what ideas, values and attitudes are present in the text in relation to the topic of Creation in FRANKENSTEIN?

If you order your cheap custom essays from our custom writing service you will receive a perfectly written assignment on what ideas, values and attitudes are present in the text in relation to the topic of Creation in FRANKENSTEIN?. What we need from you is to provide us with your detailed paper instructions for our experienced writers to follow all of your specific writing requirements. Specify your order details, state the exact number of pages required and our custom writing professionals will deliver the best quality what ideas, values and attitudes are present in the text in relation to the topic of Creation in FRANKENSTEIN? paper right on time. Out staff of freelance writers includes over 120 experts proficient in what ideas, values and attitudes are present in the text in relation to the topic of Creation in FRANKENSTEIN?, therefore you can rest assured that your assignment will be handled by only top rated specialists. Order your what ideas, values and attitudes are present in the text in relation to the topic of Creation in FRANKENSTEIN? paper at affordable prices! What is the point of life??is the question often pondered in human minds. To answer this question it may be best to first look at where life begins. Creation. Then the next question in this matter would be, what is creation??The simple answer to that is everything. A stool is the creation of a carpenter, as a painting is a creation of an artist and we along with many other extraordinary things in this world are the creation of God. The motivation that inspires humans to create is a desire to feel good about oneself, to achieve something and to look good in the eyes of others.

There are many ideas, values and attitudes about creation in Mary Shellys story Frankenstein (the modern Prometheus). This essay will be describing how ideas, values and attitudes about creation in Frankenstein parallel either in similarities or differences to the original creation story found in the Holy Bible and the Greek myth Prometheus.

The first and most important value in Frankenstein about creation is that man should not play God and if humans try, then they shall suffer. In the story the main character Victor Frankenstein is determined to create life out of the dead, in his mission to defeat death. "Wealth was an inferior object; But what glory would attend the discovery if I could banish disease from the human frame and render non invulnerable to any but a violent death!?Then after Victor creates the monster he realizes his terrible mistake. "But now that I had finished, the beauty of the dream vanished, andeathless horror and disgust filled my heart?I considered the being that I had cast among mankind, and endowed with the will and power to effect purposes of horror…my own vampire…forced to destroy all that was dear to me.?

Similarly in the Greek myth, Prometheus (a titan) plays God by stealing the fire of wisdom from the gods and giving it to the humans he had created. The gods get angry and punish him terribly. "Zeus was enraged that man had fire. He decided to inflict a terrible punishment on both man and Prometheus…on Mount Caucasus, Prometheus was tormented day and night by a giant eagle tearing at his liver. By day, the eagle would come down to the cliff and devour Prometheus liver, and by night the liver would regenerate, only to have it destroyed the following day again.?The difference in Prometheus from Frankenstein is that Prometheus did not get punished for creating humans but going against his gods will. In Frankenstein creating the being is going against Gods will. In the holy bible Adam was punished for trying to play God and to be like god by eating the forbidden fruit and he and along with Eve was punished by being send to earth from the safety of heaven.

Another idea is that the created always has a choice on which path to take. Either the good or the evil. But the society that the created inhabits also has a great impact on what choice the created takes. Like in Frankenstein the beast had tried to be hospitable, by saving the girl from the river and helping the cottagers with their daily chores, but all he got for this was negative responses and rejection. This corrupted him and he chose the evil ways. Killing everyone Frankenstein loved. This parallels with the original creation story about Adam and Eve. Adam had a choice on whether he should eat the forbidden fruit or not he chose to eat it, with the influence of Eve.

Also, Prometheus had a choice on whether he should steal the fire. But he did it out of his sympathy love for the mortal humans.

The idea of achieving respect through creation is also important. Frankenstein wanted to be great and powerful and perfect and respected by creating this beast. "Life and death appeared to me ideal bounds, which I should firsteak through…a new species would bless me…should I deserve their? Like we are expected to worship and respect God. In Prometheus, the titan gives t6he mortals the forbidden fire so that we would love and respect him. "For unrelinquishingavery in the face of the ruling and cruel Powerful who inflict suffering on the rest of mankind. His name has been associated throughout the centuries and millenniums as that of the GREAT REBEL AGAINST INJUSTICE AND THE CRUEL AUTHORITY OF TYRANNICAL POWER.?

The idea that people should take responsibility for their things/creation is also very important in Frankenstein. Frankenstein creates the monster and then flees. Later on he realised that he could not flee from what is done and that he hold responsibility for the creature no matter hoe hideous he is. "In a fit of madness I created a rational creature, and was bound towards him, to assure, as far as was in my power, his happiness and well-being.?In the story of Prometheus, he felt responsibility for the human he had created and that is why heought the fire to them.

And in the bible it is stated in almost every page that God loves his children and blesses them.

A general idea about creation is that the creator will have to make an environment for his creation and set down rules and restrictions for it to abide. Like in the creation story in the bible

God created Adam after he created the earth and animals and eve for him, god also blessed Adam and set down the 10 commandments for all humankind to follow. "And god blessed them, saying, Be fruitful and multiply and fill?Thou shall not kill.? But in Frankenstein it was the exact opposite. After creating the beast Frankenstein not only abandons it in a world which it was not meant for, but also curses it and leaves it to roam free doing whatever it wants. Frankenstein says, "I threw myself on the bed in my clothes, endeavoring to seek a few moments of forgetfulness? and later on in the story the beast recalls. "No father had watched my infant days, no mother had bless me with smiles and caresses?and later on Frankenstein realizes. "In a fit of madness I created a rational creature, and was bound towards him, to assure, as far as was in my power, his happiness and well-being.?

These are the main parallels in the story Frankenstein, creation story and Prometheus and ideas, values and attitudes of creation found in them.

Power

Power is a recurring theme throughout the text that manifests itself in many different forms. The power to grant life, toing death, and the power to control life are all themes that Shelley presents in relation to Creation. Frankenstein has the power to create life as well as to destroy whereas the monster has the power to destroy life and cause pain.

At first Frankensteins main goal is to discover the "principle of life?for the good of mankind, but in his passing trance he soon finds himself "capable of bestowing animation upon lifeless matter? at this point, he is overwhelmed by the power and glory that creating woulding. Out of the desire for power and knowledge that is placed in Frankensteins hands, the monster is born without careful consideration. In creating, Frankenstein hopes that "a new species would bless me [him] as its creator and source? he wants to be worshipped for his god-like powers, hence seeing himself akin to God. However, unlike God Frankenstein does not provide for his monster, neglecting his duties to his creation and allowing the wretched being to roam free. Frankenstein has no plans for his monster and it is suggested that the monster is not bound by destiny like Frankenstein because he is not a child of god?

When the monster asks Frankenstein to exercise his powers yet again to create a female, Frankenstein agrees to this request rationally although less than willingly. He reasons with his creature but eventually decides that he is bound towards the monster as his creator and has "no right to withhold from him [the monster] the small portion of happiness which was yet in my [Frankensteins] power to bestow.?

In the text, the ultimate power is still in creation. Although the monster has physical power over Frankenstein, the monster does not have the knowledge that is needed to create another being. Without the knowledge to create, Frankenstein does not have hold over the monster. So we can say that in relation to the text, knowledge is power. The second monster is created out of knowledge ?from experimenting on the first monster, this bears similarities to Eve who made by God from the ribs of Adam.

As Frankenstein becomes more and more God-like? he has the choice and power to decide whether to grant life or to not. After considerable debate, he decides that it is wrong to create another monster because "she might become ten thousand times more malignant than her mate, and delight…in murder and wretchedness?and so "trembling with passion, tore to pieces the thing?that he was engaged in. In this instance, Frankenstein has once again manipulated life by restricting what is allowed to live and what is not. This act can again be seen as humans trying to play God by controlling nature and destiny.

Balance of Power

The balance of power shifts between the creator and created throughout this text. This can be seen as early as when Frankenstein leaves the monster to fend for itself. From this moment on, Frankenstein no longer has control over the actions of his creation who is "endowed with the will and power to…destroy? The monster realises that he has physical power over his creator when he strangles William to death, he then knows that Frankenstein is not at all like God but is a mere human who can be hurt physically and emotionally and is not perfect. The monsters "enemy is not invulnerable?

It is also suggested in the text that the creator is not necessary the master, the master is whoever holds the most power ?in this case, the monster. Despite this, Frankenstein has skills and knowledge to offer the monster, and as long as he still holds his status as the creator, there is equiliium between the two conflicting characters. That is, when Frankenstein promises to create a female for the monster, the monster does what he is told and does not defy any of his creators wishes. This is the same for Frankenstein, with the monster threatening "to create desolation?if he does not create a female monster. When Frankenstein destroys the female monster, the whole equiliium disappears. This is when the monster uses his physical advantages over Frankenstein, killing Clerval and Elizabeth causing him great emotional pain.

