Scientific Approach Psychology of Human Behaviour

Question: Depict about logical methodology and examination and suggestion? Answer: Presentation Brain research is the psyche which assists with seeing various things in a remarkable way regardless of how the information is to be accumulated and should be responded on by the human conduct. It is up to you how to make do with the complexities and attempt to approach towards various discernments which will gadget to anticipate the best in you. (Borman Motowidlo, 2014). The psyche is prepared to see the distinctive crude information and bring the most for keeping up various connections between psyche, cerebrum and the responses of various individuals. The social brain research helps in taking care of better yield which concern all the terrible and the better altruism which rely upon the revultion treatment or the great conduct issue. The advancement of various morals and controls are commonly constrained under various social orders which attempt to guarantee that the human conduct totally relies upon the responses regarding how the human culture is following up on a specific circum stance. (Anderson Bower, 2014). The brain science is simply to mastermind with the satisfactory structures to act with better patterns, which impacts the various attributes like the imagination factor, normal practices, confidence in culture or the hereditary qualities. Rundown It is up to you how you need to attract the consideration various fields and aptitude to approach for a superior survey of various conduct of human. On a certified scale, one can simply guarantee that one can concentrate on the creation of specialized terms which are thoroughly dealing with the two limits and parity the extension to accomplish the limited subtleties according to the premise of learning and thinking. The belief system to follow all the enhanced traits which are significance for a superior work of execution can be differentiated and prompting combination at various levels. The gatherings have the mastery which will have the option to limit the presentation and lead to demographical changes according to the inquires about. (Lopez et al., 2014). Diverse forthcoming have the capacity for dealing with the advantages in the association which will be arranged under the idea of reliance of collaboration. The broadening and the kind of individuals from groups are commonly the individuals who chiefly center around the correspondence to persuade the individuals for prompting the gathering work and coordinated effort. According to the strategies which go under the Meta investigation, there is an enhanced update under the proportion of connections just as methods which for the most part lead to a superior relationship categorisation. (Vernon, 2014). The primary outcomes are commonly the enhancement in the work group just as execution which will prompt conduction and partition of errands for the positive just as critical drew closer for keeping up the statics. Logical Approach It has been said that the clinicians are the best individuals who can introspect and attempt to utilize their own specialization for a superior comprehension to change out the segment capacity and present the best throughput generally speaking. (Smith et al., 2014). With the overseeing condition of workmanship, the individuals need to focus on the various prospects which will help in achieving towards a progressively muddled impact, prompting a powerful way to deal with handle all the key components on the earth. It is the recollections which attempt to hold the observations and bring the best conduct changes which majorly affect all the forceful status circumstance. There is a need to build up an understanding which will pick up and gather to guarantee that they go over with better methods of living which principally incorporates: Characterizing the influence to investigation and hold back to stand up to by bouncing to the circumstance which will have a hurry to all the experience, attempting to defeat with the vulnerabilities throughout everyday life. The development to deal with the reasons and look for the spots which will have the option to deal with the stems of viciousness and help the network to arrive at the restricted flexibly, along these lines, attempting to retain all the measures just as drawing in the better circumstances. (Argyle Beit-Hallahmi, 2014). The worry for increase in the affirmation and the information accessibility for guiding towards enhanced administration could prompt center obstructions of the diverse inventive personalities of the individuals in various groups. In the activity, it is imperative to have staggered work mentalities as there are individuals who could invest in their amounts of energy and lead to commitment of such exercises which could be advantageous for the organization just as the whole association. One ought to keep up a superior relationship which will segregate and attempt to the various aspects of life fundamentally the installment level and their advantages which lead to associations improvement and pledge to work fulfillment. (Earthy colored Richerson, 2014). Examination and suggestion According to the examination, we comprehend that it is hard to limit to progress constantly with the brain science yet there are a great deal of exercises that should be learnt before it so one can increase their method of better changes and experience. There are sure focuses and conduct, one have to concentrate on: There is far to deal with the transformations and request the better inquiries which will have centered changes relying on how one can oversee and hop towards a higher line of loss of motion. (Carter, 2014). There is a requirement for personalisation which will effectively help in dealing with the various varieties relying on the augmentations as referenced with the underlying time and just as after when there is no secluded methodology left. Move with a beginning heading towards a greater amount of steadfastness of clients which will be progressively fruitful in the manner that it yields most extreme protection from what is given to suggest on various human practices. The experience of good clashes regularly lead to direct that there are sure stuns that must be kept up to deal with the contentions and lead to a success just as higher force order. (Sternberg, 2014). The gatherings of reliability frequently had an intergroup clashes which prompted drawing portions of contentions which is imperative to gather information for long joy and fulfillments. The flourish to make do with the confidence and deal with the status socially is critical to accomplish a lift and make do with the fakes to deal with the outlasting which will be imperative to order and acquire an adjustment in the wellbeing approach. The sense to understanding and conviction that there are sure arrangement which will be paid, there is a need to invest energy partaking with the better assignment and legitimize that it will diminish the level and lead to lies, showing up in a lot more elevated level of cacophony. The passionate method to portray the structure and mange with the arrangement of various procedure for the most part have a going up against choices to hold the synopsis which will have a superior plan to recognize and make do with the prideful inclusion, along these lines, prompting the level of consistency which will help in dealing with the human conduct just as dealing with the discernment which layout the transformative methodology. (Keller Schoenfeld, 2014). The conduct to look at and research the work and deal with the powerful customized and enthusiastic methodology which will present the glimmering impact, accordingly, developing the best job for the turmoil and dealing with the circumstances which will point towards various changes according to the imagination factor and attempt to process the difficulties which are essential to deal with. End The conviction which prompts placing in the higher deals in the administration procedure has made the association to reach to the business which will have the option to guarantee some safe and predictable yield for the equivalent. The plans for the human conduct gets separated when there is an adjustment in the method of the individual places in for the best analyses to change the ability to deal with various individuals. The human conduct straightforwardly collaborates with the psyche and attempt to test the information and how it can keep up its relationship with the others through a united feeling of organic upgrades. According to the looks into, the brain research directly affects the soundness of a person as mind is legitimately associated with it. (Nestor Schutt, 2014). There are sure contrasts which as indicated by the inclinations want for an increasingly segment change and shift according to the moderate impacts of worm culture. There is a connect to see mostly better the ch aracter advancement and the innovation which trusts in bunch configuration work. it is imperative to center at the correct heading at the ideal time inorder to depend on the retaining minutes with the non-postponed procedure of satisfaction. The human mind attempts to make progress through standard driving forces and the work which gives a knowledge how the more slender can regulate the progressions as communicated in the association or a little assembling. Reference Keller, F. S., Schoenfeld, W. N. (2014).Principles of brain science: An efficient book in the study of behavior(Vol. 2). BF Skinner Foundation. Nestor, P. G., Schutt, R. K. (2014).Research techniques in brain science: Investigating human conduct. Sage Publications. Argyle, M., Beit-Hallahmi, B. (2014).The brain research of strict conduct, conviction and experience. Routledge. Borman, W. C., Motowidlo, S. J. (Eds.). (2014).Organizational Citizenship Behavior and Contextual Performance: A Special Issue of Human Performance. Brain science Press Sternberg, R. J. (Ed.). (2014).Advances in the brain science of human intelligence(Vol. 4). Brain research Press. Smith, E. R., Mackie, D. M., Claypool, H. M. (2014).Social brain science. Brain research Press. Carter, C. S. (2014). Oxytocin pathways and the development of human behavior.Annual survey of psychology,65, 17-39. Lopez, S. J., Pedrotti, J. T., Snyder, C. R. (2014).Positive brain science: The logical and down to earth investigations of human qualities. Sage Publications. Anderson, J. R.,

