components of trend, seasonality, cyclical, and random variation. 12.? When the smoothing constant, (, is large (close to 1. 0), more weight is given to recent data; when ( is low (close to 0. 0), more weight is given to past data. 13.? Seasonal patterns are of fixed duration a nd repeat regularly.Cycles vary in length and regularity. Seasonal indices allow â€Å"generic† forecasts to be made specific to the month, week, etc. , of the application. 14.? Exponential smoothing weighs all previous values with a set of weights that decline exponentially. It can place a full weight on the most recent period (with an alpha of 1. 0). This, in effect, is the naive approach, which places all its emphasis on last period’s actual demand. 15.? Adaptive forecasting refers to computer monitoring of tracking signals and self-adjustment if a signal passes its present limit. 16.?Tracking signals alert the user of a forecasting tool to periods in which the forecast was in significant error. 17.? The correlation coefficient measures the degree to which the independent and dependent variables move together. A negative value would mean that as X increases, Y tends to fall. The variables move together, but move in opposite directions. 18.? Independent variable (x) is said to explain variations in the dependent variable (y). 19.? Nearly every industry has seasonality. The seasonality must be filtered out for good medium-range planning (of production and inventory) and performance evaluation. 20.? There are many examples.Demand for raw materials and component parts such as steel or tires is a function of demand for goods such as automobiles. 21.? Obviously, as we go farther into the future, it becomes more difficult to make forecasts, and we must diminish our reliance on the forecasts. Ethical Dilemma This exercise, derived from an actual situation, deals as much with ethics as with forecasting. Here are a few points to consider:  ¦ No one likes a system they don’t understand, and most college presidents would feel uncomfortable with this one. It does offer the advantage of depoliticizing the funds al- location if used wisely and fairly.But to do so means all parties must have input to the process (such as smoothing constants) and all data need to be open to everyone.  ¦ The smoothing constants could be selected by an agreed-upon criteria (such as lowest MAD) or could be based on input from experts on the board as well as the college.  ¦ Abuse of the system is tied to assigning alphas based on what results they yield, rather than what alphas make the most sense.  ¦ Regression is open to abuse as well. Models can use many years of data yielding one result or few years yielding a totally different forecast.Selection of associative variables can have a major impact on results as well. Active Model Exercises* ACTIVE MODEL 4. 1: Moving Averages 1.? What does the graph look like when n = 1? The forecast graph mirrors the data graph but one period later. 2.? What happens to the graph as the number of periods in the moving average increases? The forecast graph becomes shorter and smoother. 3.? What value for n minimizes the MAD for this data? n = 1 (a naive forecast) ACTIVE MODEL 4. 2: Exponential Smoothing 1.? Wha t happens to the graph when alpha equals zero? The graph is a straight line.The forecast is the same in each period. 2.? What happens to the graph when alpha equals one? The forecast follows the same pattern as the demand (except for the first forecast) but is offset by one period. This is a naive forecast. 3.? Generalize what happens to a forecast as alpha increases. As alpha increases the forecast is more sensitive to changes in demand. *Active Models 4. 1, 4. 2, 4. 3, and 4. 4 appear on our Web site, www. pearsonhighered. com/heizer. 4.? At what level of alpha is the mean absolute deviation (MAD) minimized? alpha = . 16 ACTIVE MODEL 4. 3: Exponential Smoothing with Trend Adjustment .? Scroll through different values for alpha and beta. Which smoothing constant appears to have the greater effect on the graph? alpha 2.? With beta set to zero, find the best alpha and observe the MAD. Now find the best beta. Observe the MAD. Does the addition of a trend improve the forecast? alpha = . 11, MAD = 2. 59; beta above . 6 changes the MAD (by a little) to 2. 54. ACTIVE MODEL 4. 4: Trend Projections 1.? What is the annual trend in the data? 10. 54 2.? Use the scrollbars for the slope and intercept to determine the values that minimize the MAD. Are these the same values that regression yields?No, they are not the same values. For example, an intercept of 57. 81 with a slope of 9. 44 yields a MAD of 7. 17. End-of-Chapter Problems [pic] (b) | | |Weighted | |Week of |Pints Used |Moving Average | |August 31 |360 | | |September 7 |389 |381 ( . 1 = ? 38. 1 | |September 14 |410 |368 ( . 3 = 110. 4 | |September 21 |381 |374 ( . 6 = 224. 4 | |September 28 |368 |372. | |October 5 |374 | | | |Forecast 372. 