Huis

To have the attitude of huis is to believe that one possesses God-like powers. As a result, one is often corrupted, becoming a source of great misery to themselves and people around them. Victor Frankenstein constantly strives to further his huis in this text. At the beginning, he is simply someone who desires to learn about the workings of the universe. Intelligence, ambition and curiosity take him a step further, hence he greatly exceeds his original plan and to gain and misuse power. The huis is presented as a sin in the text, because with the possession of huis, Frankenstein runs into terrible mischief and trouble.

The complications in the narrative can all be linked to VFs discovery of power when he ponders the creation of a being in his own image. ? I found so astonishing a power placed within my hands?and "I hesitated a long time concerning the manner which I should employ it?(pg 5). These quotes demonstrate VFs initial concern and attitude towards the power that he is about to possess.

VF does have some forethoughts concerning the creation of a being, for example, "I doubted at first whether I should attempt the creation? But ultimately, he makes the decision to create. This decision may have being induced by the supreme power of being in control of a man ?a being of his own kind. As a result of his decision to create, VF attempts to play God, which he later realizes is a grave miscalculation.

Inevitably, VF becomes corrupted by huis, believing he will have ultimate control over the being that will owe his life to him. His ignorance and the resulting chaos reflect an attitude reminiscent of the time ?that God already possesses all the power in the universe, and thus man is easily corrupted and led astray, and should not attempt pursuits that are deemed part of a higher order.

One thing VF fails to recognize is that those who have been victorious throughout history have always sacrificed a great deal just to maintain their power, and the cycle is not about to change. Thus with the power he fails to behold (by running off right after the birth?of the monster) come disasters which destroy him.

VF also uses his headstrong power to deny life. This is made clear to us when heeaks his promise about creating a female counterpart for the monster "Begone, I doeak my promise; never will I create another like yourself? (pg 16) and tosses the unfinished body into the water. In this act, he displays more godlike tendencies.

Responsibility

Responsibility, in relation to creation, is one of the core themes of this text. From Victor Frankenstein to the monster he creates, we are reminded of the theme in many different forms. An obvious application is when VF debates, in his mind, his own degree of responsibility for his own actions, and for the actions of the monster.

First and foremost, Victor Frankenstein creates a monster. He is so blinded by huis that he never genuinely stops to consider the consequences of his actions, acting in an irresponsible fashion.

"Life and death appeared to me ideal bounds, which I should firsteak through, and pour a torrent of light into our dark world. A new species would bless me as its creator and source; many happy and excellent creatures would own their being to me. No father could claim the gratitude of his child so completely as I should deserve theirs. Pursuing these reflections, I thought, that if I could bestow animation upon lifeless matter, I might in the process of time (although I now found it impossible) renew life where death had apparently devoted the body to corruption?

Frankenstein only realise his own foolishness when he actually "infuses his creation with the spark of life? and is shocked by the gruesome creature that awakes. VF further contributes to the problem by neglecting his responsibilities as creator. It would be expected that, after two years of toil at the expense of his own health and rest, he should at least try to examine the monster, or take some interest in it. But alas, he is "unable to endure the aspect of the being he has created? and retires to his room. Thus we see the first act that sets off the conflict between man and monster, creator and creation. If the view is taken that the creation possesses a complete and total innocence from his beginning, then this is truly the first step toward corrupting that innocence.

When Frankenstein sees the monster for the second time, he falls under the misapprehension that it is attempting to attack him and flees. It is quite possible, of course, that the monster is simply confused, and in want of some manner of explanation. Nevertheless, Frankenstein runs away, returning the next day to find that his creation has vanished. Upon realising this, he is overjoyed, not for one moment taking responsibility for what he has unleashed on the world, or even for the wellbeing of the monster to whom he gave life.

?I was unable to contain myself. It was not joy only that possessed me; I felt my flesh tingle with the excess of sensitiveness, and my pulse beat rapidly?I jumped over chairs, clapped my hands, and laughed out loud.?

However, VF cannot be rid of feelings of guilt deep within him. The monster is "forever before his eyes? and he "raves incessantly concerning him? proving that he does feel some responsibility for his actions and creation. Still, he feels no compassion or regard for the monster, simply for the individuals that it may affect.

When it murders William and frames Justine as the culprit, to her conviction and execution, Frankenstein feels a deep pang, as though it were by his hands that they died "I beheld those I loved spend vain sorrow upon the graves of William and Justine, the first hapless victims to my unhallowed arts.?Similarly, Clerval and Elizabeth are killed by the monster, acts for which VF also feels remorse and anger. Interestingly, these are directed no so much towards himself, as to the monster.

Corruption of Innocence

Another key issue surrounding the story of Frankenstein is the corruption of innocence. Many argue that the monster is like a child ?completely innocent, his mind a clean slate. It is put forward that he is simply corrupted by society, and we hear the clich?that "society is to blame? It is suggested that the acts of ignorant people, for instance Felix and his family, can be more horrible than any monster.

If we are to believe the monsters side of the story, then he is simply naïve, lonely and in want of a friend when he accidentally strangles William. He only starts to plot and scheme ways toing misery to Frankensteins life after he experiences the same misery himself. Betrayed by the world, he resorts to a killing spree that would gain nothing but vengeance.

Another more extreme reading of the text suggests that the monster is actually a demon sent by god to punish Frankenstein for his huis. This is led by his belief that he can accomplish feats as great as that of god himself, and indeed play god to an entire race of people. This theory would act to negate the idea of corruption of innocence in the text.

Conclusion from Research

In relation to creation, the core theme in Frankenstein is power. This is presented in many different contexts, including the power to play god (i.e. to create, to give and deny life), the pursuit of power above all else, the attitude that God has supreme power and no human can come close to wielding this, the idea that the balance of power is shifts between creator and creation, and the idea of Huis, among others.

While power is central, there are many other less obvious themes encoded in the text. These are issues like responsibility, including the responsibility for the actions of our creations, our duty towards them, and the guilt we feel when something we create or devise results in the misery of others. Also discussed in the text is the idea of corruption of innocence, and that ultimately, true innocence is corrupted by society.

Please note that this sample paper on what ideas, values and attitudes are present in the text in relation to the topic of Creation in FRANKENSTEIN? is for your review only. In order to eliminate any of the plagiarism issues, it is highly recommended that you do not use it for you own writing purposes. In case you experience difficulties with writing a well structured and accurately composed paper on what ideas, values and attitudes are present in the text in relation to the topic of Creation in FRANKENSTEIN?, we are here to assist you. Your cheap research papers on what ideas, values and attitudes are present in the text in relation to the topic of Creation in FRANKENSTEIN? will be written from scratch, so you do not have to worry about its originality. Order your authentic assignment and you will be amazed at how easy it is to complete a quality custom paper within the shortest time possible!

Order & Society

If you order your custom term paper from our custom writing service you will receive a perfectly written assignment on Order & Society. What we need from you is to provide us with your detailed paper instructions for our experienced writers to follow all of your specific writing requirements. Specify your order details, state the exact number of pages required and our custom writing professionals will deliver the best quality Order & Society paper right on time. Out staff of freelance writers includes over 120 experts proficient in Order & Society, therefore you can rest assured that your assignment will be handled by only top rated specialists. Order your Order & Society paper at affordable prices! Order makes a society run in harmony, and without it there would be chaos. It comprises of laws which the people in the society have agreed and accepted. A society is a group of people that live similar lives. They obey the rules and regulations made by the society and often support each other, which helps them keep the order. In Tomorrow When the War Began 7 teenagers go out on a camping trip but when they return home they find their society, which theyve lived in their whole lives,oken down and stolen. They are forced to flee and create their own society or they too will be captured and imprisoned like the rest of the town. In the Shawshank Redemption, Andy, a married, successful man with aight future is sentenced to 50 years in jail for the murder of his wife and her lover. He is then forced to live in a new society with a new harsh freedom less order. These media are linked by the sudden change of order & society and how they adjust to it. When Elle in Tomorrow When the War Began is sitting there watching the stars, it creates a peaceful scene which symbolizes the teenagers society they originally lived in. Then came a humming noise which Elle quotes as "artificial," being the noises of the planes carrying the invaders. The artificial noise informs the reader that it doesnt belong to the peaceful scene, symbolizing the invaders arrival. At this point the society that the teenagers have been living in has just entirely changed, yet they are all still unaware. They are going to have to soon adjust to an aspect of life theyve never been exposed to, survival. In The Shawshank Redemption there is a similar scene also symbolizing the point of change to the characters order & society. When Andy walks into Shawshank the old, heavy doors close behind, thus symbolizing the end of his society which his lived in his whole life, and the beginning of his new harsh, dismal life in his new society. These scenes link by using the technique of symbolism. The concept of an aupt change from a peaceful, happy society running in harmony to a rough, unpleasant society is used in the text and the film. In conclusion these media have concentrated mainly on informing the viewer & reader of the enormous impact of changing order & society can be to a person.