Louis Xiv Essays (893 words) - Dauphins Of France,

Louis Xiv Louis XIV The Sun King, Louis XIV was just four years of age when he succeeded his dad to the French seat. Frequently not thought about, he almost suffocated on the grounds that nobody was watching him as he played close to a lake. (The Sun King) This started to shape in his young psyche, and gave him an early dread of God. Louis was additionally molded by the French Civil War. In this, the Paris Parliament rose against the crown. For a long time, Louis would endure dread, cold, hunger and other soul breaking occasions. He could never pardon Paris, the nobles, or the average citizens. At long last, in 1653, Cardinal Jules Mazarin had the option to end the insubordination. He started to teach Louis on his situation as lord. Despite the fact that Louis the XIV was currently old enough, the Cardinal remained the predominant expert in French governmental issues. Louis presented with the French armed force during Frances war with Spain. His greatest fight was relinquishing his adoration for Mazarins niece for legislative issues. In 1660 he wedded the little girl of the lord of Spain to bring harmony between the two nations. Mazarin passed on March 9, 1661. On March 10, Louis guaranteed incomparable expert in France. Not since Henry IV had such a case been made. Louis considered himself to be Gods delegate on earth, in this manner, infallible.(Louis XIV and the French Monarchy) He regulated street building, court dignity, safeguard, and questions inside the congregation. He had the help of his pastors, and the French individuals. He had given France the picture it wanted, youth and imperativeness encompassed by grandness. Louis won the kindness of the nobles by making it obvious that their future relied upon their capacity remain on his great side. This debilitated the honorability, and would inevitably debilitate France. Louis had among his supporters a wide range of people. Authors, for example, Moliere were requested to laud him. Landmarks rose all through the nation and Louis had royal residences worked in his respect. The most intricate was Versailles, situated outside Paris. Away from infection, Versailles likewise confined the ruler from his kin. France was additionally experiencing a financial insurgency. Fares were expanded, and a n aval force, trader marine, and police affiliation rose. Streets, ports, and waterways were being assembled. He attacked the Spanish Netherlands in 1667. The restarted war among France and Spain would be hit or miss, for the rest of Louis rule. In 1668, the French armed force withdrew under tension from Dutch and English powers. Louis pledged to overcome the Dutch and ruin their Protestant commercial republic. He aligned himself with his cousin, Charles II of England, and attacked the Netherlands in 1672. Louis was triumphant when the Treaty of Mijmegen was marked in 1678. At the point when the Dutch were vanquished, he had likewise crushed its partners, Spain and the Holy Roman Empire. Frances fringes had extended toward the north and the east. His naval force had become as extensive as that of England and Holland. His private life was not as blessed. Companions had been embroiled in the Illicit relationship of the Poisons, where prominent individuals had been blamed for magic and m urder. Louis requested his court to get discrete. The seat of Government was moved to Versailles in 1682. At the point when the Queen passed on, he wedded Mme de Maintenon, who had been tutor to the Kings kids. Louis didn't comprehend the renewal, and he saw French Protestants as dangers to the seat. He disavowed the Edict of Nantes, which had conceded them opportunity of love. Many left France, those that remained were aggrieved. Britain, the Dutch, and the Holy Roman Empire joined in 1688 in the Grand Alliance to stop French extension. This war finished in 1697 with the marking of the Treaty of Rijswijk. France lost piece of its domain, and Louis lost open help. He had to perceive William of Orange as ruler of England. This conflicted with his conviction that the Stuarts had divine right to the seat. An energy for notoriety and the longing to expand A french area in Europe were the main intentions of Louis XIV. He disregarded the chances to increase a realm in America and India an d included France in wars that destroyed the nation monetarily and made ready for the