9 | | (c) | | | |Forecasting | Error | | |Week of |Pints |Forecast |Error |( . 20 |Forecast| |August 31 |360 |360 |0 |0 |360 | |September 7 |389 |360 |29 |5. 8 |365. 8 | |September 14 |410 |365. 8 |44. 2 |8. 84 |374. 64 | |September 21 |381 |374. 64 |6. 36 |1. 272 |375. 12 | |Se ptember 28 |368 |375. 912 |–7. 912 |–1. 5824 |374. 3296| |October 5 |374 |374. 3296 |–. 3296 |–. 06592 |374. 2636| The forecast is 374. 26. (d)? The three-year moving average appears to give better results. [pic] [pic] Naive tracks the ups and downs best but lags the data by one period. Exponential smoothing is probably better because it smoothes the data and does not have as much variation. TEACHING NOTE: Notice how well exponential smoothing forecasts the naive. [pic] (c)? The banking industry has a great deal of seasonality in its processing requirements [pic] b) | | |Two-Year | | | |Year |Mileage |Moving Average |Error ||Error| | |1 |3,000 | | | | | |2 |4,000 | | | | | |3 |3,400 |3,500 |–100 | |100 | |4 |3,800 |3,700 |100 | |100 | |5 |3,700 |3,600 |100 | |100 | | | |Totals| |100 | | |300 | | [pic] 4. 5? (c)? Weighted 2 year M. A. ith . 6 weight for most recent year. |Year |Mileage |Forecast |Error ||Error| | |1 |3,000 | | | | |2 |4,000 | | | | |3 |3,400 |3,600 |–200 |200 | |4 |3,800 |3,640 |160 |160 | |5 |3,700 |3,640 |60 |60 | | | | | | | 420 | | Forecast for year 6 is 3,740 miles. [pic] 4. 5? (d) | | |Forecast |Error ( |New | |Year |Mileage |Forecast |Error |( = . 50 |Forecast | |1 |3,000 |3,000 | ?0 | 0 |3,000 | |2 |4,000 |3,000 |1,000 |500 |3,500 | |3 |3,400 |3,500 | –100 |–50 |3,450 | |4 |3,800 |3,450 | 350 |175 |3,625 | |5 |3,700 |3,625 | 75 |? 38 |3,663 | | | |Total |1,325| | | | The forecast is 3,663 miles. 4. 6 |Y Sales |X Period |X2 |XY | |January |20 |1 |1 |20 | |February |21 |2 |4 |42 | |March |15 |3 |9 |45 | |April |14 |4 |16 |56 | |May |13 |5 |25 |65 | |June |16 |6 |36 |96 | |July |17 |7 |49 |119 | |August |18 |8 |64 |144 | |September |20 |9 |81 |180 | |October |20 |10 |100 |200 | |November |21 |11 |121 |231 | |December |23 |12 |144 |276 | |Sum | 18 |78 |650 |1,474 | |Average |? 18. 2 | 6. 5 | | | (a) [pic] (b)? [i]? NaiveThe coming January = December = 23 [ii]? 3-month moving (20 + 21 + 23)/3 = 21. 33 [iii]? 6-month weighted [(0. 1 ( 17) + (. 1 ( 18) + (0. 1 ( 20) + (0. 2 ( 20) + (0. 2 ( 21) + (0. 3 ( 23)]/1. 0 = 20. 6 [iv]? Exponential smoothing with alpha = 0. 3 [pic] [v]? Trend? [pic] [pic] Forecast = 15. 73? +?. 38(13) = 20. 67, where next January is the 13th month. (c)? Only trend provides an equation that can extend beyond one month 4. 7? Present = Period (week) 6. a) So: where [pic] )If the weights are 20, 15, 15, and 10, there will be no change in the forecast because these are the same relative weights as in part (a), i. e. , 20/60, 15/60, 15/60, and 10/60. c)If the weights are 0. 4, 0. 3, 0. 2, and 0. 1, then the forecast becomes 56. 3, or 56 patients. [pic] [pic] |Temperature |2 day M. A. | |Error||(Error)2| Absolute |% Error | |93 |— | — |— |— | |94 |— | — |— |— | |93 |93. 5 | 0. 5 |? 0. 25| 100(. 5/93) | = 0. 54% | |95 |93. 5 | 1. 5 | ? 2. 25| 100(1. 5/95) | = 1. 58% | |96 |94. 0 | 2. 0 |? 4. 0 0| 100(2/96) | = 2. 08% | |88 |95. 5 | 7. | 56. 25| 100(7. 5/88) | = 8. 52% | |90 |92. 0 | 2. 0 |? 4. 00| 100(2/90) | = 2. 22% | | | | |13. 5| | | 66. 75 | | |14. 94% | MAD = 13. 5/5 = 2. 7 (d)? MSE = 66. 75/5 = 13. 35 (e)? MAPE = 14. 94%/5 = 2. 99% 4. 9? (a, b) The computations for both the two- and three-month averages appear in the table; the results appear in the figure below. [pic] (c)? MAD (two-month moving average) = . 750/10 = . 075 MAD (three-month moving average) = . 793/9 = . 088 Therefore, the two-month moving average seems to have performed better. [pic] (c)? The forecasts are about the same. [pic] 4. 12? t |Day |Actual |Forecast | | | | |Demand |Demand | | |1 |Monday |88 |88 | | |2 |Tuesday |72 |88 | | |3 |Wednesday |68 |84 | | |4 |Thursday |48 |80 | | |5 |Friday | |72 |( Answer | Ft = Ft–1 + ((At–1 – Ft–1) Let ( = . 25. Let Monday forecast demand = 88 F2 = 88 + . 25(88 – 88) = 88 + 0 = 88 F3 = 88 + . 25(72 – 88) = 88 – 4 = 84 F4 = 84 + . 25(68 – 84) = 84 – 4 = 80 F5 = 80 + . 25(48 – 80) = 80 – 8 = 72 4. 13? (a)? Exponential smoothing, ( = 0. 6: | | |Exponential |Absolute | |Year |Demand |Smoothing ( = 0. |Deviation | |1 |45 |41 |4. 0 | |2 |50 |41. 0 + 0. 6(45–41) = 43. 4 |6. 6 | |3 |52 |43. 4 + 0. 6(50–43. 4) = 47. 4 |4. 6 | |4 |56 |47. 4 + 0. 6(52–47. 4) = 50. 2 |5. 8 | |5 |58 |50. 2 + 0. 6(56–50. 2) = 53. 7 |4. 3 | |6 |? |53. 7 + 0. 6(58–53. 7) = 56. 3 | | ( = 25. 3 MAD = 5. 06 Exponential smoothing, ( = 0. 9: | | |Exponential |Absolute | |Year |Demand |Smoothing ( = 0. |Deviation | |1 |45 |41 |4. 0 | |2 |50 |41. 0 + 0. 9(45–41) = 44. 6 |5. 4 | |3 |52 |44. 6 + 0. 9(50–44. 6 ) = 49. 5 |2. 5 | |4 |56 |49. 5 + 0. 9(52–49. 5) = 51. 8 |4. 2 | |5 |58 |51. 8 + 0. 9(56–51. 8) = 55. 6 |2. 4 | |6 |? |55. 6 + 0. 9(58–55. 6) = 57. 8 | | ( = 18. 5 MAD = 3. 7 (b)? 