Please note that this sample paper on Order & Society is for your review only. In order to eliminate any of the plagiarism issues, it is highly recommended that you do not use it for you own writing purposes. In case you experience difficulties with writing a well structured and accurately composed paper on Order & Society, we are here to assist you. Your cheap custom college paper on Order & Society will be written from scratch, so you do not have to worry about its originality. Order your authentic assignment and you will be amazed at how easy it is to complete a quality custom paper within the shortest time possible!

SATURN CAR COMPANY

If you order your cheap custom essays from our custom writing service you will receive a perfectly written assignment on SATURN CAR COMPANY. What we need from you is to provide us with your detailed paper instructions for our experienced writers to follow all of your specific writing requirements. Specify your order details, state the exact number of pages required and our custom writing professionals will deliver the best quality SATURN CAR COMPANY paper right on time. Out staff of freelance writers includes over 120 experts proficient in SATURN CAR COMPANY, therefore you can rest assured that your assignment will be handled by only top rated specialists. Order your SATURN CAR COMPANY paper at affordable prices! Saturn is a carand from America which is a subsidiary of General Motors Corp. In 185, Saturn Corp is created as a separate subsidiary as a way to foght against Japanese competitors as the Honda Civic and the Toyota Corolla in quality. Saturn was create with simple idea to put people first. In the beinning, our focus was on creating a different kind of company, a different kind of car. One dedicated to finding better ways for people to work together to design, buid and sell cars.

One visible wxample of the Saturn quality emphasis has been the decision to offer a moner-back guarantee. Within the first thirty days or 1,500 miles, which ever comes first, an original purchase may return his or her Saturn for a full refund or for a replacement car. The guarantee not only reassures the buyer about the purchase decision but also provides an internal signal about the quality level that is needed and expected.

Saturns uniqueness set out ot be customer-sensitive, labor friendly and service-oriented, no haggle pricing that means pur customer first and put all of us qorkers, managers, retailers together. Moreover, we work as team members. One reaon Saturn became popular was that they were different from the rest of GM.

One of the best was in June 14 Happy Hominging 40,000 people SATURN ENTHUSIASTS to Spring Hill to celeate our first five year and tour facility including meeting otheR sATURN OWNERS from across the country. That event has been described as The mother of all marketing programs aND FOR THE PROPLE WHO CAT MAKE IT TO Spring Hill, Saturn sponsors get-together at dealership around United State which gain 100,000 people attended the event.During the homecoming as campaign sales were up 5% compared to earlier year. Ad focus more on employees and Saturns philosophy.

Customer loyalty is one and Saturn continues to rank No.1 in sales satisfaction Studies that more than 70% of sales are to buyers who havent bought other GM vehicle including professional women --- that other parts of GM have difficulty reaching

Please note that this sample paper on SATURN CAR COMPANY is for your review only. In order to eliminate any of the plagiarism issues, it is highly recommended that you do not use it for you own writing purposes. In case you experience difficulties with writing a well structured and accurately composed paper on SATURN CAR COMPANY, we are here to assist you. Your cheap research papers on SATURN CAR COMPANY will be written from scratch, so you do not have to worry about its originality. Order your authentic assignment and you will be amazed at how easy it is to complete a quality custom paper within the shortest time possible!

<div dir="ltr" style="text-align: left;" trbidi="on">How do you write an essay Do my essay for me certainly is the scholar's most well-known phrase with a eve regarding assignment due date. To prevent disappointment attending college or even college, college students request someone to write the actual documents on their behalf. This technique is extremely common as well as gives earnings for some type of on-line businesses. In fact, college students sign up for greatest composing paper companies in order to discover a pre-written dissertation or get a new required 1. It appears to them more simple having the paper set instead of shelling out difficulty sleeping on unnecessary dissertation composing problems. Do my essay for me appears like the call with regard to relief, certainly, as opposed to the school workout. Students preserve on pondering that will compose dissertation personally. National internet marketers with the students, together with authors as well as mindset sound system find themselves in comfortable circumstances. One too when explained, "Do my essay for me!" These test is really appropriate in order to anyone that finds themself within comfortable instances. However, you will discover the actual dissertation composing services that could resolve the issues as well as you inside the best way. The case is actually well-known along with everybody. In fact, almost all of todays youth find themselves in this kind of not likeable case, exactly where they will see his or her composing jobs as the really terrible problems. It's aggravating with regard to college student to never accomplish operate you require regarding him or her with time. In some cases, doing work college students, as an example, only shortage time to fulfil a number of jobs; which might be exactly why they prefer the actual composing companies, certainly. In spite of a number of claims what sort of custom made dissertation composing companies possess deceitful character regarding training, the amount of composing companies or even sole freelancer freelance writers, even so, won't decrease along with many years dramatically. Really opposite, how much the companies that could meet The Great depression came and swept the world like wild fire, Hitler sized this opportunity and all the problems in Germany during the Weimar Republic. Moral in Germany was low, unemployment was high and the people of Germany wanted a change. Hitler gave them that change and made the Jews the scape goat of all problems. No one paid attention of the new wave sweeping Germany as other countries were worried about their own domestic problems. Hitler set to rebuild Germany which met no resistance from other countries.

By 15, Hitler had pulled out of the League of Nations and told he will run Germany him self. The Treaty of Versailles wasnt well enforced as the US never joined it, the USSR was evolved in its own revolution and most important the US being an economic power, they never ratified the Treaty of Versailles. The US anditain pledged to come to Frances aid if ever attacked but the US never signed the treaty so the US never had any obligation to help France and with no US,itain could not honor her part of the deal to help France.

itain then let Germany build up her navy to 5% of theitish Navy and of course Germany agreed. France felt left out of this and became wary of German actions next door. Disagreements between the great world powers let Hitler to do what he wants and in 16 he marched his army straight into the Rhineland and no opposition was met.

In August 1, the USSR joined Germany in non-aggression pact which worrieditain and France as this left Poland in very shaky grounds. Hitler marched through Poland and met the USSR half way there, France was taken in 140 and defeated in 5 days and noitain was left alone to face the might of the Germany military.

Stalin supplied Hitler with oil until June 140 when Hitler decided to invade the USSR, most of Europe was under Hitlers control and it looked like no one could stop Germany. Japan on the other side of the world had made massive gains. A tripartite pact was signed in September 140 between Italy, Germany and Japan and agreed to set up the "New Order."

The New World Order did not entirely collapse under the force of nation sates, its own shortcomings contributed to its downfall. There were no means to rehabilitate and limit Germany potential for belligerency. Germany lost little in terms of territory, in the Versailles peace, as Germany still remained one of the larges nations in Europe. The Versailles peace was too harsh; it made no effort to rehabilitate Germany.

Please note that this sample paper on The Treaty of Versailles is for your review only. In order to eliminate any of the plagiarism issues, it is highly recommended that you do not use it for you own writing purposes. In case you experience difficulties with writing a well structured and accurately composed paper on The Treaty of Versailles, we are here to assist you. Your cheap custom college paper on The Treaty of Versailles will be written from scratch, so you do not have to worry about its originality. Order your authentic assignment and you will be amazed at how easy it is to complete a quality custom paper within the shortest time possible!

The day that changed my life

If you order your custom term paper from our custom writing service you will receive a perfectly written assignment on The day that changed my life. What we need from you is to provide us with your detailed paper instructions for our experienced writers to follow all of your specific writing requirements. Specify your order details, state the exact number of pages required and our custom writing professionals will deliver the best quality The day that changed my life paper right on time. Out staff of freelance writers includes over 120 experts proficient in The day that changed my life, therefore you can rest assured that your assignment will be handled by only top rated specialists. Order your The day that changed my life paper at affordable prices!ett Nordstrom

Professor Valvivia

English 101

7 September 00The Day That Changed My Life

I woke up to a usual September day. It was the eleventh of the month; the summer sun was shining and the birds were chirping. It was actually one of the last warm days that New York would see as the season of autumn was approaching. I stretched and thought about my average day problems as any guy at the age of seventeen would. "Did I do all of my homework? Do I have to work tonight?" I started to get ready for school as my dad was almost out of the door to catch the train. My dad takes the train every morning to work in Manhattan. Ive always envied him because he goes to New York City everyday. There are so many interesting people to see and there are fun things to do in the city. The city symbolizes freedom with out Statue of Liberty and our beautiful, tall towers. I left the house to go to high school. I would never have thought a day that started out so usual, would change my life forever.