How Big Data Changes the Banking Industry

How Big Data Changes the Banking Industry Big Data is a very important step in developing the future of all banking industries. It  is defined as a set of consolidated information based on the behavioral and other trends followed by human beings. This information is assessed through databases over a long period of time. The collection of this data helps banking industry understand the needs and expectations of people.This way they are able to keep up with trends and make changes in the industry that will improve their system and simultaneously enhance customer relations in the future. By staying aware of newer trends they are able to understand and provide their clients with what they need on time. © Shutterstock.com | Syda ProductionsIn this article we will look at 1) how Big Data changes the dynamics of the banking industry, 2) the dimensions in which the banking industry is affected by Big Data, 3) how Big Data has radicalized customer service in  the banking industry, 4) the benefits of Big Data to the banking industry, and 5) the concerns related to Big Data in the banking industry.HOW BIG DATA CHANGES THE DYNAMICS OF THE BANKING INDUSTRYThe use of the internet has made it a lot easier to assess and evaluate the progress of a bank beforehand. Banks are always trusted with a lot of information regarding their clients. With this information they can learn newer ways to provide better services. With the insight they have, they can make sure their customers are always provided with resources that are beneficial and not available elsewhere. The use of Big Data helps banks run more profitably.As the number of clients increase in an industry, it becomes important for the bank to provide better services. They are entrusted with more information in their system that has to be kept safely and also be accessible quickly. Small scale databases cannot be easily trusted with such large quantities of information. If Big Data is not taken up by a bank, their databases can experience a system fail. They keep their clients waiting while they try to provide them with an answer to a simple question. Big Data helps them avoid such embarrassing situations.Big Data may be a huge system but it simplifies everything. When a name is entered into the system it filters the required information. This way banks can save time. It helps the industries function a lot smoother and helps them identify problems being faced at both the large and small scale. This way they are able to keep their organization up to date before a problem is revealed to their clients.Sometimes certain investors or clients make an agreement but change their mind later. Big Data helps the industry make sure that these people are not walking away from their contracts. It becomes easy for banks to keep a clear record of credit card limits assigned to each customer. With Big Data they can make sure no client is exceeding the limit unjustly. Along with keeping track of credit card limits, Big Data also helps them ensure no fraud is being committed. They can easily trace debtors through this system and block or avoid them from making unauthorized transactions that can harm their customer.These are some of the apparent improvements that Big Data can bring about in banks. In the long run it can help banks slowly and gradually develop a better understanding of their clients. This way the industry can stay a step ahead and learn what market is valued by their clients and what strategies gain more attention. DIMENSIONS IN WHICH BANKING INDUSTRY IS AFFECTED BY BIG DATAIt is made clear that Big Data is an extensive system that can help banking industries simplify their system and work better. Big Data helps them in developing a sincere clientele. Following is a list of ways in which the banking industry is affected by the use of Big Data:Optimize Offers and Cross SellWith the use of Big Data, the banking industry can improve their professional relationships with their clients and be able to understand them better. Big Data provides a more personal assistance and helps banks with detailed information on their clients. This information helps banks come up with schemes that are direct and personalized. These schemes gain maximum attention and are able to make sure they can come up with similar plans for the future. As their ability to analyze and assess the situation increases, their ability to deliver according to the expectations of their clients increases as well.When customers begin to realize that their banks are providing them with everything they need then automatically the bank becomes a better brand. The industry gains a favorable appreciation from their old clients an d gain newer ones too. They become able to attract newer clients by keeping their older ones happy with their services.Contact Centre Efficiency and Problem ResolutionBig Data helps banks avoid all situations that can be embarrassing for them. When a client asks a question that can only be answered by assessing the database Big Data makes it easy. The clients no longer have to wait to be answered because of a system fail. Their queries are solved immediately. By entering the name of the individual, banks are provided with all the important details. This also increases the level of satisfaction received from the customers. It also develops a sincere number of clients that learn to rely on their banks more.Payment Fraud Detection and InvestigationThough the internet has made many things easy it has also made committing fraud easy. When banks come up with newer ways to avoid fraud, people come up with newer ways to commit it. This makes it impossible for banks to be able to trace their fraudulent person. The use of Big Data rids them of this problem. It makes it easy for banks to immediately be able to trace a fraud and put an end to it. The installation and use of this system makes it easy for banks to identify where the fraud is being committed. They can stop it immediately without delaying or experiencing a loss.Counterparty Credit Risk ManagementBanks have to make many agreements to keep their business going. They have to meet with newer investors and make newer financial agreements with them. In these cases they have to evaluate the agreements and avoid risks and chances of financial damage. Initially this was considered an impossible thing to do because the information required on individuals was always limited. Big Data provides a thorough background check on these individuals and ensures that the banks make a well-informed decision keeping all things in mind. It helps them understand how things could go wrong and how things could be beneficial for them.HO W BIG DATA HAS RADICALIZED CUSTOMER SERVICE IN THE BANKING INDUSTRYGenerally, customer data analytics is not considered very important by banks. However, Big Data has helped banks improve its customer services to a great extent. Customer analytics has enabled banks to keep their interactions with their clients at a maximum level and engage with them continually. This has helped them understand their clients better. It also helps them deliver better because they know their clients better. They are able to provide their customers with a wholesome personalized experience.As far as acquiring new clients and then retaining them is concerned, Big Data has helped banks decipher the environmental aspects and what trends influence their clients. This way banks have been able to think of marketing strategies accordingly. This also aids them in formulating services that the potential clients might want.Banks now make massive profits and are improving because they understand Big Data as a requi rement for their development. This happens as a result of better understanding and usage of data analytics and the subsequent lead generation. Banks now make sure they know what the opinions of their clients are and then later incorporate them.Moreover, when introducing a new scheme or system, Big Data allows banks to make decisions that have been evaluated. They know the benefits just as well as they know the risks related to the scheme. So when implementing it, banks stay aware and prepared for all the possible risks. They also ensure they come up with a plan to counter the risks on time. This way they can enhance their own position and function better. BENEFITS OF BIG DATA TO THE BANKING INDUSTRYThere are several large scale benefits to using Big Data for the development of the banking industry. Following are few of the major and essential ones.Fraud DetectionAs the title itself suggests, Big Data allows banks to ensure that no unauthorized trade or transaction is being made. Thi s provides a protection and safety to all exchanges and makes a secure mark for the industry.Compliance ReportingWhen banks gain unlimited access to customer needs, it gives them the opportunity to help and provide for them in a better way. The business can be expanded by learning how to collect more revenue by cutting down costs. By cutting down of unnecessary costs, banks can provide the customers with what they are looking for and nothing insignificant.Customer SegmentationWhen a bank is provided with the insight to track and trace the habits of their clients of where and how they spend their money, it becomes easier for them to understand the clients’ needs. Once the results are in, clients can be categorized into different segments. This will suggest their banking needs and the marketing campaigns can be drawn up accordingly. This will also promote a healthier customer relationship.Risk ManagementThrough Big Data the information is easily located on a single large scale and i t becomes easier to reduce the number of risks. Everything that is needed by the banks becomes available at a central platform. This reduces the chances of them losing any information. It also helps them avoid being ignorant towards a fraud. They can easily detect them and in turn reduce all kinds of risks.Personalized Product OfferingsCustomer segmentation can help deliver newer schemes that directly involve the requirements of their clients. The past and present of their clients’ expenses is traced. With the help of this information banks can understand what policies will help them get a maximum response from their clients. This increases the revenue of the bank and helps to better relationships with the clients.Since Big Data is inclusive of many elements, it becomes easier for banks to provide increased security to their customers. They can detect and mitigate fraud whenever their systems detect its occurrence.CONCERNS RELATED TO BIG DATA IN THE BANKING INDUSTRYSilos of Data B lock a Single Customer ViewThere are several barriers present in organizations that minimize Big Data’s ability to be able to help them fully see the needs of their customers. This does not allow these industries to be able to reach their full potentials. Their customer view is then reduced and their schemes never strike their clients the way they should. These barriers do not allow banks to access information. This is considered the only unsolvable problem present in industries that reduces Big Data’s ability and function.The Skills and Development Gap Needs ClosingBig Data is unlike any other system which is why people operating it need to have some information about it. An individual uneducated in Big Data will struggle while handling it. This can make it slow down the bank and create a gap in customer bank communication. So unless the individual operating the system is educated enough, it becomes difficult to utilize the positive aspects of it.Lack of Strategic FocusA lot of banks intend to use and incorporate Big Data without realizing what it means. Big Data needs space to grow and be able to demonstrate the diversity of its nature. Alongside an educated individual dealing with the system, it also needs to be given space to grow. It is a complicated system and it must be met with so it can function fully. It has a great deal of potential and its approaches and concepts must be given more room to flexibly grow and expand.Privacy Concerns Limit the Adoption of Customer Data AnalyticsWhen Big Data provides such detailed information to a bank its responsibility increases as well. They must make sure that the information given to them is not misused in any way. The misuse of personal information makes it very difficult for the bank to keep up its progress. The banks must then take up the responsibility to protect the information. They must also keep in mind the privacy concerns of their clients and make sure that the personal information is not mistreated . It is important to ensure no personal information of any individual or details of a certain contract find its way in the wrong hands.It can be concluded that though Big Data is made to face a lot of obstacles in the industries, it is very beneficial. If it is taken up by industries it can be utilized with the right kind of knowledge. It assists the banks in taking steps and coming up with initiatives that provide guaranteed benefit. Big Data not only helps banks attract a bigger clientele but also helps them markedly reduce all frauds and secure their system. The expected growth and ability to eventually be able to explore the full potential of Big Data is also anticipated to result in the expansion of the banking industry.