3-year moving average: | | |Three-Year |Absolute | |Year |Demand |Moving Average |Deviation | |1 45 | | | |2 |50 | | | |3 |52 | | | |4 |56 |(45 + 50 + 52)/3 = 49 |7 | |5 |58 | (50 + 52 + 56)/3 = 52. 7 |5. 3 | |6 |? | (52 + 56 + 58)/3 = 55. 3 | | ( = 12. 3 MAD = 6. 2 (c)? Trend projection: | | | |Absolute | |Year |Demand |Trend Projection |Deviation | |1 |45 |42. 6 + 3. 2 ( 1 = 45. 8 |0. 8 | |2 |50 |42. 6 + 3. 2 ( 2 = 49. 0 |1. 0 | |3 |52 |42. 6 + 3. 2 ( 3 = 52. 2 |0. 2 | |4 |56 |42. 6 + 3. 2 ( 4 = 55. 4 |0. | |5 |58 |42. 6 + 3. 2 ( 5 = 58. 6 |0. 6 | |6 |? |42. 6 + 3. 2 ( 6 = 61. 8 | | ( = 3. 2 MAD = 0. 64 [pic] | X |Y |XY |X2 | | 1 |45 | 45 | 1 | | 2 |50 |100 | 4 | | 3 |52 |156 | 9 | | 4 |56 |224 |16 | | 5 |58 |290 |25 | Then: (X = 15, (Y = 261, (XY = 815, (X2 = 55, [pic]= 3, [pic]= 52. 2 Therefore: [pic] (d)? Comparing the results of the forecasting methodologies for parts (a), (b), and (c). |Forecast Methodology |MAD | |Exponential smoothing, ( = 0. |5. 06 | |Exponential smoothing, ( = 0. 9 |3. 7 | |3-year moving average |6. 2 | |Trend projection |0. 64 | Based on a mean absolute deviation criterion, the trend projection is to be preferred over the exponential smoothing with ( = 0. 6, exponential smoothing with ( = 0. 9, or the 3-year moving average forecast methodologies. 4. 14 Method 1:MAD: (0. 20 + 0. 05 + 0. 05 + 0. 20)/4 = . 125 ( better MSE : (0. 04 + 0. 0025 + 0. 0025 + 0. 04)/4 = . 021 Method 2:MAD: (0. 1 + 0. 20 + 0. 10 + 0. 11) / 4 = . 1275 MSE : (0. 01 + 0. 04 + 0. 01 + 0. 0121) / 4 = . 018 ( better 4. 15 | |Forecast Three-Year |Absolute | |Year |Sales |Moving Average |Deviation | |2005 |450 | | | |2006 |495 | | | |2007 |518 | | | |2008 |563 |(450 + 495 + 518)/3 = 487. 7 |75. 3 | |2009 |584 |(495 + 518 + 563)/3 = 525. 3 |58. 7 | |2010 | |(518 + 563 + 584)/3 = 555. 0 | | | | | ( = 134 | | | | MAD = 67 | 4. 16 Year |Time Period X |Sales Y |X2 |XY | |2005 |1 |450 | 1 |450 | |2006 |2 |495 | 4 |990 | |2007 |3 |518 | 9 |1554 | |2008 |4 |563 |16 |2252 | |2009 |5 |584 |25 |2920 | | | | ( = 2610| |( = 55 | |( = 8166 | [pic] [pic] |Year |Sales |Forecast Trend |Absolute Deviation | |2005 |450 |454. 8 |4. 8 | |2006 |495 |488. 4 |6. | |2007 |518 |522. 0 |4. 0 | |2008 |563 |555. 6 |7. 4 | |2009 |584 |589. 2 |5. 2 | |2010 | |622. 8 | | | | | | ( = 28 | | | | | MAD = 5. 6 | 4. 17 | | |Forecast Exponential |Absolute | |Year |Sales |Smoothing ( = 0. 6 |Deviation | |2005 |450 |410. 0 |40. | |2006 |495 |410 + 0. 6(450 – 410) = 434. 0 |61. 0 | |2007 |518 |434 + 0. 6(495 – 434) = 470. 6 |47. 4 | |2008 |563 |470. 6 + 0. 6(518 – 470. 6) = 499. 0 |64. 0 | |2009 |584 |499 + 0. 6(563 – 499) = 537. 4 |46. 6 | |2010 | |537. 4 + 0. 6(584 – 537. 4) = 565. 6 | | | | | ( = 259 | | | | MAD = 51. 8 | | | |Forecast Exponential |Absolute | |Year |Sales |Smoothing ( = 0. |Deviation | |2005 |450 |410. 0 |40. 0 | |2006 |495 |410 + 0. 9(450 – 410) = 446. 0 |49. 0 | |2007 |518 |446 + 0. 9(495 – 446) = 490. 1 |27. 9 | |2008 |563 |490. 1 + 0. 9(518 – 490. 1) = 515. 2 |47. 8 | |2009 |584 |515. 2 + 0. 9(563 – 515. 2) = 558. 2 |25. 8 | |2010 | |558. 2 + 0. 9(584 – 558. 2) = 581. 4 | | | | |( = 190. 5 | | | |MAD = 38. 1 | (Refer to Solved Problem 4. 1)For ( = 0. 3, absolute deviations for 2005–2009 are 40. 0, 73. 0, 74. 1, 96. 9, 88. 8, respectively. So the MAD = 372. 8/5 = 74. 6. [pic] Because it gives the lowest MAD, the smoothing constant of ( = 0. 9 gives the most accurate forecast. 4. 18? We need to find the smoothing constant (. We know in general that Ft = Ft–1 + ((At–1 – Ft–1); t = 2, 3, 4. Choose either t = 3 or t = 4 (t = 2 won’t let us find ( because F2 = 50 = 50 + ((50 – 50) holds for any (). Let’s pick t = 3. Then F3 = 48 = 50 + ((42 – 50) or 48 = 50 + 42( – 50( or –2 = –8( So, . 25 = ( Now we can find F5 : F5 = 50 + ((46 – 50)F5 = 50 + 46( – 50( = 50 – 4( For ( = . 25, F5 = 50 – 4(. 25) = 49 The forecast for time period 5 = 49 units. 4. 19? Trend adjusted exponential smoothing: ( = 0. 1, ( = 0. 2 | | |Unadjusted | |Adjusted | | | |Month |Income |Forecast |Trend |Forecast ||Error||Error2 | |February |70. 0 | 65. 0 | 0. 0 | 65 |? 5. 0 |? 25. 0 | |March |68. 5 | 65. 5 | 0. 1 | 65. 6 |? 2. 9 |? 8. 4 | |April |64. 8 | 65. 9 | 0. 16 |66. 05 |? 1. 2 |? 1. 6 | |May |71. 7 | 65. 92 | 0. 13 |66. 06 |? 5. 6 |? 31. 9 | |June |71. | 66. 62 | 0. 25 |66. 87 |? 4. 4 |? 19. 7 | |July |72. 8 | 67. 31 | 0. 33 |67. 64 |? 5. 2 |? 26. 6 | |August | | 68. 16 | |68. 60 | |24. 3| | |113. 2| | MAD = 24. 3/6 = 4. 05, MSE = 113. 2/6 = 18. 87. Note that all numbers are rounded. Note: To use POM for Windows to solve this problem, a period 0, which contains the initial forecast and initial trend, must be added. 4. 20? Trend adjusted exponential smoothing: ( = 0. 1, ( = 0. 8 [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] 4. 23? Students must determine the naive forecast for the four months .The naive forecast for March is the February actual of 83, etc. |(a) | |Actual |Forecast ||Error| ||% Error| | | |March |101 |120 |19 |100 (19/101) = 18. 