I left for school promptly at seven oclock a.m. and began my scheduled routine. I walked down my social studies wing and found that al the T.VS were on and a massive amount of students were crowded around. I said to myself, "what could be so interesting on television?" I looked at the television and I saw the replay of two airplanes crashing into the "twin towers" which were located in Manhattan. My mouth hung open and the first thing I thought of was my dad! Tragic ideas kept haunting me about my dad. I tried to remember if the "twin towers" were near my dads office. My mom picked me up from school at noon crying. My heart was beating a million times a minute. I asked with extreme concern if my dad was okay. She answered yes but I could tell by her fidgety gestures and the shakiness of her voice that the only thing she really wanted at that moment was to have her family together.

I drove home with my mom and I was petrified. I was scared of what could have happened if my dad was working in those buildings and I was scared for the safety of our country. Later that day, I found out that this attack caused massive destruction to the beautiful city which I loved. Thousands of people were reported dead or missing. The news tore my heart into pieces. Fireman, police officers and rescue workers were doing the best they could to help save these victims. They had to put their fear in the back of their mind and use their courage to help save the people in need. As I saw this on the television, I felt proud to be an American because of the help and support we all gave each other. I couldnt believe that this day had changed the outlook on life for millions of people.

September 11, 001 will always be a memorable day for everybody in America. Its not a good memory, but it has taught us all a lesson. I lost the sense of innocence from my life since this occurred. I now know how horrible it is to feel unsafe in my own environment. I will never take life for granted. From this tragic experience, Ive developed into a more responsible person. I am not afraid to express feelings to the people whom I love so much because I strongly believe the saying "Live for today because you never know what could happen tomorrow".

Please note that this sample paper on The day that changed my life is for your review only. In order to eliminate any of the plagiarism issues, it is highly recommended that you do not use it for you own writing purposes. In case you experience difficulties with writing a well structured and accurately composed paper on The day that changed my life, we are here to assist you. Your cheap custom college paper on The day that changed my life will be written from scratch, so you do not have to worry about its originality. Order your authentic assignment and you will be amazed at how easy it is to complete a quality custom paper within the shortest time possible!

Business Forecasting;Using the United Kingdom statistics locates Banks consumer credit: Gross lending figures from 1993-2003

If you order your cheap custom essays from our custom writing service you will receive a perfectly written assignment on Business Forecasting;Using the United Kingdom statistics locates Banks consumer credit: Gross lending figures from 1993-2003. What we need from you is to provide us with your detailed paper instructions for our experienced writers to follow all of your specific writing requirements. Specify your order details, state the exact number of pages required and our custom writing professionals will deliver the best quality Business Forecasting;Using the United Kingdom statistics locates Banks consumer credit: Gross lending figures from 1993-2003 paper right on time. Out staff of freelance writers includes over 120 experts proficient in Business Forecasting;Using the United Kingdom statistics locates Banks consumer credit: Gross lending figures from 1993-2003, therefore you can rest assured that your assignment will be handled by only top rated specialists. Order your Business Forecasting;Using the United Kingdom statistics locates Banks consumer credit: Gross lending figures from 1993-2003 paper at affordable prices! Using the United Kingdom statistics locates Banks consumer credit Gross lending figures from 1-00

1. Examine the data by plotting and/or otherwise for seasonal effects, trends and cycles.

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1 454 61 454 4405 4788 4576 41 45 478714 41 80 487 46 4657 457 464 551 48 4688 505 57

15 481 4571 5485 508 56 5678 5587 6116 556 576 5781 5855

16 576 5448 577 650 658 60 7066 6841 6480 617 6468 7161

17 6 6088 646 78 700 747 7 7516 750 74 71 8751

18 7516 77 86 8180 87 884 17 8606 4 878 80 445

1 8107 84 767 88 8511 874 85 80 87 460 1057 10450

000 50 1006 108 4 11156 10806 1077 1088 06 10857 108 1070

001 1114 100 1106 117 1188 1171 1 15 1158 147 184 165

00 176 1165 167 184 14085 111 147 115 18 144 14 1

00 10 110

Resource http//www.statistics.gov.uk/statbase/TSDtimezone.asp (Accessed /04/0)

The figure shows Banks consumer credit Gross lending on a monthly basis starting with the first month of 14 and ending with the twelfth month of 00. And we will use the data in 00 to check on the forecast.

As seen from the graph above, the time series data moves upward over a period of time so it can be said that the data shows a fairly strongly positive trend, so that we might expect high autocorrelation coefficients. Considering ACF, it supports this expectation because it is significantly different from zero. Autocorrelation at lag 1 is 0.45 and move downwards to 0.581 at lag 16.

Afterwards, we will choose data from 14-001 to model.

. Try a classical decomposition method on part of the data and check it against the rest of the data.

The ACF of the actual data shows that there is a trend or cycle or both, so we should look at the ACF of the first difference.

From the ACF of the first difference, we could use the 1 points moving average to smooth the data. Subsequently, we will calculate the twelve point moving average, seasonal estimate, trend-cycle estimate and forecast based on the trend-cycle and the seasonal ratios.

After calculating center moving average and getting seasonal estimate (ratio), we will find out the factors as following table

Sequence Mean Count

1 0.5 7

0.1 7

1.00 7

4 0.8 7

5 1.01 7

6 1.0 7

7 1.04 7

8 1.05 7

1.00 7

10 1.00 7

11 1.00 7

1 1.04 7

Then we can de-seasonalise the data by dividing the factors into the data to create a trend cycle column. Next, we regress the trend cycle against the time and will get a trend column.

Coefficients

Unstandardized Coefficients Standardized Coefficients t Sig.

Model B Std. Error Beta

1 (Constant) 45.164 84.847 46.48 0.000

TIME 84.57 1.51 0.85 55.56 0.000

a Dependent Variable TRENCYC

From the coefficients table above, the trend equation is

Trend = 45.164+84.57 Time

Then we can create a moving average forecasting column with the factors and trend column. The moving average forecasting equation is

Forecast = Trend Factors

Therefore, it can be implied that

Forecast = (45.164+84.57 Time) Factors

Moreover, the errors will be calculated by deducting the forecast data from the actual data in order to see how accurate the forecast is.

Errors = Data Forecast

See the figures of the result as following table

Time Data Center Moving Average Ratios Sequence Factors Trend-cycle Trend line Forecast Errors

14 Jan 1 41 0.5 446. 40.5 88.04 00.6

Feb 80 0.1 47.6 411.88 74.6 6.7

Mar 487 1.00 487.00 418.4 418.4 680.76

Apr 4 46 0.8 4485.71 48.5 416.4 1.06

May 5 4657 1.01 4610.8 466.5 4410.6 46.8

Jun 6 457 1.0 485.80 4451.1 4540. 416.67

Jul 7 464 4804.00 0.8 7 1.04 451.46 455.66 4717.0 -.0

Aug 8 551 4857.1 1.14 8 1.05 550.48 455.66 4851.0 661.8

Sep 48 407.00 1.0 1.00 550.48 4704.8 4704.8 84.6

Oct 10 4688 461.50 0.4 10 1.00 4688.00 4788.7 4788.7 -100.7

Nov 11 505 50.88 1.00 11 1.00 505.00 487.0 487.0 178.1

Dec 1 57 5107.04 1.05 1 1.04 5165.8 457.45 5155.75 16.5

15 Jan 1 481 5107.04 0. 1 0.5 5066. 5041.81 478.71 .

Feb 14 4571 56.6 0.87 1 0.1 50.08 516.16 4664.81 -.81

Mar 15 5485 585.6 1.04 1.00 5485.00 510.5 510.5 74.48

Apr 16 508 554. 0.5 4 0.8 50.04 54.88 5188.8 -0.8

May 17 56 54.46 1.05 5 1.01 50.04 57. 54.0 58.7

Jun 18 5678 547.6 1.04 6 1.0 5566.67 546.5 557.86 105.14

Jul 1 5587 55.6 1.01 7 1.04 57.1 5547.5 576.86 -18.86

Aug 0 6116 5615.71 1.0 8 1.05 584.76 56.0 51. 0.08

Sep 1 556 5664.5 0.8 1.00 556.00 5716.66 5716.66 -154.66

Oct 576 578.4 1.01 10 1.00 576.00 5801.0 5801.0 -8.0

Nov 5781 5817.67 0. 11 1.00 5781.00 5885.8 5885.8 -104.8

Dec 4 5855 586.54 1.00 1 1.04 56.81 56.7 608.5 -5.5

16 Jan 5 576 545.6 0.7 1 0.5 6065.6 6054.0 5751.8 10.6

Feb 6 5448 607.7 0.0 0.1 586.81 618.45 5751.8 -17.

Mar 7 577 6106.5 0.5 1.00 577.00 6.80 6.80 -44.80

Apr 8 650 61.58 1.0 4 0.8 647.5 607.16 6181.0 168.8

May 658 66. 1.05 5 1.01 6516.8 61.5 6455.4 16.57

Jun 0 60 65. 0.5 6 1.0 514.71 6475.87 6605. -57.