Index of Economic Freedom Essay - 908 Words

The steady stream of current world events provides us with the signs to be read if we only take the time and the intention to pay attention to those lessons. The economic view provides a robust language for understanding the basic physical reality of scarcity, and the varied human beliefs and responses we take as we attempt to deal with that scarcity. As Smith and Ricardo teach us, the subtle and important understanding lies in the space between what human desires dictate in the personal scale, in the short run; and what consequences play out on the grand scale, in the long run. In this paper we look at the proxy effects of policies intended by governments to forge policies in the short run, as compared to the inevitable effects that flow†¦show more content†¦The following table shows the results: Fraser Institute : Heritage Index Correlation: 0.9276 Ease of Doing Business, World Bank : Heritage Index Correlation: 0.9606 Freedom House, Civil Liberties Index : Heritage Index Correlation: 0.8205 This data makes clear that the Heritage Index, even if biased, correlates strongly with measures from other organizations, calculated with different methodologies, for different purposes. The Heritage Index is calculated using ten sub-indices as measures of relevant components of economic freedom. These sub-indices produce an overall composite score. The list of indices and some detail on what they measure is as follows: Business Freedom; the general barriers to entry and exit of the market, procedures, licenses, time, cost, and difficulty in starting a business. Trade Freedom; tariffs and quote barriers to import and export, price, investment, and regulatory restrictions, as well as direct government intervention such as domestic subsidy. Monetary Freedom; measures of price stability and price controls, inflation rate over a three year period. Investment Freedom; the ability to transfer money and investments across borders, transparency and bureaucracy, nationality prescreening, res trictions on land ownership, industry sector restrictions, expropriation or nationalization without compensation, access controls on foreign exchanges,Show MoreRelatedCorruption Of Corruption And Corruption1445 Words   |  6 Pagesgreat amount of corruption and their relationship with the people. First, it will help to look directly at countries who have still sustained with such tough conditions. Second, the relationship between economic growth will be explored in relation to social well-being. 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Thus, levels of prosperity and wealth also vary widely across the globeRead MoreDifferent Subjective Assessment Of Quality Of Living1186 Words   |  5 Pageslocation, factoring in the interaction of political, socio-economic and environmental factors. For this ranking, New York serves as the base city and all other cities are ranked relatively Table ‎1 3 shows the categories and criteria that Mercer quantifies for each city. (Khalil, 2012) Index Criteria Political and social environment Relationship with other countries Internal stability Crime Law enforcement Ease of entry and exit Economic environment Currency exchange Regulations Banking servicesRead MoreMacro And Micro Of Sweden And United States811 Words   |  4 PagesMacroeconomics? Macroeconomics is the part of economics study that looks at the operation of a nation’s economy as a whole (30). Macroeconomics main focus is the production and consumption in an economy. Economists that specialize in macro investigate the effects of general taxes such as income and sales taxes output and prices. They also study the causes of economic upswings and downturn and the effects of monetary and fiscal point policy on economic wealth. Macroeconomists also determine how interestsRead MoreMarket Potential Indicator966 Words   |  4 Pagesrecent progress they have made in economic liberalization using eight dimensions. Each dimension is given a weight to contribute to the overall market potential index. Construction of the index The market potential index was first constructed using only seven dimensions and was given the following weights: Market Size (4/20), Market Growth Rate (3/20), Market Intensity (3/20), Market Consumption Capacity (2/20), Commercial Infrastructure (2/20), Economic Freedom (2/20), Market Receptivity (4/20)

Feminity and Sexual Desires in The Storm by Kate Chopin

The Storm is a story Written by Kate Chopin. The time at which the story was set is in the 19th century. The story was set in Louisiana and in real sense, Louisiana is a place that is best known for violent storms. The story is about is a confrontation of the theme of femininity and complexities of the married people in the storm. The storm is used throughout the story, and it only ends after the characters, Alcee and Calixta’s sexual encounter, which brings out the theme of femininity, and sexual desires. Kate Chopin opens her story with the storm brewing, which keeps Calixta’s husband away from her stormy passions. She makes the storm so natural that one can never foreshadow what the storm symbolizes. The storm is always depicted as something dangerous, and destructive, but Chopin depicts the storm in her story as something that is good, and fulfilling. The characters were happy, and fulfilled after the storm ended instead of it causing destructions. Chopin introduce s of theme of femininity, and its complexities in marriage, a theme which she later develops throughout the story using the image of the storm. She presents the image of the Storm in its natural life when she says, the storm kept Bobinot away from his wife Calixta, because he had to stay in the general store, until it ends. However, one has to search for the real meaning of the story as it extends. Chopin uses the image of the storm in the story. There is an underneath meaning of the storm in this story

Learning Styles †VAK Free Essays

VAK theory is widely recognised by teachers – particularly those who recommend accelerated learning techniques – but the idea that we receive information via different modes has been around considerably longer than that and can be traced back to the work of Grace Fernald (â€Å"Remedial Techniques in Basic School Subjects†) who promoted kinaesthetic learning techniques; Samuel Orton’s work on dyslexia; Anna Gillingham’s subsequent work on developing multisensory approaches; and the holistic educational philosophy of Maria Montessori. (WWW. brainboxx . We will write a custom essay sample on Learning Styles – VAK or any similar topic only for you Order Now co. k) We all learn in different styles it’s just a case of finding out what style suits the individual learner to get the best results out of them, for example if you were to teach a learner that is best suited to Kinaesthetic learning, by giving them just handouts and talking, they will lose concentration much quicker and wont absorb any of the information you are giving them, whereas if you keep them engaged by maybe doing something as simple as giving out the handouts or give a little demonstration of what their learning they will keep engaged therefore absorbing the information for longer. There are three types of learning styles, Visual, Auditory and Kinaesthetic. Someone that requires a Visual learning style would prefer to learn using drawing, sketches, handouts, charts etc. An Auditory learner would prefer to learn with the aid of listening and then answering questions about what they have just heard, stories (maybe of past experiences), anecdotes, puns etc. Whereas someone that learns in a Kinaesthetic way would learn in a much more hands on way by building things, taking something apart and re-building to see how it works, using their hands, being able to move around etc. When setting up your lesson plan you will have to cater for all three learning types and maybe you could already have an idea on how to get the best out of your group of learners that use all three types, for example: if you have a few Kinaesthetic learners in a group or discussion session you could keep them engaged by asking them to come up and give a demonstration, or if you’re in a workshop environment you could give a demonstration for all, whilst verbally explaining what you’re doing and achieving, therefore also catering for Visual and Auditory learners. The main thing is to keep the learners engaged. (As Geoff petty says in Teaching Today) it is better to have 80 per cent concentration on a moderately effective method than 10per cent on a supposedly brilliant one. How to cite Learning Styles – VAK, Essays