81% | | |April |? 96 |114 |18 |100 (18/96) ? = 18. 75% | | |May |? 89 |110 |21 |100 (21/89) ? = 23. 60% | | |June |108 |108 |? 0 |100 (0/108) ? = 0% | | | | | | |58 | | | 61. 16% | [pic] |(b)| |Actual |Naive ||Error| ||% Error| | | |March |101 |? 83 |18 |100 (18/101) = 17. 82% | | |April |? 96 |101 |? |100 (5/96) ? = 5. 21% | | |May |? 89 |? 96 |? 7 |100 (7/89) ? =? 7. 87% | | |June |108 |? 89 |19 |100 (19/108) = 17. 59% | | | | | | |49| | |48. 49% | | [pic] Naive outperforms management. (c)? MAD for the manager’s technique is 14. 5, while MAD for the naive forecast is only 12. 25. MAPEs are 15. 29% and 12. 12%, respectively. So the naive method is better. 4. 24? (a)? Graph of demand The observations obviously do not form a straight line but do tend to cluster about a straight line over the range shown. (b)? Least-squares regression: [pic] Assume Appearances X |Demand Y |X2 |Y2 |XY | |3 | 3 | 9 | 9 | 9 | |4 | 6 |16 | 36 |24 | |7 | 7 |49 | 49 |49 | |6 | 5 |36 | 25 |30 | |8 |10 |64 |100 |80 | |5 | 7 |25 | 49 |35 | |9 | ? | | | | (X = 33, (Y = 38, (XY = 227, (X2 = 199, [pic]= 5. 5, [pic]= 6. 33. Therefore: [pic] The following figure shows both the data and the resulting equation: [pic] (c) If there are nine performances by Stone Temple Pilots, the estimated sales are: (d) R = . 82 is the correlation coefficient, and R2 = . 68 means 68% of the variation in sales can be explained by TV appearances. 4. 25? |Number of | | | | | |Accidents | | | | |Month |(y) |x |xy |x2 | |January | 30 | 1 | 30 | 1 | |February | 40 | 2 | 80 | 4 | |March | 60 | 3 |180 | 9 | |April | 90 | 4 |360 |16 | |? Totals | |220 | | | [pic] The regression line is y = 5 + 20x. The forecast for May (x = 5) is y = 5 + 20(5) = 105. 4. 26 |Season |Year1 |Year2 |Average |Average |Seasonal |Year3 | | |Demand |Demand |Year1(Year2 |Season |Index |Demand | | | | |Demand |Demand | | | |Fall |200 |250 |225. 0 |250 |0. 90 |270 | |Winter |350 |300 |325. |250 |1. 30 |390 | |Spring |150 |165 |157. 5 |250 |0. 63 |189 | |Summer |300 |285 |292. 5 |250 |1. 17 |351 | 4. 27 | | Winter |Spring |Summer |Fall | |2006 |1,400 |1,500 |1,000 |600 | |2007 |1,200 |1,400 |2,100 |750 | |2008 |1,000 |1,600 |2,000 |650 | |2009 | 900 |1,500 |1,900 | 500 | | |4,500 |6,000 |7,000 |2,500 | 4. 28 | | | | |Average | | | | | | |Average |Quarterly |Seasonal | |Quarter |2007 |2008 |2009 |Demand |Demand |Index | |Winter | 73 | 65 | 89 | 75. 67 |106. 67 |0. 709 | |Spring |104 | 82 |146 |110. 67 |106. 67 |1. 037 | |Summer |168 |124 |205 |165. 67 |106. 67 |1. 553 | |Fall | 74 | 52 | 98 | 74. 67 |106. 67 |0. 700 | 4. 29? 2011 is 25 years beyond 1986. Therefore, the 2011 quarter numbers are 101 through 104. | | | | |(5) | | |(2) |(3) |(4) |Adjusted | |(1) |Quarter |Forecast |Seasonal |Forecast | |Quarter |Number |(77 + . 3Q) |Factor |[(3) ( (4)] | |Winter |101 |12 0. 43 | . 8 | 96. 344 | |Spring |102 |120. 86 |1. 1 |132. 946 | |Summer |103 |121. 29 |1. 4 |169. 806 | |Fall |104 |121. 72 | . 7 | 85. 204 | 4. 30? Given Y = 36 + 4. 3X (a) Y = 36 + 4. 3(70) = 337 (b) Y = 36 + 4. 3(80) = 380 (c) Y = 36 + 4. 3(90) = 423 4. 31 4. 33? (a)? See the table below. For next year (x = 6), the number of transistors (in millions) is forecasted as y = 126 + 18(6) = 126 + 108 = 234. Then y = a + bx, where y = number sold, x = price, and |4. 32? a) | x |y |xy |x2 | | | 16 | 330 | 5,280 |256 | | | 12 | 270 | 3,240 |144 | | | 18 | 380 | 6,840 |324 | | | 14 | 300 | 4,200 |196 | | | 60 |1,280 |19,560 |920 | So at x = 2. 80, y = 1,454. 6 – 277. 6($2. 80) = 677. 32. Now round to the nearest integer: Answer: 677 lattes. [pic] (b)? If the forecast is for 20 guests, the bar sales forecast is 50 + 18(20) = $410. Each guest accounts for an additional $18 in bar sales. |Table for Problem 4. 33 | | | | | |Year |Transistors | | | | | | | |(x) |(y) |xy |x2 |126 + 18x |E rror |Error2 ||% Error| | | |? 1 |140 |? 140 |? 1 |144 |–4 |? 16 |100 (4/140)? = 2. 86% | | |? 2 |160 |? 320 |? 4 |162 |–2 | 4 |100 (2/160)? = 1. 25% | | |? 3 |190 |? 570 |? 9 |180 |10 |100 |100 (10/190) = 5. 26% | | |? 4 |200 |? 800 |16 |198 |? 2 | 4 |100 (2/200) = 1. 00% | | |? |210 |1,050 |25 |216 |–6 |? 36 |100 (6/210)? = 2. 86% | |Totals |15 | | |900 | | |2,800 | | (b)? MSE = 160/5 = 32 (c)? MAPE = 13. 23%/5 = 2. 65% 4. 34? Y = 7. 5 + 3. 5X1 + 4. 5X2 + 2. 5X3 (a)? 28 (b)? 43 (c)? 58 4. 35? (a)? [pic] = 13,473 + 37. 65(1860) = 83,502 (b)? The predicted selling price is $83,502, but this is the average price for a house of this size. There are other factors besides square footage that will impact the selling price of a house. If such a house sold for $95,000, then these other factors could be contributing to the additional value. (c)?Some other quantitative variables would be age of the house, number of bedrooms, size of the lot, and size of the garage, etc. (d)? Coefficient of determination = (0. 63)2 = 0. 397. This means that only about 39. 