Jul 1 7066 64.00 1.10 7 1.04 674. 6560. 68.64 4.6

Aug 6841 6485. 1.05 8 1.05 6515.4 6644.5 676.8 -15.8

Sep 6480 65. 0. 1.00 6480.00 678.5 678.5 -48.4

Oct 4 617 655.00 1.05 10 1.00 617.00 681.0 681.0 10.70

Nov 5 6468 6655.7 0.7 11 1.00 6468.00 687.66 687.66 -4.66

Dec 6 7161 67.58 1.06 1 1.04 6885.58 68.0 761.0 -100.0

17 Jan 7 6 68. 0.4 1 0.5 678.4 7066.7 671.05 -1.05

Feb 8 6088 68.1 0.88 0.1 660.11 7150.7 6507.16 -41.16

Mar 646 664.58 0.0 1.00 646.00 75.0 75.0 -8.0

Apr 40 78 7050.67 1.05 4 0.8 75.65 71.44 717.06 08.4

May 41 700 711.88 0.8 5 1.01 6.60 740.80 7477.84 -468.84

Jun 4 747 76.58 1.0 6 1.0 76.47 7488.16 767. -164.

Jul 4 7 74.67 1.08 7 1.04 7618.7 757.5 7875.4 47.58

Aug 44 7516 7448.54 1.01 8 1.05 7158.10 7656.87 80.7 -5.7

Sep 45 750 7588.7 0. 1.00 750.00 7741. 7741. -1.

Oct 46 74 7710.5 1.0 10 1.00 74.00 785.5 785.5 117.41

Nov 47 71 774.5 0.5 11 1.00 71.00 70.4 70.4 -518.4

Dec 48 8751 707.6 1.11 1 1.04 8414.4 74.0 814.07 46.

18 Jan 4 7516 801.08 0.4 1 0.5 711.58 8078.66 7674.7 -158.7

Feb 50 77 8116.67 0.0 0.1 806.64 816.01 748.4 -1.4

Mar 51 86 8.8 1.0 1.00 86.00 847.7 847.7 115.6

Apr 5 8180 88. 0.8 4 0.8 846.4 81.7 8165.0 14.1

May 5 87 844.71 0.8 5 1.01 8145.54 8416.0 8500.5 -7.5

Jun 54 884 857. 1.05 6 1.0 8807.84 8500.44 8670.45 1.55

Jul 55 17 8580.88 1.06 7 1.04 8775.6 8584.80 88.1 18.81

Aug 56 8606 864.46 1.00 8 1.05 816.1 866.16 10.61 -46.61

Sep 57 4 877. 1.06 1.00 4.00 875.51 875.51 488.4

Oct 58 878 88.6 0. 10 1.00 878.00 887.87 887.87 -10.87

Nov 5 80 886.67 1.01 11 1.00 80.00 8. 8. -.

Dec 60 445 811.58 1.06 1 1.04 081.7 006.58 66.85 78.15

1 Jan 61 8107 878.88 0.0 1 0.5 85.68 00.4 866. -5.

Feb 6 84 060.08 0.1 0.1 07.6 175.0 84.5 -15.5

Mar 6 767 14.1 1.07 1.00 767.00 5.66 5.66 507.4

Apr 64 88 0.67 0.6 4 0.8 01.7 44.01 157.1 -4.1

May 65 8511 04.04 0.1 5 1.01 846.7 48.7 5.65 -1011.65

Jun 66 874 415.7 1.05 6 1.0 680. 51.7 70.8 171.0

Jul 67 85 516.6 1.04 7 1.04 47.08 57.08 80.7 -18.7

Aug 68 80 651. 1.0 8 1.05 61.0 681.44 10165.51 -5.51

Sep 6 87 770.8 1.0 1.00 87.00 765.80 765.80 1.0

Oct 70 460 86.50 0.6 10 1.00 460.00 850.15 850.15 -0.15

Nov 71 1057 67.6 1.06 11 1.00 1057.00 4.51 4.51 66.4

Dec 7 10450 10117.00 1.0 1 1.04 10048.08 10018.87 1041.6 0.8

000 Jan 7 50 1014.1 0. 1 0.5 1001.58 1010. 58.06 -68.06

Feb 74 1006 1076.4 0.8 0.1 11017.58 10187.58 70.70 755.0

Mar 75 108 1016.88 1.05 1.00 108.00 1071.4 1071.4 561.06

Apr 76 4 1071.71 0.0 4 0.8 5.67 1056.0 1014.17 -806.17

May 77 11156 1044.1 1.07 5 1.01 11045.54 10440.65 10545.06 610.4

Jun 78 10806 10465. 1.0 6 1.0 1054.1 1055.01 1075.51 70.4

Jul 7 1077 1054. 1.0 7 1.04 1058.65 1060.7 110.74 -60.74

Aug 80 1088 10610.8 1.0 8 1.05 106.81 106.7 118.41 -46.41

Sep 81 06 1061.7 0. 1.00 06.00 10778.08 10778.08 -87.08

Oct 8 10857 10707.8 1.01 10 1.00 10857.00 1086.44 1086.44 -5.44

Nov 8 108 10817.5 1.01 11 1.00 108.00 1046.80 1046.80 -54.80

Dec 84 1070 1088.1 0.8 1 1.04 107.1 1101.15 1147.40 -76.40

001 Jan 85 1114 1100.08 1.01 1 0.5 117.47 11115.51 1055.7 58.7

Feb 86 100 11170.04 0.0 0.1 11014. 111.87 1011.88 -168.88

Mar 87 1106 110.67 0.8 1.00 1106.00 1184. 1184. -.

Apr 88 117 11450.75 0.8 4 0.8 11456.1 1168.58 11141.1 85.7

May 8 1188 1161.88 1.0 5 1.01 11780.0 1145.4 11567.47 0.5

Jun 0 1171 117.67 1.00 6 1.0 1155.80 1157. 11768.04 .6

Jul 1 1 1.04 141.7 1161.65 1086.5 84.48

Aug 15 1.05 1178.10 11706.01 11.1 .6

Sep 1158 1.00 1158.00 1170.7 1170.7 -4.6

Oct 4 147 1.00 147.00 11874.7 11874.7 17.8

Nov 5 184 1.00 184.00 115.08 115.08 88.

Dec 6 165 1.04 114.04 104.44 155.17 10.8

Then, we can plot the decomposition forecasting against the actual data to illustrate what is happening.

The graph shows that the decomposition forecasting fits the actual data better at the middle. However, at the beginning and the end, there are some errors.

Comparing the forecast data with the rest of the data, we calculate them by extending the time line and see the result as following table

Rest Data Time Factors Trend Forecast Errors

00 Jan 176.00 7 0.5 117.7 1151.40 140.60

Feb 1165.00 8 0.1 11.15 1111.06 851.4

Mar 167.00 1.00 16.51 16.51 8.4

Apr 184.00 100 0.8 180.86 11.5 1850.75

May 14085.00 101 1.01 1465. 158.87 145.1

Jun 111.00 10 1.0 154.58 1800.57 -688.57

Jul 147.00 10 1.04 16.4 11. 158.71

Aug 115.00 104 1.05 1718. 154.1 -1.1

Sep 18.00 105 1.00 180.65 180.65 186.5

Oct 144.00 106 1.00 1887.01 1887.01 606.

Nov 14.00 107 1.00 171.6 171.6 -.6

Dec 1.00 108 1.04 1055.7 1577.5 44.05

00 Jan 158.00 10 0.5 1140.08 148.07 1114.

Feb 17.00 110 0.1 14.4 104. 7.77

Sum of squared errors = 1855.51

Mean-squared error (MSE) = 1108.75

Root-mean-squared error (RMSE) = 57.74

This RMSE is about 7. % of the mean for the rest of data during the forecast period

From the table above, we can notice the errors as illustrated the graph below which is plotted from rest data against forecast data of 00-00.

Although there is some significant errors, perhaps which are caused by economic uncertainty during that period, overall the plot graph below shows that the predict data relatively fit the real data.

. Try an Autocorrelation approach on the same data and discuss the differences between the results and those from the Decomposition approach. Please comment on the different from of the equation for the forecast.

After the first difference, it shows that there are seven spikes on lag1, lag, lag4, lag10, lag1, lag1, and lag14 so we will try on these lagged data to calculate correlations.