To what extent was Mikhail Gorbachev responsible for the collapse of communism in the Soviet Union free essay sample

Many Historians contributed the fall of Soviet Union directly to Mikhail Gorbachev and his reforms. They argued that Gorbachev’s Glasnost, (openness) and Perestroika, (restructuring) directly led to uprisings within the Soviet Union, and its Soviet republics that brought the downfall of Soviet Union. This is however a very shallow analysis of the downfall of the Soviet Union. For one to truly understand the fall of the Soviet Union one must understand the history of The Union of Soviet Socialist Republics (USSR), and the political and economic situation of the former empire when Gorbachev took reign. For example the USSR’s stagnating economy compounded with the arms race, ethnic tensions, war in Afghanistan, as well as the communism ideology itself, all played great roles in the downfall of USSR. Gorbachev’s reforms merely torpedoed an already sinking ship, as the Soviet Union was doomed to fail at the moment Stalin took over. We will write a custom essay sample on To what extent was Mikhail Gorbachev responsible for the collapse of communism in the Soviet Union? or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page Gorbachev’s reforms also played a part in the dissolution of the Soviet Union. He tried to combat all these problems with glasnost and perestroika, however it was too radical at the time and was too little too late. The reforms themselves were flawed, as they were too radical and not properly implemented. Gorbachev’s reforms were literally the polar opposite of the policies of his predecessors. This radical change plunged the USSR into chaos and eventually the fall of the Soviet Union. If one were to truly comprehend the cause of the Soviet Union’s downfall one needs to understand the economic and political situation of USSR before Gorbachev came into power. One may argue that the Soviet Union was doomed to fail from the moment Stalin took power. The command economy under Stalin took the capitalist world by surprise. Large scales of industrialization, and economic growth had many critical flaws as one Polish economist states, â€Å"state socialism was not a good idea badly implemented, but a bad idea which was implemented surprisingly well† (Mazower, 329). The command economy model produced a ratchet effect: over production by an institution in a given plan cycle, causing even more over production in the next plan cycle to match the new expected standard (Jefferies, 15). This is very problematic as it reduced the quality of Soviet goods as workers were rushed to complete the plan at the end of a planning cycle. This in turn had two detrimental effects. First Soviet goods were shielded from the world economy, as the quality was too poor. Second, when Soviet goods were rejected from the world market, citizens of the Soviet Union were targeted as consumers (Jefferies, 22). This meant that the consumers were neglected, and as information technology progressed the people of USSR began to see the gap between their standard of living, and that of the West. This caused citizens to become disgruntled. Another critical flaw that command economy possessed was its inability to adapt to the dynamic world economy as it was run according to state plan not to the market as it was supposed to. This can be seen very clearly in the Soviet Union’s economy. A prominent example to illustrate this point is the effect of the arms race on the Soviet economy. By 1980 Soviet Union’s defense budget raised from an already horrifying 22 percent to 27 percent its GDP compared to 7% of the U. S. (Hilton). The United Stated solved its problem by amalgamating a unique relation with its private defense sectors. The Soviet Union however did not have such option as everything was run by the state, and the defense budget continued to sap an already crumpling economy, eventually to its grave. The political situation of the Soviet Union when Gorbachev came to power in 1982 was dire. There had been much history of tension between the people of the satellite states, and the installed governments of the Soviet satellite states. A prime example of this was the failed Hungarian revolution. In October 1956 hundreds of thousands of people rose up against Erno Gero’s regime. The people succeeded in toppling Erno Gero’s rule, but despite their success, the unfortunately met with Soviet tank columns. After the Soviet intervention a new government under Janos Kadar was installed. Wide oppression soon followed, and hundreds of thousands of refugees fled the country, tens of thousands were imprisoned, and hundreds were executed (Holodkov, 1956). One decade later in 1968, Prague Spring saw the invasion of 200,000 Warsaw pact troops. Although there was not much bloodshed, the message was clear, radical democratization will be replied in force by the Soviet Union (Zaninovich, 1973). From these two failed uprisings one thing was clear, the oppressive governments of the Eastern bloc were doomed to fail. These regimes were installed and backed by the Soviet Military instead of the people. These governments had no public support, and at worst they were not even considered legitimate by the people. Instantly, when the progressive leader came into power, all citizens were soon silenced. With all the oppression, bloodshed and historical grievances, there had ought to be tremendous animosity between the people of the satellite states and the USSR. When the economy of the Soviet Union began to crumble, and when it could no longer afford backup by the dictators they had installed, Eastern Europe was a powder keg waiting to explode. When Gorbachev came into power he initiated a series of economic and political reforms known as Perestroika (restructuring). Perestroika granted a great degree of autonomy to state enterprises. These enterprises for example were free to determine output levels (they still had to fulfill state orders), sell their own goods and the freedom to import their own material (Rogers, 1987). Essentially managers could run their enterprises however they saw fit without much state intervention. Perestroika allowed foreign capital to flow into the Soviet Union, and even permitted ownership of private property (David, 1987). These reforms seemed like a huge step toward repairing the sluggish Soviet economy, however in practice it created much chaos and eventually put