7% of the variability in the sales price of a house is explained by this regression model that only includes square footage as the explanatory variable. 4. 36? (a)? Given: Y = 90 + 48. 5X1 + 0. 4X2 where: [pic] If: Number of days on the road ( X1 = 5 and distance traveled ( X2 = 300 then: Y = 90 + 48. 5 ( 5 + 0. 4 ( 300 = 90 + 242. 5 + 120 = 452. 5 Therefore, the expected cost of the trip is $452. 50. (b)? The reimbursement request is much higher than predicted by the model. This request should probably be questioned by the accountant. (c)?A number of other variables should be included, such as: 1.? the type of travel (air or car) 2.? conference fees, if any 3.? costs of entertaining customers 4.? other transportation costs—cab, limousine, special tolls, or parking In addition, the correlation coefficient of 0. 68 is not exceptionally high. It indicates that the model explains approximately 46% of the overall variation in trip cost. This correlation coefficient would suggest that the model is not a particularly good one. 4. 37? (a, b) |Period |Demand |Forecast |Error |Running sum ||error| | | 1 |20 |20 |0. 00 |0. 00 |0. 00 | | 2 |21 |20 |1. 00 |1. 0 |1. 00 | | 3 |28 |20. 5 |7. 50 |8. 50 |7. 50 | | 4 |37 |24. 25 |12. 75 |21. 25 |12. 75 | | 5 |25 |30. 63 |–5. 63 |15. 63 |5. 63 | | 6 |29 |27. 81 |1. 19 |16. 82 |1. 19 | | 7 |36 |28. 41 |7. 59 |24. 41 |7. 59 | | 8 |22 |32. 20 |–10. 20 |14. 21 |10. 20 | | 9 |25 |27. 11 |–2. 10 |12. 10 |2. 10 | |10 |28 |26. 05 | 1. 95 |14. 05 | | | | | | |1. 95 | | | | | | | | | | | | | | | |MAD[pic]5. 00 | Cumulative error = 14. 05; MAD = 5? Tracking = 14. 05/5 ( 2. 82 4. 38? (a)? least squares equation: Y = –0. 158 + 0. 1308X (b)? Y = –0. 158 + 0. 1308(22) = 2. 719 million (c)? coefficient of correlation = r = 0. 966 coefficient of determination = r2 = 0. 934 4. 39 |Year X |Patients Y |X2 |Y2 |XY | |? 1 |? 36 | 1 |? 1,296 | 36 | |? 2 |? 33 | |? 1,089 | 66 | |? 3 |? 40 | 9 |? 1,600 |? 120 | |? 4 |? 41 |? 16 |? 1,681 |? 164 | |? 5 |? 40 |? 25 |? 1,600 |? 200 | |? 6 |? 55 |? 36 |? 3,025 |? 330 | |? 7 |? 60 |? 49 |? 3,600 |? 420 | |? 8 |? 54 |? 64 |? 2,916 |? 432 | |? 9 |? 58 |? 81 |? 3,364 |? 522 | |10 |? 61 |100 |? 3,721 |? 10 | |55 | | |478 | | |X |Y |Forecast |Deviation |Deviation | |? 1 |36 |29. 8 + 3. 28 ( ? 1 = 33. 1 |? 2. 9 |2. 9 | |? 2 |33 |29. 8 + 3. 28 ( ? 2 = 36. 3 |–3. 3 |3. 3 | |? 3 |40 |29. 8 + 3. 28 ( ? 3 = 39. 6 |? 0. 4 |0. 4 | |? 4 |41 |29. 8 + 3. 28 ( ? 4 = 42. 9 |–1. 9 |1. 9 | |? 5 |40 |29. 8 + 3. 28 ( ? 5 = 46. 2 |–6. 2 |6. 2 | |? 6 |55 |29. 8 + 3. 28 ( ? 6 = 49. 4 |? 5. 6 |5. 6 | |? 7 |60 |29. 8 + 3. 28 ( ? 7 = 52. 7 |? 7. 3 |7. 3 | |? |54 |29. 8 + 3. 28 ( ? 8 = 56. 1 |–2. 1 |2. 1 | |? 9 |58 |29. 8 + 3. 28 ( ? 9 = 59. 3 |–1. 3 |1. 3 | |10 |61 |29. 8 + 3. 28 ( 10 = 62. 6 |–1. 6 |1. 6 | | | | | | ( = | | | | | |32. 6 | | | | | |MAD = 3. 26 | The MAD is 3. 26—this is approximately 7% of the average number of patients and 10% of the minimum number of patients. We also see absolute deviations, for years 5, 6, and 7 in the range 5. 6–7. 3.The comparison of the MAD with the average and minimum number of patients and the comparatively large deviations during the middle years indicate that the forecast model is not exceptionally accurate. It is more useful for predicting general trends than the actual number of patients to be seen in a specific year. 4. 40 | |Crime |Patients | | | | |Year |Rate X |Y |X2 |Y2 |XY | |? 1 |? 58. 3 |? 36 |? 3,398. 9 |? 1,296 |? 2,098. 8 | |? 2 |? 61. 1 |? 33 |? 3,733. 2 |? 1,089 |? 2,016. 3 | |? 3 |? 73. |? 40 |? 5,387. 6 |? 1,600 |? 2,936. 0 | |? 4 |? 75. 7 |? 41 |? 5,730. 5 |? 1,681 |? 3,103. 7 | |? 5 |? 81. 1 |? 40 |? 6,577. 2 |? 1,600 |? 3,244. 0 | |? 6 |? 89. 0 |? 55 |? 7,921. 0 |? 3,025 |? 4,895. 0 | |? 7 |101. 1 |? 60 |10,221. 2 |? 3,600 |? 6,066. 0 | |? 8 |? 94 . 8 |? 54 |? 8,987. 0 |? 2,916 |? 5,119. 2 | |? 9 |103. 3 |? 58 |10,670. 9 |? 3,364 |? 5,991. 4 | |10 |116. 2 |? 61 |13,502. 4 |? 3,721 |? 7,088. 2 | |Column | |854. | | |478 | |Totals | | | | | | |months) |(Millions) |(1,000,000s) | | | | |Year |(X) |(Y) |X2 |Y2 |XY | |? 1 |? 7 |1. 5 |? 49 |? 2. 25 |10. 5 | |? 2 |? 2 |1. 0 | 4 |? 1. 00 |? 2. 0 | |? 3 |? 6 |1. 3 |? 36 |? 1. 69 |? 7. 8 | |? 4 |? 4 |1. 5 |? 16 |? 2. 25 |? 6. 0 | |? 5 |14 |2. 5 |196 |? 6. 25 |35. 0 | |? 6 |15 |2. 7 |225 |? 7. 9 |40. 5 | |? 7 |16 |2. 4 |256 |? 5. 76 |38. 4 | |? 8 |12 |2. 0 |144 |? 4. 00 |24. 0 | |? 9 |14 |2. 7 |196 |? 7. 29 |37. 8 | |10 |20 |4. 4 |400 |19. 36 |88. 0 | |11 |15 |3. 4 |225 |11. 56 |51. 0 | |12 |? 7 |1. 7 |? 49 |? 2. 89 |11. 9 | Given: Y = a + bX where: [pic] and (X = 132, (Y = 27. 1, (XY = 352. 9, (X2 = 1796, (Y2 = 71. 59, [pic] = 11, [pic]= 2. 26. Then: [pic] andY = 0. 511 + 0. 159X (c)?Given a tourist population of 10,000,000, the model predicts a ridership of: Y = 0. 511 + 0. 159 ( 10 = 2. 101, or 2,101,000 persons. (d)? If there are no tourists at all, the model predicts a ridership of 0. 