The table below shows the correlations between the actual data and lagged data

Correlations

DATA LAGS LAGS LAGS LAGS LAGS LAGS LAGS

(DATA,1) (DATA,) (DATA,4) (DATA,10) (DATA,1) (DATA,1) (DATA,14)

DATA Pearson Correlation 1 .5 .6 .5 .6 .7 .40 .54

Sig. (-tailed) . .000 .000 .000 .000 .000 .000 .000

N 6 5 86 84 8 8

LAGS(DATA,1) Pearson Correlation .5 1 .56 .6 .50 5 .7 .8

Sig. (-tailed) .000 . .000 .000 .000 .000 .000 .000

N 5 5 86 84 8 8

LAGS(DATA,) Pearson Correlation .6 .56 1 .57 .8 .5 .4 .58

Sig. (-tailed) .000 .000 . .000 .000 .000 .000 .000

N 86 84 8 8

LAGS(DATA,4) Pearson Correlation .5 .6 .57 1 .45 .51 .51 .

Sig. (-tailed) .000 .000 .000 . .000 .000 .000 .000

N 86 84 8 8

LAGS(DATA,10) Pearson Correlation .6 .50 .8 .45 1 .48 .64 .

Sig. (-tailed) .000 .000 .000 .000 . .000 .000 .000

N 86 86 86 86 86 84 8 8

LAGS(DATA,1) Pearson Correlation .7 .5 .5 .51 .48 1 .48 .45

Sig. (-tailed) 0.000 .000 .000 .000 .000 . .000 .000

N 84 84 84 84 84 84 8 8

LAGS(DATA,1) Pearson Correlation .40 .7 .4 .51 .64 .48 1 .47

Sig. (-tailed) .000 .000 .000 .000 .000 .000 . .000

N 8 8 8 8 8 8 8 8

LAGS(DATA,14) Pearson Correlation .54 .8 .58 . . .45 .47 1

Sig. (-tailed) .000 .000 .000 .000 .000 .000 .000 .

N 8 8 8 8 8 8 8 8

Correlation is significant at the 0.01 level (-tailed).

From the correlation between the data and lagged data, these are all high and then we need to use regression on the chosen columns to find the relationship.

Variables Entered/Removed

Model Variables Entered Variables Removed Method

1 LAGS(DATA,14), LAGS(DATA,4), LAGS(DATA,10), LAGS(DATA,1), LAGS(DATA,), LAGS(DATA,1), LAGS(DATA,1) . Enter

a All requested variables entered.

b Dependent Variable DATA

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 0.80 0.61 0.57 448.111

a Predictors (Constant), LAGS(DATA,14), LAGS(DATA,4), LAGS(DATA,10), LAGS(DATA,1), LAGS(DATA,), LAGS(DATA,1), LAGS(DATA,1)

This explains 5.7% of the variability and so will be reasonable fit to the data

Coefficients

Unstandardized Coefficients Standardized Coefficients t Sig.

Model B Std. Error Beta

1 (Constant) 1.580 10.57 1.85 .067

LAGS(DATA,1) 1.658E-0 .1 .017 .16 .8

LAGS(DATA,) .10 .10 .01 .04 .00

LAGS(DATA,4) 1.70E-0 .0 .00 .017 .86

LAGS(DATA,10) -.11 .10 -.11 -1.17 .4

LAGS(DATA,1) .5 .105 .547 5.66 .000

LAGS(DATA,1) 6.E-0 .14 .05 .471 .6

LAGS(DATA,14) .16 .104 .181 1.88 .064

a Dependent Variable DATA

From the coefficients table, the lag1, lag4, lag10, lag1, and lag14 data are not significant as well as the constant so the regression should be repeated only with lag and lag1 data because their significant is less than .05

Variables Entered/Removed

Model Variables Entered Variables Removed Method

1 LAGS(DATA,1), LAGS(DATA,) . Enter

a All requested variables entered.

b Dependent Variable DATA

c Linear Regression through the Origin

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 . .7 .7 44.446

a For regression through the origin (the no-intercept model), R Square measures the proportion of the variability in the dependent variable about the origin explained by regression. This CANNOT be compared to R Square for models which include an intercept.

b Predictors LAGS(DATA,1), LAGS(DATA,)

This fit has increased slightly from .7% to 6% by removing the lag1, lag4, lag10, lag1, and lag14 data as well as the constant.

Coefficients

Unstandardized Coefficients Standardized Coefficients

Model B Std. Error Beta t Sig.

1 LAGS(DATA,) .4 .07 .44 6.08 .000

LAGS(DATA,1) .651 .07 .575 8.70 .000

a Dependent Variable DATA

b Linear Regression through the Origin

All lag data are significant, so

Forecast = 0.4 (data lagged) + 0.651 (data lagged1)

See the figures of the Autocorrelation forecasting as following table

Time Data lag lag1 Forecast Error

14 Jan 1 41

Feb 80

Mar 487

Apr 4 46 41.00

May 5 4657 80.00

Jun 6 457 487.00

Jul 7 464 46.00

Aug 8 551 4657.00

Sep 48 457.00

Oct 10 4688 464.00

Nov 11 505 551.00

Dec 1 57 48.00

15 Jan 1 481 4688.00 41.00 4746.01 66.

Feb 14 4571 505.00 80.00 4808.81 -7.81

Mar 15 5485 57.00 487.00 554.54 -4.54

Apr 16 508 481.00 46.00 474.70 1.0

May 17 56 4571.00 4657.00 508.8 65.6

Jun 18 5678 5485.00 457.00 564. 4.08

Jul 1 5587 508.00 464.00 5.8 .18

Aug 0 6116 56.00 551.00 6087.75 8.5

Sep 1 556 5678.00 48.00 5740.48 -178.48

Oct 576 5587.00 4688.00 5504.58 58.4

Nov 5781 6116.00 505.00 57.78 -1.78

Dec 4 5855 556.00 57.00 58.8 -8.8

16 Jan 5 576 576.00 481.00 566. 8.78

Feb 6 5448 5781.00 4571.00 551.58 -65.58

Mar 7 577 5855.00 5485.00 6141.08 -68.08

Apr 8 650 576.00 508.00 5848. 501.68

May 658 5448.00 56.00 607.16 484.84

Jun 0 60 577.00 5678.00 60.7 -17.7

Jul 1 7066 650.00 5587.00 644.7 641.1

Aug 6841 658.00 6116.00 6871.01 -0.01

Sep 6480 60.00 556.00 66.5 10.65

Oct 4 617 7066.00 576.00 685.6 6.1

Nov 5 6468 6841.00 5781.00 6766.6 -8.6

Dec 6 7161 6480.00 5855.00 6656. 504.68

17 Jan 7 6 617.00 576.00 6787.6 -5.6

Feb 8 6088 6468.00 5448.00 686.10 -8.10

Mar 646 7161.00 577.00 601.0 -655.0

Apr 40 78 6.00 650.00 6.4 44.06

May 41 700 6088.00 658.00 657.51 51.4

Jun 4 747 646.00 60.00 666.48 80.5

Jul 4 7 78.00 7066.00 7840.66 8.4

Aug 44 7516 700.00 6841.00 750.44 -14.44

Sep 45 750 747.00 6480.00 74.1 0.87

Oct 46 74 7.00 617.00 781.16 -8.16

Nov 47 71 7516.00 6468.00 7510.1 -11.1

Dec 48 8751 750.00 7161.00 76.0 787.1

18 Jan 4 7516 74.00 6.00 7648.17 -1.17

Feb 50 77 71.00 6088.00 707.4 1.06

Mar 51 86 8751.00 646.00 707.84 455.17

Apr 5 8180 7516.00 78.00 8105.1 74.7

May 5 87 77.00 700.00 778.80 44.0

Jun 54 884 86.00 747.00 856.8 447.7

Jul 55 17 8180.00 7.00 8748.8 78.11

Aug 56 8606 87.00 7516.00 8504.57 101.4

Sep 57 4 884.00 750.00 88.50 40.50

Oct 58 878 17.00 74.00 177.65 -44.65

Nov 5 80 8606.00 71.00 858.58 0.4

Dec 60 445 4.00 8751.00 754.14 -0.14

1 Jan 61 8107 878.00 7516.00 874.51 -617.51

Feb 6 84 80.00 77.00 86.7 -468.7

Mar 6 767 445.00 86.00 50.67 176.

Apr 64 88 8107.00 8180.00 8884.15 -51.15

May 65 8511 84.00 87.00 866.11 -455.11

Jun 66 874 767.00 884.00 1016.0 -6.0

Jul 67 85 88.00 17.00 81.6 .64

Aug 68 80 8511.00 8606.00 8.84 41.16

Sep 6 87 874.00 4.00 1051. -64.

Oct 70 460 85.00 878.00 10006.6 -546.6

Nov 71 1057 80.00 80.00 101. 474.71

Dec 7 10450 87.00 445.00 105. -8.

000 Jan 7 50 460.00 8107.00 40.60 .40

Feb 74 1006 1057.00 84.00 10005.1 0.0

Mar 75 108 10450.00 767.00 1045.87 -11.87

Apr 76 4 50.00 88.00 .5 -50.5

May 77 11156 1006.00 8511.00 4.08 11.