the final nails in the economy of USSR. There were numerous reasons of how Gorbachev’s perestroika failed to keep the Soviet Union intact. One main and most prominent reason of how perestroika failed to repair the USSR’s economy is that the economy needed fundamental changes not just some modifications (Hosking, 207). Gorbachev merely wanted a human face on socialism so that it would make it more efficient. He never desired to transform the system to free market economy. For example, perestroika gave enterprise managers control over their plants and provided them incentives to produce better quality goods which would translate to a profit for the plant, and in return boost the Soviet economy. However in practice it yielded opposite results. Most managers were incompetent to run their respective enterprises, as they were inexperienced with such degree of freedom. What they often did was they ran their plants the same as before, which resulted inevitable bankruptcy (Hosking, 209). Instead of boosting the economy successfully, perestroika essentially drained the economy. The government’s spending increased drastically as they had to bail out an increasingly number of failed enterprises. This coupled with two decades of stagnation, the war of Afghanistan and a horrifying defense budget, the economy of the Soviet Union soon collapsed. Perestroika also had a huge impact on the moral of the Soviet citizens. In perestroika Gorbachev introduced the concept of privatization for the first time since Lenin’s New Democratic Party (NDP). Although this seemed like a step toward a brighter future for the Soviet Union it created much tension between the government and its people. Gorbachev’s introduction of such a concept contradicted his vehemently commitment to preserve Marxist- Leninist principles which he clearly stated in his speech to the CPSU in 1985 (Rogers, 1987). In retrospect he was trying to maintain the socialist ideology while moving toward a market approach economy. This proved disastrous, as the two economic systems could have never been compatible with each other. Free market economy inevitably led to inequality of wealth and the creation of a capitalist class who exploited the proletariat. This is exactly what happened in the Soviet Union after perestroika was implemented. A small number of enterprises did become successful and their profiteering was much resented by the general population (Hosking, 215). This resentment along with the failure of perestroika, as well as shortage in basic essential items (bread and milk) much discredited Gorbachev’s rule among the Soviet people. The citizens of the USSR became increasingly more demoralized as shelves were empty and saw the grim picture that perestroika painted for the ordinary folk. Elena Bonner the widow of Andrei Sakarov in November 1988 said that she lost all faith in perestroika as she stated â€Å"I was always was a believer†¦ but today my faith in Perestroika is waning. † (Hewitt, Winston 145). Along with Perestroika, Glasnost (openness) was also introduced within the Soviet Union. The goal of glasnost was to make Soviet Union more transparent by allowing freedom of speech, press, basically opening the country for debates and criticisms (Rogers, 1987). This meant Soviet citizens no longer had to fear neighbors turning them into the KGB or arrested and exiled for negative thoughts against the state. Gorbachev even allowed some form of democracy within the Soviet Union by allowing the people to vote for CPSU candidates. Glasnost had unintended consequences however. In a regime based on oppression and propaganda, allowing freedom in any sort will discredit the regime. By removing ideology control the party (CPSU) had to depend on the people’s support in order to stay in power (Rogers, 1987). This however was unachievable as Perestroika was moving the country toward a more bourgeoisie, capitalist society. In addition giving the people freedom of speech and removing media censorship all the atrocities committed by the Soviet Union were realized. It was inevitable that there will be extreme hostility toward those who were responsible, Namely the USSR who has been oppressing the people of Eastern Europe for more than 40 years (Kagarlitsky, 103). The tension between the people of the satellite states and the Soviet Union will no doubt intensify and eventually there will be nothing the Soviet Union could do to control the people of Eastern Europe. Nationalist movements swept across the Soviet satellite states like a storm and one by one these states declared independence. This time without the backing of the Soviet military the communist dictators fell like dominoes. Gorbachev could do nothing but watch as the Soviet Union was in no shape to intervene especially after its defeat in Afghanistan. Glasnost was too sudden and too radical and it isolated Gorbachev from the people instead of bringing him closer to the public. One important point worth mentioning that contributed to the downfall of the Soviet Union was the communism ideology itself. Gorbachev unlike his predecessors was born and raised under the communist ideology. He was educated within the Soviet system ad had no recollection of the past. So in theory he should be the fruit of communist propaganda. However the fact that Gorbachev chose to implement reforms and move the country toward democracy revealed the incompatibility of communism and human nature. Karl Marx’s idea that with time communism would led to the creation of an utopia where everyone is equal and there will be no incentive to compete was proven false. Inequality and competition are embedded in the human nature and to ignore this is to ignore oneself. It is true that Mikhail Gorbachev and his reforms played great parts in the downfall of the Soviet Union. However one cannot ignore other aspects that brought down the USSR namely the obsolete command economic model and the repression imposed on the people for more than 70 years. Perestroika failed to restructure the economy as it merely sought to modify it instead of replacing it. Glasnost failed to keep the Soviet empire together as it was too sudden and radical. Finally the communism ideology itself proved to be incompatible with human nature. All of this coupled with some bad decisions made by Gorbachev’s predecessors e. g. the war in Afghanistan ultimately brought down the Soviet Union that ruled Russia for more than 70 years.