511, or 511,000 persons. One would not place much confidence in this forecast, however, because the number of tourists (zero) is outside the range of data used to develop the model. (e)? The standard error of the estimate is given by: (f)? The correlation coefficient and the coefficient of determination are given by: [pic] 4. 42? (a)? This problem gives students a chance to tackle a realistic problem in business, i. e. , not enough data to make a good forecast.As can be seen in the accompanying figure, the data contains both seasonal and trend factors. [pic] Averaging methods are not appropriate with trend, seasonal, or other patterns in the data. Moving averages smooth out seasonality. Exponential smoothing can forecast January next year, but not farther. Because seasonality is strong, a naive model that students create on their own might be best. (b) One model might be: Ft+1 = At–11 That is forecastnext period = actualone year earlier to account for seasonality. But this ignores the trend. One very good approach would be to calculate the increase from each month last year to each month this year, sum all 12 increases, and divide by 12.The forecast for next year would equal the value for the same month this year plus the average increase over the 12 months of last year. (c) Using this model, the January forecast for next year becomes: [pic] where 148 = total monthly increases from last year to this year. The forecasts for each of the months of next year then become: |Jan. |29 | |July. |56 | |Feb. |26 | |Aug. |53 | |Mar. |32 | |Sep. |45 | |Apr. |35 | |Oct. |35 | |May. |42 | |Nov. |38 | |Jun. |50 | |Dec. |29 | Both history and forecast for the next year are shown in the accompanying figure: [pic] 4. 3? (a) and (b) See the following table: | |Actual |Smoothed | |Smoothed | | |Week |Value |Value |Forecast |Value |Forecast | |t |A(t) |Ft (( = 0. 2) |Err or |Ft (( = 0. 6)|Error | | 1 |50 |+50. 0 |? +0. 0 |+50. 0 |? +0. 0 | | 2 |35 |+50. 0 |–15. 0 |+50. 0 |–15. 0 | | 3 |25 |+47. 0 |–22. 0 |+41. 0 |–16. 0 | | 4 |40 |+42. 6 |? –2. 6 |+31. 4 |? +8. 6 | | 5 |45 |+42. 1 |? –2. 9 |+36. 6 |? +8. | | 6 |35 |+42. 7 |? –7. 7 |+41. 6 |? –6. 6 | | 7 |20 |+41. 1 |–21. 1 |+37. 6 |–17. 6 | | 8 |30 |+36. 9 |? –6. 9 |+27. 1 |? +2. 9 | | 9 |35 |+35. 5 |? –0. 5 |+28. 8 |? +6. 2 | |10 |20 |+35. 4 |–15. 4 |+32. 5 |–12. 5 | |11 |15 |+32. 3 |–17. 3 |+25. 0 |–10. 0 | |12 |40 |+28. 9 |+11. 1 |+19. 0 |+21. 0 | |13 |55 |+31. 1 |+23. 9 |+31. 6 |+23. 4 | |14 |35 |+35. 9 |? 0. 9 |+45. 6 |–10. 6 | |15 |25 |+36. 7 |–10. 7 |+39. 3 |–14. 3 | |16 |55 |+33. 6 |+21. 4 |+30. 7 |+24. 3 | |17 |55 |+37. 8 |+17. 2 |+45. 3 |? +9. 7 | |18 |40 |+41. 3 |? –1. 3 |+51. 1 |–11. 1 | |19 |35 |+41. 0 |? –6. 0 |+44. 4 |? –9. 4 | |20 |60 |+39. 8 |+20. 2 |+38. 8 |+21. 2 | |21 |75 |+43. 9 |+31. 1 |+51. 5 |+23. 5 | |22 |50 |+50. 1 |? –0. 1 |+65. 6 |–15. | |23 |40 |+50. 1 |–10. 1 |+56. 2 |–16. 2 | |24 |65 |+48. 1 |+16. 9 |+46. 5 |+18. 5 | |25 | |+51. 4 | |+57. 6 | | | | |MAD = 11. 8 |MAD = 13. 45 | (c)? Students should note how stable the smoothed values are for ( = 0. 2. When compared to actual week 25 calls of 85, the smoothing constant, ( = 0. 6, appears to do a slightly better job. On the basis of the standard error of the estimate and the MAD, the 0. 2 constant is better. However, other smoothing constants need to be examined. |4. 4 | | | | | | |Week |Actual Value |Smoothed Value |Trend Estimate |Forecast |Forecast | |t |At |Ft (( = 0. 3) |Tt (( = 0. 2) |FITt |Error | |? 1 |50. 000 |50. 000 |? 0. 000 |50. 000 | 0. 000 | |? 2 |35. 000 |50. 000 |? 0. 000 |50. 000 |–15. 000 | |? 3 |25. 000 |45. 500 |–0. 900 |44. 600 |–19. 600 | |? 4 |40. 000 |38. 720 |– 2. 076 |36. 644 | 3. 56 | |? 5 |45. 000 |37. 651 |–1. 875 |35. 776 | 9. 224 | |? 6 |35. 000 |38. 543 |–1. 321 |37. 222 |? –2. 222 | |? 7 |20. 000 |36. 555 |–1. 455 |35. 101 |–15. 101 | |? 8 |30. 000 |30. 571 |–2. 361 |28. 210 | 1. 790 | |? 9 |35. 000 |28. 747 |–2. 253 |26. 494 | 8. 506 | |10 |20. 000 |29. 046 |–1. 743 |27. 03 |? –7. 303 | |11 |15. 000 |25. 112 |–2. 181 |22. 931 |? –7. 931 | |12 |40. 000 |20. 552 |–2. 657 |17. 895 |? 22. 105 | |13 |55. 000 |24. 526 |–1. 331 |23. 196 |? 31. 804 | |14 |35. 000 |32. 737 |? 0. 578 |33. 315 | 1. 685 | |15 |25. 000 |33. 820 |? 0. 679 |34. 499 |? –9. 499 | |16 |55. 000 |31. 649 |? 0. 109 |31. 58 |? 23. 242 | |17 |55. 000 |38. 731 |? 1. 503 |40. 234 |? 14. 766 | |18 |40. 000 |44. 664 |? 2. 389 |47. 053 |? –7. 053 | |19 |35. 000 |44. 937 |? 1. 966 |46. 903 |–11. 903 | |20 |60. 000 |43. 332 |? 1. 252 |44. 584 |? 15. 416 | |21 |75. 00 0 |49. 209 |? 2. 177 |51. 386 |? 23. 614 | |22 |50. 000 |58. 470 |? 3. 94 |62. 064 |–12. 064 | |23 |40. 000 |58. 445 |? 2. 870 |61. 315 |–21. 315 | |24 |65. 000 |54. 920 |? 1. 591 |56. 511 | 8. 489 | |25 | |59. 058 |? 2. 100 |61. 158 | | To evaluate the trend adjusted exponential smoothing model, actual week 25 calls are compared to the forecasted value. The model appears to be producing a forecast approximately mid-range between that given by simple exponential smoothing using ( = 0. 2 and ( = 0. 