Jun 78 10806 108.00 874.00 1118.66 -77.66

Jul 7 1077 4.00 85.00 10515. 57.77

Aug 80 1088 11156.00 80.00 116.81 -414.81

Sep 81 06 10806.00 87.00 1145.7 -1.7

Oct 8 10857 1077.00 460.00 10887.81 -0.81

Nov 8 108 1088.00 1057.00 11675.85 -78.85

Dec 84 1070 06.00 10450.00 11151.68 -44.68

001 Jan 85 1114 10857.00 50.00 1070.5 17.75

Feb 86 100 108.00 1006.00 1108.51 -185.51

Mar 87 1106 1070.00 108.00 1175.5 -61.5

Apr 88 117 1114.00 4.00 1074.07 5.

May 8 1188 100.00 11156.00 1166.65 5.5

Jun 0 1171 1106.00 10806.00 1180. -.

Jul 1 1 117 1077 1141.88 87.1

Aug 15 1188 1088 107.40 17.60

Sep 1158 1171 06 1165.06 -67.06

Oct 4 147 1 10857 174.74 50.6

Nov 5 184 15 108 1501.7 47.6

Dec 6 165 1158 1070 1157.7 677.8

Then, we can plot the autocorrelation forecasting against the actual data to illustrate what is happening.

The graph shows that the Autocorrelation forecasting fits the actual data better at the middle. However, at the end, there are some errors.

Comparing the forecast data with the rest of the data, we calculate them by extending the time line and see the result as following table

Time Data lag lag1 Forecast Error

00 Jan 7 176 147 1114 106.5 -07.5

Feb 8 1165 184 100 1165.68 -00.68

Mar 167 165 1106 1748.1 -6.1

Apr 100 184 176 117 111. 107.71

May 101 14085 1165 1188 18. 1086.77

Jun 10 111 167 1171 14.0 -110.0

Jul 10 147 184 1 14555.76 167.4

Aug 104 115 14085 15 1406.8 -1.8

Sep 105 18 111 1158 1711. 77.77

Oct 106 144 147 147 15087.1 -15.1

Nov 107 14 115 184 14166.08 -14.08

Dec 108 1 18 165 17.56 -5.56

00 Jan 10 158 144 176 141. -6.

Feb 110 17 14 1165 1470.75 -118.75

Sum of squared errors = 1071844.

Mean-squared error (MSE) = 7650.16

Root-mean-squared error (RMSE) = 874.76

This RMSE is about 6.6 % of the mean for the rest of data during the forecast period

From the table above, we can notice the errors as illustrated the graph below which is plotted from rest data against forecast data of 00-00.

However, there are some errors at the end, overall the plot graph below (During 14-00) shows that the predict data relatively fit the real data.

From the graph above, it illustrated the actual data (red line), Decomposition forecasting (blue line), and Autocorrelation forecasting (green line). Some periods Decomposition model is closer and fitter than Autocorrelation model, but some periods do contrast. Therefore, it is relatively difficult to say which model is better because both models are quite close to the real data.

However, it can be seen the error of these two models as following graph

Decomposition Model

Forecast = (45.164 + 84.57 Time) Factors

Autocorrelation Model

Forecast = 0.4 (data lagged) + 0.651 (data lagged1)

From these equations, it demonstrates that decomposition model depends on the trend running following time and some seasonal effects showing by the factors. And that means it can forecast more than one period ahead. Considering Autocorrelation model, it depends on the lag and lag1 data. It means it can forecast third periods ahead because from its equation has to forecast following the lag data.

4. Try a Box-Jenkins ARIMA approach on the same data and compare your forecasts with the rest of the data as before.

The trendcycle data from the question two will be used to analyse on the Box-Jenkins models as the seasonal effect was removed and the trendcycle data is shown at the chart below.

See the ACF and PACF of the trend-cycle data

From ACF, it shows a trend, so we will do the first difference to remove the trend

From Partial ACF, there is one strong spike on lag one and it dies away after one spike so if a model is fitted to the data, it should be an AR(1).

After first different, ACF does not die away so try the Partial ACF.

This PACF does not show any evidence of pattern repeat and does not die away so try the second difference.

The ACF and PACF of the second difference do not give anything helpful.

Therefore, AR(1) model is suggested by the Partial ACF of the trendcycle data. So we will try ARIMA(1,0,0) trendcycle data.

Model Description

Variable TRENCYC

Regressors NONE

Non-seasonal differencing 0

No seasonal component in model.

Parameters

AR1 ________ value originating from estimation

CONSTANT ________ value originating from estimation

5.00 percent confidence intervals will be generated.

Split group number 1 Series length 6

No missing data.

Melards algorithm will be used for estimation.

Termination criteria

Parameter epsilon .001

Maximum Marquardt constant 1.00E+0

SSQ Percentage .001

Maximum number of iterations 10

Initial values

AR1 .416

CONSTANT 808.40

Conclusion of estimation phase.

Estimation terminated at iteration number because

Sum of squares decreased by less than .001 percent.

FINAL PARAMETERS

Number of residuals 6

Standard error 5.65186

Log likelihood -74.7657

AIC 150.551

SBC 1508.658

Variables in the Model

B SEB T-RATIO APPROX. PROB.

AR1 .8057 .0185 4.8788 .00000000

CONSTANT 8144.71650 17.450 .7476 .00008

The constant term is significant so we can fit an AR(1) to the data with the constant.

The following new variables are being created

Name Label

FIT_1 Fit for TRENCYC from ARIMA, MOD_1 CON

ERR_1 Error for TRENCYC from ARIMA, MOD_1 CON

LCL_1 5% LCL for TRENCYC from ARIMA, MOD_1 CON

UCL_1 5% UCL for TRENCYC from ARIMA, MOD_1 CON

SEP_1 SE of fit for TRENCYC from ARIMA, MOD_1 CON

From the box of variables in the model above, T-ratio of the AR1 and constant are far away from 0 and bigger than and also the all probabilities of which are significant.

So, the ARIMA Model will be

Z t = 8144.71650 + 0.8057 Z t-1

Because of using the trendcycle data with ARIMA, the ARIMA forecast data need to be multiplied by factors in order to get the real forecasts and compare to the actual data

Forecast = (8144.71650 + 0.8057 Z t-1) factors

After multiplying the forecast data by factors and plotting the forecast data against the actual data, it can be seen that it is very close to the data all over the period of time so this ARIMA model fits to the actual data.

Then check this model by considering the residuals by plotting the error. So, the graph shows removing trend and it is stationary.

So, the graph shows removing trend and it is relatively stationary. Then check with ACF and PACF which do not suggest anything.

All figures of ARIMA forecasting are shown as following table.

Time Data Forecast Error LCL UCL

14 Jan 1 41 777.48 -78.40 755.1 1554.1

Feb 80 40.1 -46.50 40.41 55.8

Mar 487 4446.0 4.10 67.1 566.61

Apr 4 46 484.61 -456.74 76.75 61.16

May 5 4657 460.8 54.08 77.10 576.5

Jun 6 457 477.15 180.5 4.85 585.6

Jul 7 464 510.58 -410.17 74. 610.4

Aug 8 551 481. 666.46 404.1 576.7

Sep 48 506.71 -17.71 417.01 6486.4

Oct 10 4688 5050. -6. 870.61 60.0

Nov 11 505 4755.17 6.8 575.46 54.88

Dec 1 57 516.58 5. . 61.80

15 Jan 1 481 46.11 -156.6 404.57 640.8

Feb 14 4571 4664.78 -10.06 46.4 605.84

Mar 15 5485 508.7 401.7 04.0 66.44

Apr 16 508 545.5 -4.64 456.7 6716.

May 17 56 511.81 76.4 407.51 648.

Jun 18 5678 578.0 -117.7 4504.6 6864.11

Jul 1 5587 5841.4 -44.65 447.05 676.47

Aug 0 6116 567. 8.77 446.8 6605.70

Sep 1 556 586.84 -07.84 460.1 704.55

Oct 576 561.18 150.8 44.48 671.8

Nov 5781 580.8 -8.8 46.57 688.

Dec 4 5855 6060.01 -17.1 4647. 7006.64

16 Jan 5 576 54.74 86.5 448.7 6858.8

Feb 6 5448 5556.16 -118.86 45.6 785.8

Mar 7 577 608.74 -55.74 484.04 708.45

Apr 8 650 570.70 660.51 46.8 68.7

May 658 6577.07 4.88 5.4 761.65

Jun 0 60 667.4 -6.76 568.76 778.17

Jul 1 7066 616.6 86.1 4778. 717.75

Aug 6841 7161.50 -05. 5640.76 8000.18

Sep 6480 6546.0 -66.0 567.1 776.61

Oct 4 617 651.5 404.65 5.64 76.06

Nov 5 6468 640.86 -47.86 5761.15 810.56

Dec 6 7161 6760.60 85.00 50.87 7680.