The lengths of lines are easier to guess than angles. Also, that year 11s will be more accurate at estimating. Essay Example

The lengths of lines are easier to guess than angles. Also, that year 11s will be more accurate at estimating. Essay Example The lengths of lines are easier to guess than angles. Also, that year 11s will be more accurate at estimating. Essay The lengths of lines are easier to guess than angles. Also, that year 11s will be more accurate at estimating. Essay In this investigation, 3 year groups years 9, 10 and 11, were asked to estimate the lengths of some lines and angles, and the results that the pupils produced are going to be analysed to try and prove or disprove the hypothesis of:The lengths of lines are easier to guess than angles. Also, that year 11s will be more accurate at estimating.The reasons I think these things are because people are more used to seeing lines than they are angles, so this could mean that they are better at estimating the length of lines. The reason I think they year 11s will be more accurate is because they have done maths longer than the year 9s, so they have had more experience.I will be using an example of one line, and one angle, and the results of Year 9 and Year 11 estimates. This is secondary data which has been previously recorded, during a survey to find out the estimates that the pupils gave. This data is continuous as it is As there are 117 year 9s and 145 year 11s I will have to reduce the size of my sample as these numbers are too large to handle, so I will be using a stratified method to reduce the size of the samples as this method keeps the results for the year groups in proportion to each other.I am going to be sampling 60 people in total, out of the year 9s and year 11s, as this is a manageable amount, and it can represent the data from the two year groups accurately as a smaller number might not show the difference in results suitably.To choose my samples I am first going to add together the two total numbers of each year group, which is:145 + 117 = 262 (Year 11 / Year 9)Then I am going to do some calculations. For the year 11s I am going to do:(145 / 262) x 100 = 55.355.3 is about 55 %This means I need to have 55% of the sample of 60 from year 11s results. 55% of 60 is 33, so I need 33 samples to be Year 11 samples.For The Year 9s I am going to do:(117 / 262) x 100 = 44.644.6 is about 45%This means that 45% of the sample of 60 need to be Year 9 results. 45% of 60 is 27, so I need 27 Year 9 samples, which gives you the total of 60 samples.To get these 60 samples from the 262 results I am going to use a random systematic method. To do this I will use a random number generator to find a number from the year 11 and year 9 data, and I am then going to count down from that number, and every 7th piece of data I am going to use. (As 7 was the number that came up when I used a random number generator to find a number between 0 and 10.)Year 9 Random Number Generator91 was the number the generator produced for the year 9s, so I am going to use the 91st piece of data, and then every 7th piece of data after that I am going to use until I have my 27 pieces of data. So, the numbers I am going to use are:91, 98, 106, 113, 3, 10, 17, 24, 31, 38, 45, 52, 59, 66, 73, 80, 87, 94, 101, 108, 115, 5, 12, 19, 26, 33, 40Year 11 Random Number Generator127 was the number the generator produced, so I am going to start from the 127th piece of data and count down 6 piece s of data (as 6 was the number produced from the generator between 0 and 10) and then every 6th piece of data after that I am going to use until I have the 33 pieces of data I need for the year 11s. So the pieces of data are:127, 133, 139, 145, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96, 102, 108, 114, 120, 126, 132, 138, 144, 5, 11, 17, 23, 29Once I have collected my samples, I am going to draw some grouped frequency tables, which will also have frequency density on. These tables are there so that I can find the mean from grouped data. Also, because the data is put into groups it is easier to handle. I will also find out the spread of the data from the mean using standard deviation.Then, I am going to draw some histograms, using the frequency density from the grouped frequency tables. These will show the density of the data in certain groups. This shows which group had the most estimates in it.Next, I am going to draw some cumulative frequency tables and curves. These will show the inter-quartile range. This shows the range of the quartiles. Also, from the cumulative frequency curves I can draw some box plots. These will show the inter-quartile range, median and lower and upper quartiles in a more compact and easy to read way.Then, I am going to draw some percentage error tables. These will show the error of the estimates and if people estimated below or above the actual size or length of the line.I am then going to draw some scatter graphs showing the errors from the percentage error tables. From these you will be able to see if some guessed below the actual length of the line, and whether or not they guessed below the size of angle as well.Then I am going to draw stem and leaf diagrams for each year group. From these I will be able to find the median and mode. Stem and leaf diagrams show all the data in an easy to read way.Finally, I am going to find The Spearmans Coefficient of rank. This shows whether or not there will be negative or po sitive correlation in the scatter graphs which I will then draw. These will show the estimates of the line for one individual person plotted against their estimate for the angle. From these scatter graphs you can see whether or not anybody guessed exactly the correct size or length.These things should help me prove or disprove my hypothesis.I have recorded the estimates in a table so as to know which pieces of data I am using they have been highlighted. I am using ICT to do parts of my work as it spreadsheets can work things out extremely quickly, but I will also check and record how I would work things out.First of all I am going to draw some grouped frequency tables, which also show frequency density. This will make the data easier to handle and will mean I can draw a histogram, and grouped frequency graphs. Also, from the frequency tables I am going to find the mean, and I am also going to use standard deviation to find the spread of the number from the mean. If the spread is sma ller, it means that the year group guessed closer to the mean value. An advantage of using standard deviation is that you use all of the data.This is a table to show the Year 9 estimates for the length of line 2.Estimate of length (cm)Frequency (f)Class Width (w)Frequency Density (f) / (w)Mid-Point (x)(f) x (x)3 ? cm ; 43133.510.54 ? cm ; 4.540.584.25174.5 ? cm ; 550.5104.7523.755 ? cm ; 68185.5446 ? cm ; 77176.545.57 ? cm ; 902080Total27140.75The average of results from the above table is the total (f) x (x) column divided by the total frequency. This is 140.75/27=5.21cm. This is 0.61cm longer than the actual length of the line. This is not very much, which means the year nines were quite accurate in estimating the length of the line.To find the spread of this data from the mean I am going to use the equation for standard deviation from grouped data, which is:Efxà ¯Ã‚ ¿Ã‚ ½ (mean)à ¯Ã‚ ¿Ã‚ ½EfSo, for the above table I would do:(33.5à ¯Ã‚ ¿Ã‚ ½)+(44.25à ¯Ã‚ ¿Ã‚ ½)+(54.75à ¯Ã‚ ¿Ã‚ ½)+(85.5à ¯Ã‚ ¿Ã‚ ½)+(76.5à ¯Ã‚ ¿Ã‚ ½)+(0x8à ¯Ã‚ ¿Ã‚ ½)27This equals 28.13194, which I will now subtract the meanà ¯Ã‚ ¿Ã‚ ½ from this. The meanà ¯Ã‚ ¿Ã‚ ½ is 5.21à ¯Ã‚ ¿Ã‚ ½, which gives 27.1441. I will subtract this to give me 0.987844, which I now find the square root of this answer, which is 0.9939. This is the spread of data from the mean. This is quite a low spread.This is a table to show the Year 9 estimates for the size of angle 6.Estimate for Angle sizeFrequency (f)Class Width (w)Frequency Density (f) / (w)Mid-Point (x)(f) x (x)20 ? à ¯Ã‚ ¿Ã‚ ½ ; 301100.124.524.530 ? à ¯Ã‚ ¿Ã‚ ½ ; 35350.6329635 ? à ¯Ã‚ ¿Ã‚ ½ ; 40751.43725940 ? à ¯Ã‚ ¿Ã‚ ½ ; 45751.44229445 ? à ¯Ã‚ ¿Ã‚ ½ ; 50450.84718850 ? à ¯Ã‚ ¿Ã‚ ½ ; 1005500.174.5372.5Total271234The