6.Trend adjustment does not appear to give any significant improvement. 4. 45 |Month |At |Ft ||At – Ft | |(At – Ft) | |May |100 |100 | 0 | 0 | |June | 80 |104 |24 |–24 | |July |110 | 99 |11 |11 | |August |115 |101 |14 |14 | |September |105 |104 | 1 | 1 | |October |110 |104 |6 |6 | |November |125 |105 |20 |20 | December |120 |109 |11 |11 | | | | |Sum: 87 |Sum: 39 | |4. 46 (a) | |X |Y |X2 |Y2 |XY | | |? 421 |? 2. 90 |? 177241 | 8. 41 |? 1220. 9 | | |? 377 | ? 2. 93 |? 142129 | 8. 58 |? 1104. 6 | | |? 585 |? 3. 00 |? 342225 | 9. 00 |? 1755. 0 | | |? 690 |? 3. 45 |? 476100 |? 11. 90 |? 2380. 5 | | |? 608 |? 3. 66 |? 369664 |? 13. 40 |? 2225. 3 | | |? 390 |? 2. 88 |? 52100 | 8. 29 |? 1123. 2 | | |? 415 |? 2. 15 |? 172225 | 4. 62 | 892. 3 | | |? 481 |? 2. 53 |? 231361 | 6. 40 |? 1216. 9 | | |? 729 |? 3. 22 |? 531441 |? 10. 37 |? 2347. 4 | | |? 501 |? 1. 99 |? 251001 | 3. 96 | 997. 0 | | |? 613 |? 2. 75 |? 375769 | 7. 56 |? 1685. 8 | | |? 709 |? 3. 90 |? 502681 |? 15. 21 |? 2765. 1 | | |? 366 |? 1. 60 |? 133956 | 2. 56 | 585. 6 | | |Column |6885 | |36. 6 | | | |totals | | | | | |January |400 |— |— | — |— | |February |380 |400 |— |20. 0 |— | |March |410 |398 |— |12. 0 |— | |April |375 | 399. 2 |396. 67 |24. 2 |21. 67 | |May |405 | 396. 8 |388. 33 |8. 22 |16. 67 | | | | |MAD = | |16. 11| | |19. 17| | (d)Note that Amit has more forecast observations, while Barbara’s moving average does not start until month 4. Also note that the MAD for Amit is an average of 4 numbers, while Barbara’s is only 2. Amit’s MAD for exponential smoothing (16. 1) is lower than that of Barbara’s moving average (19. 17). So his forecast seems to be better. 4. 48? (a) |Quarter |Contracts X |Sales Y |X2 |Y2 |XY | |1 |? 153 |? 8 |? 23,409 |? 64 |? 1,224 | |2 |? 172 |10 |? 29,584 |100 |? 1,720 | |3 |? 197 |15 |? 38,809 |225 |? 2,955 | |4 |? 178 |? 9 |? 31,684 |? 81 |? 1,602 | |5 |? 185 |12 |? 34,225 |144 |? 2,220 | |6 |? 199 |13 |? 39,601 |169 |? 2,587 | |7 |? 205 |12 |? 42,025 |144 |? ,460 | |8 |? 226 |16 |? 51,076 |256 |? 3,616 | |Totals | | 1,515 | | |95 | b = (18384 – 8 ( 189. 375 ( 11. 875)/(290,413 – 8 ( 189. 375 ( 189. 375) = 0. 1121 a = 11. 875 – 0. 1121 ( 189. 375 = –9. 3495 Sales ( y) = –9. 349 + 0. 1121 (Contracts) (b) [pic] 4. 49? (a) |Method ( Exponential Smoothing | | | |0. 6 = ( | | | |Year |Deposits (Y) |Forecast ||E rror| |Error2 | | 1 |? 0. 25 |0. 25 |0. 00 |? 0. 00 | | 2 |? . 24 |0. 25 |0. 01 |? 0. 0001 | | 3 |? 0. 24 |0. 244 |0. 004 |? 0. 0000 | | 4 |? 0. 26 |0. 241 |0. 018 |? 0. 0003 | | 5 |? 0. 25 |0. 252 |0. 002 |? 0. 00 | | 6 |? 0. 30 |0. 251 |0. 048 |? 0. 0023 | | 7 |? 0. 31 |0. 280 |0. 029 |? 0. 0008 | | 8 |? 0. 32 |0. 298 |0. 021 |? 0. 0004 | | 9 |? 0. 24 |0. 311 |0. 071 |? 0. 0051 | |10 |? 0. 26 |0. 68 |0. 008 |? 0. 0000 | |11 |? 0. 25 |0. 263 |0. 013 |? 0. 0002 | |12 |? 0. 33 |0. 255 |0. 074 |? 0. 0055 | |13 |? 0. 50 |0. 300 |0. 199 |? 0. 0399 | |14 |? 0. 95 |0. 420 |0. 529 |? 0. 2808 | |15 |? 1. 70 |0. 738 |0. 961 |? 0. 925 | |16 |? 2. 30 |1. 315 |0. 984 |? 0. 9698 | |17 |? 2. 80 |1. 906 |0. 893 |? 0. 7990 | |18 |? 2. 80 |2. 442 |0. 357 |? 0. 278 | |19 |? 2. 70 |2. 656 |0. 043 |? 0. 0018 | |20 |? 3. 90 |2. 682 |1. 217 |? 1. 4816 | |21 |? 4. 90 |3. 413 |1. 486 |? 2. 2108 | |22 |? 5. 30 |4. 305 |0. 994 |? 0. 9895 | |23 |? 6. 20 |4. 90 |1. 297 |? 1. 6845 | |24 |? 4. 10 |5. 680 |1. 580 |? 2. 499 | |25 |? 4. 50 |4. 732 |0. 232 |? 0. 0540 | |26 |? 6. 10 |4. 592 |1. 507 |? 2. 2712 | |27 |? 7. 0 |5. 497 |2. 202 |? 4. 8524 | |28 |10. 10 |6. 818 |3. 281 |10. 7658 | |29 |15. 20 |8. 787 |6. 412 |41. 1195 | (Continued) 4. 49? (a)? (Continued) |Method ( Exponential Smoothing | | | |0. 6 = ( | | | |Year |Deposits (Y) |Forecast ||Error| |Error2 | |30 |? 18. 10 |12. 6350 | 5. 46498 |29. 8660 | |31 |? 24. 10 |15. 9140 |8. 19 |67. 01 | |32 |? 25. 0 |20. 8256 |4. 774 |22. 7949 | |33 |? 30. 30 |23. 69 | 6. 60976 |43. 69 | |34 |? 36. 00 |27. 6561 | 8. 34390 |69. 62 | |35 |? 31. 10 |32. 6624 | 1. 56244 | 2. 44121 | |36 |? 31. 70 |31. 72 | 0. 024975 | 0. 000624 | |37 |? 38. 50 |31. 71 |6. 79 |? 46. 1042 | |38 |? 47. 90 |35. 784 |12. 116 |146. 798 | |39 |? 49. 10 |43. 0536 |6. 046 |36. 56 | |40 |? 55. 80 |46. 814 | 9. 11856 | 83. 1481 | |41 |? 70. 10 |52. 1526 |17. 9474 |322. 11 | |42 |? 70. 90 |62. 9210 | 7. 97897 |63. 66 | |43 |? 79. 10 |67. 7084 |11. 3916 |129. 768 | |44 |? 94. 0 0 |74. 5434 | 19. 4566 | 378. 561 | |TOTALS | |787. 30 | | | |150. 3 | | |1,513. 22 | |AVERAGE | 17. 8932 | | 3. 416 | 34. 39 | | | | |(MAD) |(MSE) | |Next period forecast = 86. 2173 |Standard error = 6. 07519 | Method ( Linear Regression (Trend Analysis) | |Year |Period (X) |Deposits (Y) |Forecast |Error2 | |? 1 |? 1 |0. 25 |–17. 330 |309. 061 | |? 2 |? 2 |0. 24 |–15. 692 |253. 823 | |? 3 |? 3 |0. 24 |–14. 054 |204. 31 | |? 4 |? 4 |0. 26 |–12. 415 |160. 662 | |? 5 |? 5 |0. 25 |–10. 777 |121. 594 | |? 6 |? 6 |0. 30 |? –9. 1387 |89. 0883 | |? 7 |? 7 |0. 31 |? –7. 50 |61. 0019 | |? 8 |? 8 |0. 32 |? –5. 8621 |38. 2181 | |? |? 9 |0. 24 |? –4. 