17 Jan 7 6 6564.54 -181.6 570.4 808.75

Feb 8 6088 6147.1 -65.8 5576. 75.65

Mar 646 6718.7 -47.7 558.67 788.08

Apr 40 78 6157.4 14.76 510.1 746.60

May 41 700 761. -604.4 664.84 874.5

Jun 4 747 710.8 6.45 578.1 814.7

Jul 4 7 766.06 75.0 616.66 85.08

Aug 44 7516 800. -470.40 6448.7 8808.1

Sep 45 750 7177.7 4.7 57.56 856.7

Oct 46 74 75.14 410.86 65.4 8711.85

Nov 47 71 746. -555. 6767.1 16.6

Dec 48 8751 7701.87 1008.78 65.4 8585.5

18 Jan 4 7516 788.7 -47.60 7.47 588.8

Feb 50 77 70.66 146.5 676.40 05.8

Mar 51 86 8064. 8.77 6884.5 4.4

Apr 5 8180 811.58 -11.8 717.05 58.47

May 5 87 846.44 -17.46 716.0 5.7

Jun 54 884 808.44 66.1 665.8 5.4

Jul 55 17 146.76 -1.00 7615.5 74.67

Aug 56 8606 01.88 -567.51 758. 4.40

Sep 57 4 815.1 1046.81 7015.48 74.0

Oct 58 878 0.68 -4.68 8040.7 10400.

Nov 5 80 8716.67 0. 756.6 86.7

Dec 60 445 61.1 176.80 775. 10084.64

1 Jan 61 8107 8610.5 -5.84 788.8 104.

Feb 6 84 7758.77 511.4 746.4 705.8

Mar 6 767 00.0 746.8 7840.1 101.7

Apr 64 88 540.77 -7.1 8555.77 1015.1

May 65 8511 086.5 -56.66 7816.68 10176.10

Jun 66 874 858.68 15.14 741.54 600.6

Jul 67 85 1006.57 -177.48 8470.84 1080.6

Aug 68 80 1.6 -85.6 867.56 1066.7

Sep 6 87 8.5 648.75 8158.55 10517.6

Oct 70 460 51.0 -41.0 8771.4 1110.1

Nov 71 1057 44.44 116.56 854.7 10614.15

Dec 7 10450 1071. -501.7 6.64 117.06

000 Jan 7 50 510.54 0.4 881.8 1110.80

Feb 74 1006 05.7 10.67 8815.1 11174.6

Mar 75 108 1061.76 -18.76 78.05 1141.47

Apr 76 4 10565.15 -147.0 601.06 1160.47

May 77 11156 601.75 158.86 86.8 10686.

Jun 78 10806 1108.6 -5.06 80.47 1168.8

Jul 7 1077 1068.8 -187.87 66.8 1176.

Aug 80 1088 1081.4 48.17 15. 1145.4

Sep 81 06 100.6 -414.6 140.8 11500.40

Oct 8 10857 871.78 85. 86.07 11051.48

Nov 8 108 10804.0 87.70 64.5 1184.01

Dec 84 1070 117.16 -541.50 658.1 1018.

001 Jan 85 1114 74.5 1474.18 075.58 1145.00

Feb 86 100 10610.4 -645.5 10480.11 18.5

Mar 87 1106 1058.5 10.47 778.8 118.4

Apr 88 117 10785.1 450.81 85.61 1185.0

May 8 1188 11505.70 88.4 101.07 1571.4

Jun 0 1171 114.75 -14.75 105.85 188.7

Jul 1 1 115.18 8. 101.74 167.15

Aug 15 165.85 -610. 11168.7 158.14

Sep 1158 11668.7 -10.7 10488.56 1847.8

Oct 4 147 115.56 151.44 10115.85 1475.7

Nov 5 184 1147.86 -8.86 1168.15 147.57

Dec 6 165 167.8 -608.55 11577.88 17.0

Comparing the forecast data with the rest of the data, we calculate them by extending the time line and see the result as following table

Time Data Forecast Error LCL UCL

00 Jan 7 176 1664.74 10.8 117.4046 14.04

Feb 8 1165 10.5 -147.8 11764.648 1488.006

Mar 167 14.08 -715.08 1186.16 145.887

Apr 100 184 184.01 714.8 10.184 15087.04

May 101 14085 11.54 6.10 11477.54 14541.5

Jun 10 111 18.7 -146.05 11588.6446 1465.4704

Jul 10 147 1484.8 5.60 1.05 156.046

Aug 104 115 1700.5 -46.8 11516.186 14580.01

Sep 105 18 1587.1 -58.1 1055.04 1511.007

Oct 106 144 1448.75 45.5 1116.868 1480.666

Nov 107 14 175.48 -.48 1174.5704 14807.6

Dec 108 1 1400.47 -78.4 11.66 146.7884

00 Jan 10 158 1646.74 1001. 11780.4406 14844.664

Feb 110 17 1184.77 41.47 1146.5 1456.161

Sum of squared errors = 55.1

Mean-squared error (MSE) = 4566.7

Root-mean-squared error (RMSE) = 68.15

This RMSE is about 4.76 % of the mean for the rest of data during the forecast period

However, overall the plot graph below (During 14-00) shows that the predict data relatively fits the real data.

5. Discuss the differences between the Box-Jenkins results and the other methods you have used. In particular comment on the different mathematical forms of the models selected.

Decomposition Model

Forecast = Trend Factors

= (45.164 + 84.57 Time) Factors

Autocorrelation Model

Forecast = 0.4 (Data lagged) + 0.651 (Data lagged1)

ARIMA Model

Forecast (Zt) = (8144.71650 + 0.8057 Z t-1) Factors

First of all, decomposition model is a linear model which depends on the trendcycle, runs following time, and seasonal effects showing by the factors. So it can forecast more than one period ahead.

Second, autocorrelation model is based on two explanatory variables which are data lagged and data lagged1. So it can forecast third periods ahead because from its equation has to forecast following the lag data.

Finally, Box-Jenkins model or ARIMA model shows the form of an ARIMA(1,0,0) or AR(1) model which depends on the constant term and Zt-1 as well as factors.

The graph below illustrates the comparisons of actual data and three forecasting data which are generated from decomposition model, autocorrelation model and ARIMA model.

It is very difficult to say which model is better because most forecast data are relatively close to real data and some periods one is fitter to the actual data more than others but some periods do contrast. Therefore, we can not tell much different among them.

Next, we will focus on the most appropriate of these three models by considering the error.

Compare the errors of the data for model fit.

From the graph above, it can be seen that the autocorrelation model is more appropriate than other two because of more accuracy. Moreover, it involves only the lag data and does not have any influences from the factors like other two models. If you look at the actual data graph below, it shows a trend pattern but does not show a seasonal pattern. It is emphasised to use autocorrelation model. On the other hand, if the graph shows obviously seasonal pattern, decomposition model should be better. And because of using the trendcycle data to use for ARIMA forecasting, it involves seasonal effects (factors); so make ARIMA model not a proper method as well.

However, there has some lost data at the beginning and at the end by using autocorrelation model to forecast because its data is need to be lagged and it can not forecast a bit more future like Box-Jenkins or ARIMA model.

6. Comment on the impact of your choice of where to split the data in order to use some data to fit the model and other data to check it.

The data at the end (00-00) has been used to check the forecast. From the graph below, we can see that at the beginning and middle of the rest data, the forecast data from autocorrelation model and ARIMA model is close to data but other periods, significant errors will occur. However, there are considerable errors occurred.

Then, trying new data model, we choose data model from 15-00 and data in 1-14 is used for checking. The graph below shows errors from old data (split at the end) against errors from new data (split at the beginning)

The magnitudes of errors from two ways to split data all over the period are similar but they do not overlap at the same time so it does some impact on where to split the data. However, it should be consider the pattern of actual data and choose the appropriate method to forecast rather than think about where to split the data.

Please note that this sample paper on Business Forecasting;Using the United Kingdom statistics locates Banks consumer credit: Gross lending figures from 1993-2003 is for your review only. In order to eliminate any of the plagiarism issues, it is highly recommended that you do not use it for you own writing purposes. In case you experience difficulties with writing a well structured and accurately composed paper on Business Forecasting;Using the United Kingdom statistics locates Banks consumer credit: Gross lending figures from 1993-2003, we are here to assist you. Your cheap research papers on Business Forecasting;Using the United Kingdom statistics locates Banks consumer credit: Gross lending figures from 1993-2003 will be written from scratch, so you do not have to worry about its originality. Order your authentic assignment and you will be amazed at how easy it is to complete a quality custom paper within the shortest time possible!