2238 |19. 9254 | |10 |10 |0. 26 |? –2. 5855 |8. 09681 | |11 |11 |0. 25 |? –0. 947 |1. 43328 | |12 |12 |0. 33 |? 0. 691098 |0. 130392 | |13 |13 |0. 50 |? 2. 329 |3. 34667 | |14 |14 |0. 95 |? 3. 96769 |9. 10642 | |15 |15 |1. 70 |? 5. 60598 |15. 2567 | |16 |16 |2. 30 |? 7. 24 427 |24. 4458 | |17 |17 |2. 0 |? 8. 88257 |36. 9976 | |18 |18 |2. 80 |? 10. 52 |59. 6117 | |19 |19 |2. 70 |? 12. 1592 |89. 4756 | |20 |20 |3. 90 |? 13. 7974 |97. 9594 | |21 |21 |4. 90 |? 15. 4357 |111. 0 | |22 |22 |5. 30 |? 17. 0740 |138. 628 | |23 |23 |6. 20 |? 18. 7123 |156. 558 | |24 |24 |4. 10 |? 20. 35 |264. 083 | |25 |25 |4. 50 |? 21. 99 |305. 62 | |26 |26 |6. 10 |? 23. 6272 |307. 203 | |27 |27 |7. 70 |? 25. 2655 |308. 547 | |28 |28 |10. 10 |? 26. 9038 |282. 367 | |29 |29 |15. 20 |? 28. 5421 |178. 011 | |30 |30 |18. 10 |? 30. 18 |145. 936 | |31 |31 |24. 10 |? 31. 8187 |59. 58 | |32 |32 |25. 60 |? 33. 46 |61. 73 | |33 |33 |30. 30 |? 35. 0953 |22. 9945 | |34 |34 |36. 0 |? 36. 7336 |0. 5381 | |35 |35 |31. 10 |? 38. 3718 |52. 8798 | |36 |36 |31. 70 |? 40. 01 |69. 0585 | |37 |37 |38. 50 |? 41. 6484 |9. 91266 | |38 |38 | 47. 90 |? 43. 2867 |21. 2823 | |39 | 39 |49. 10 |? 44. 9250 |17. 43 | |40 | 40 |55. 80 |? 46. 5633 |? ? 85. 3163 | |41 | 41 |70. 10 |? 48. 2016 |? 479. 54 | |42 | 4 2 |70. 90 |? 49. 84 |? 443. 28 | |43 | 43 |79. 10 |? 51. 4782 |? 762. 964 | |44 | 44 |94. 00 |? 53. 1165 | 1,671. 46 | |TOTALS | |990. 00 | | |787. 30 | | | | | | | | | | | | | |7,559. 95 | | |AVERAGE |22. 50 | 17. 893 | |171. 817 | | | | | |(MSE) | |Method ( Least squares–Simple Regression on GSP | | |a |b | | | | |–17. 636 |13. 936 | | | | |Coefficients: |GSP |Deposits | | | | |Year |(X) |(Y) |Forecast ||Error| |Error2 | |? 1 |0. 40 |? 0. 25 |–12. 198 |? 12. 4482 |? 154. 957 | |? 2 |0. 40 |? 0. 24 |–12. 198 |? 12. 4382 |? 154. 71 | |? 3 |0. 50 |? 0. 24 |–10. 839 |? 11. 0788 |? 122. 740 | |? 4 |0. 70 |? 0. 26 |–8. 12 | 8. 38 | 70. 226 | |? 5 |0. 90 |? 0. 25 |–5. 4014 | 5. 65137 | 31. 94 | |? 6 |1. 00 |? 0. 30 |–4. 0420 | 4. 342 | 18. 8530 | |? 7 |1. 40 |? 0. 31 |? 1. 39545 | 1. 08545 | 1. 17820 | |? 8 |1. 70 |? 0. 32 |? 5. 47354 | 5. 5354 | 26. 56 | |? 9 |1. 30 |? 0. 24 |? 0. 036086 | 0. 203914 | 0. 041581 | |10 |1. 20 |? 0. 2 6 |–1. 3233 | 1. 58328 | 2. 50676 | |11 |1. 10 |? 0. 25 |–2. 6826 | 2. 93264 | 8. 60038 | |12 |0. 90 |? 0. 33 |–5. 4014 | 5. 73137 | 32. 8486 | |13 |1. 20 |? 0. 50 |–1. 3233 | 1. 82328 | 3. 32434 | |14 |1. 20 |? 0. 95 |–1. 3233 | 2. 27328 | 5. 16779 | |15 |1. 20 |? 1. 70 |–1. 3233 | 3. 02328 | 9. 14020 | |16 |1. 60 |? 2. 30 |? 4. 11418 | 1. 81418 | 3. 9124 | |17 |1. 50 |? 2. 80 |? 2. 75481 | 0. 045186 | 0. 002042 | |18 |1. 60 |? 2. 80 |? 4. 11418 | 1. 31418 | 1. 727 | |19 |1. 70 |? 2. 70 |? 5. 47354 | 2. 77354 | 7. 69253 | |20 |1. 90 |? 3. 90 |? 8. 19227 | 4. 29227 | 18. 4236 | |21 |1. 90 |? 4. 90 |? 8. 19227 | 3. 29227 | 10. 8390 | |22 |2. 30 |? 5. 30 |13. 6297 | 8. 32972 | 69. 3843 | |23 |2. 50 |? 6. 20 |16. 3484 |? 10. 1484 |? 102. 991 | |24 |2. 80 |? 4. 10 |20. 4265 |? 16. 3265 |? 266. 56 | |25 |2. 90 |? 4. 50 |21. 79 |? 17. 29 |? 298. 80 | |26 |3. 40 |? 6. 10 |28. 5827 |? 22. 4827 |? 505. 473 | |27 |3. 80 |? 7. 70 |34. 02 |? 26. 32 |? 6 92. 752 | |28 |4. 10 |10. 10 |38. 0983 |? 27. 9983 |? 783. 90 | |29 |4. 00 |15. 20 |36. 74 |? 21. 54 |? 463. 924 | |30 |4. 00 |18. 10 |36. 74 |? 18. 64 |? 347. 41 | |31 |3. 90 |24. 10 |35. 3795 |? 11. 2795 |? 127. 228 | |32 |3. 80 |25. 60 |34. 02 | 8. 42018 | 70. 8994 | |33 |3. 0 |30. 30 |34. 02 | 3. 72018 | 13. 8397 | |34 |3. 70 |36. 00 |32. 66 | 3. 33918 | 11. 15 | |35 |4. 10 |31. 10 |38. 0983 | 6. 99827 | 48. 9757 | |36 |4. 10 |31. 70 |38. 0983 | 6. 39827 |? 40. 9378 | |37 |4. 00 |38. 50 |36. 74 | 1. 76 | 3. 10146 | |38 |4. 50 |47. 90 |43. 5357 | 4. 36428 | 19. 05 | |39 |4. 60 |49. 10 |44. 8951 | 4. 20491 | 17. 6813 | |40 |4. 50 |55. 80 |43. 5357 |? 12. 2643 |? 150. 412 | |41 |4. 60 |70. 10 |44. 951 |? 25. 20 |? 635. 288 | |42 |4. 60 |70. 90 |44. 8951 |? 26. 00 |? 676. 256 | |43 |4. 70 |79. 10 |46. 2544 |? 32. 8456 |1,078. 83 | |44 |5. 00 |94. 00 |50. 3325 |? 43. 6675 |1,906. 85 | |TOTALS | | | |451. 223 |9,016. 45 | |AVERAGE | | | |? 10. 2551 |? 204. 92 | | | | | |? (MAD) |? (MS E) | Given that one wishes to develop a five-year forecast, trend analysis is the appropriate choice. Measures of error and goodness-of-fit are really irrelevant.Exponential smoothing provides a forecast only of deposits for the next year—and thus does not address the five-year forecast problem. In order to use the regression model based upon GSP, one must first develop a model to forecast GSP, and then use the forecast of GSP in the model to forecast deposits. This requires the development of two models—one of which (the model for GSP) must be based solely on time as the independent variable (time is the only other variable we are given). (b)? One could make a case for exclusion of the older data. Were we to exclude data from roughly the first 25 years, the forecasts for the later year