mean of the above table is 1234/27=45.70à ¯Ã‚ ¿Ã‚ ½. This is 12.7à ¯Ã‚ ¿Ã‚ ½ bigger than the actual angle. This is quite a large amount which means that the year 9s were not very accurate in their estimates of angle 6. They we re better at estimating the length of the line.To find standard deviation from this I will do:(124.5à ¯Ã‚ ¿Ã‚ ½)+(332à ¯Ã‚ ¿Ã‚ ½)+(737à ¯Ã‚ ¿Ã‚ ½)+(742à ¯Ã‚ ¿Ã‚ ½)+(447à ¯Ã‚ ¿Ã‚ ½)+(574.5à ¯Ã‚ ¿Ã‚ ½)27This gives the answer of 2303.4, which I will now subtract 45.70à ¯Ã‚ ¿Ã‚ ½ from to give 214.91. I then have to find the square root of this answer. This gives 14.7. This spread is quite high, which means that the estimates given by the year 9s for the size of angle 6 was quite big. You can also see that the year nines had a lower spread of data for the length of line 2.This is a table to show the Year 11 estimates of the length of line 2.Estimate of length (cm)Frequency (f)Class Width (w)Frequency Density (f) / (w)Mid-Point (x)(f) x (x)3 ? cm ; 43133.510.54 ? cm ; 4.590.5184.2538.254.5 ? cm ; 530.564.7514.255 ? cm ; 6141145.5776 ? cm ; 72126.5137 ? cm ; 9221816Total33169The mean of the above table is 169/33=5.12cm. This is 0.52cm bigger than the actual length of the line. This is very low, which shows they were quite accurate in their estimates and were better than the year 9s.To find standard deviation from this I will do:(33.5à ¯Ã‚ ¿Ã‚ ½)+(94.25à ¯Ã‚ ¿Ã‚ ½)+(34.75à ¯Ã‚ ¿Ã‚ ½)+(145.5à ¯Ã‚ ¿Ã‚ ½)+(26.5à ¯Ã‚ ¿Ã‚ ½)+(28à ¯Ã‚ ¿Ã‚ ½)33This gives me an answer of 27.36, from which I will now subtract the meanà ¯Ã‚ ¿Ã‚ ½, which is 5.12à ¯Ã‚ ¿Ã‚ ½. This is 26.22, and subtracted from the precious calculation, the answer given is 1.14, which I then find the square root of, which gives me an answer of 1.07. This is a low spread of data, but not as low as the year 9 estimates of the length of line 2. The year nines estimates were therefore more close together.This table shoes the Year 11 estimates for the size of angle 6.Estimate for Angle sizeFrequency (f)Class Width (w)Frequency Density (f) / (w)Mid-Point (x)(f) x (x)20 ? à ¯Ã‚ ¿Ã‚ ½ 301100.124.524.530 ? à ¯Ã‚ ¿Ã‚ ½ 35650.83219235 ? à ¯Ã‚ ¿Ã‚ ½ 405513718540 ? à ¯Ã‚ ¿Ã‚ ½ 45850.64233645 ? à ¯Ã‚ ¿Ã‚ ½ 511360.547.5617.5Total331355The mean of the above table is 1355/33=41.06à ¯Ã‚ ¿Ã‚ ½. This is 8.06à ¯Ã‚ ¿Ã‚ ½ bigger than the actual angle, which shows that the year 11s again were better at estimating the size of the angle, the year 11s were better at estimating the length of the line though.I will now work out the standard deviation for this:(124.5à ¯Ã‚ ¿Ã‚ ½)+(632à ¯Ã‚ ¿Ã‚ ½)+(537à ¯Ã‚ ¿Ã‚ ½)+(842à ¯Ã‚ ¿Ã‚ ½)+(1347.5à ¯Ã‚ ¿Ã‚ ½)33This gives an answer of 1728.26, which I now subtract 41.06à ¯Ã‚ ¿Ã‚ ½ from to give 42.33. I now have to fins the square root of this to find the final standard deviation. The square root is 6.5. This means the spread is smaller than the spread of the year 9s estimates of angle 6, but the year 11s had a lower spread of data for the line estimates. This means more year 11s guessed closer to the mean for the line. This was also the case for the year 9s.These are the histograms to show these details. The histograms show the spread and how densely populated each group of data is. To draw these histograms, I needed to find the frequency density. The grouped frequency tables on pages 3, 4 and 5 show this. To find the frequency density I did frequency divided by class width.From the histograms, you can see the dashed line. This line shows the actual length of the line, or size of the angle. From the year 11 and year 9 histograms from the angle data, you can see that the year 9s have a wider range of results because they have a very large group of data at the end of the histogram. The density of the group where the actual size of the angle is, is not very densely populated, which means not many people guessed within the correct class.From the histogram for the line data you can see that for the year 9 data the group where the actual length of the line was, had the highest frequency density, but was not the most densely populated. The year 11 data shows that not many people guessed in the correct group as it is not very dense.Cumu lative frequency tables group the data so you can see how much the data has gone up from group to group. A curve can then be drawn, and then a box plot can also be drawn.This is a cumulative frequency table to show the year 11 and the year 9 estimates for the length of line 2. These are drawn so I will be able to find the inter-quartile range of the data after a cumulative frequency curve has been drawn. The median can also be found.Estimated length; 4cm; 5cm; 6cm; 7cm; 8cm; 9cmCumulative Frequency Year 11s31529313133Cumulative Frequency Year 9s3122027This is a table to show the cumulative frequency of the year 11 and year 9 estimates of the size of angle 6.Estimated Size (à ¯Ã‚ ¿Ã‚ ½);30;40;45;50;60;70;100Cumulative Frequency Year 9s1111822252627Estimated size (à ¯Ã‚ ¿Ã‚ ½)3040455060Cumulative Frequency Year 11s112203233From these tables I was able to find the median and modal class intervals. The median class interval shows where the middle estimate is, and the modal class inter val shows which interval has the most estimates in it.These are the median class intervals:AngleYear 9: ;45 class interval. I found this by finding the middle of the total frequency and finding which interval that would be in.Year 11: ;45 class interval again. This shows the median of both results is in the same class interval.This is not the interval where the actual size of the angle would be. The actual size of the angle would be in the group ;40.LineYear 9: ;5 class intervalYear 11: ;5 class interval againThis is the correct group for where the actual length of the line is.The modal class intervals are:AngleYear 9: ;40 class interval. I found this by looking which interval had the most pieces of data in.Year 11: ;50 class interval.The year group who had the majority of estimates closest to the size of the angle were the year 9s, as their modal class was the correct class where the actual angle length could be found.LineYear 9: 6cm class intervalYear 11: 6cm class interval again. These show that the majority of people in both year groups guessed around the same number, but it was not the correct class interval of 5cm.These are the cumulative frequency curves to represent this data. Underneath each curve is a box plot. This shows the inter-quartile range of the data.This curve shows the cumulative frequency of the year 11 estimates for line 2.To work out the median from this curve, you add 1 to the total cumulative frequency and then divide by two. This means, for this particular curve I am going to do 33 + 1 = 34, so 34 / 2 = 17. This means I have to find the 17th piece of data. To do this I find 17 on the y axis, draw a line along until I meet the curve, then draw a line down the x axis, which gives me the median. Also, to find the Inter-quartile range I need to find the lower quartile and upper quartile. To do this I halve the median frequency, and follow along until I meet the curve, then draw a line down to the x axis. This gives me the Lower quartile. I then find the Upper quartile which means I add the lower quartile value from the y axis to the median value from the y axis which gives me the value for the y axis for the upper quartile. I then do as before and draw the lines.The LQ for this curve is approximately 4.5 cm.The median is approximately 5.1 cm.The UQ is approximately 5.7 cm.This means that the IQR is 5.7 4.5 which equals 1.2 cm.This next curve is for the year 9 estimates of line 2. The box plot on this curve actually shows that the lower quartile is below the actual length of the line. This is because people guessed a smaller length than the actual one of the line.The LQ for the last curve is approximately 4.5 cm.The median is approximately 5.2 cm.The UQ is approximately 6.1 cm.This means that the IQR is 6.1 4.5 which equals 1.6 cm.The next curve is for the year 11 estimates of angle 6.The LQ for this curve is approximately 38à ¯Ã‚ ¿Ã‚ ½.The median is approximately 43à ¯Ã‚ ¿Ã‚ ½.The UQ is approximately 47à ¯Ã‚ ¿Ã‚ ½.This means that the IQR is 47 38 which equals 9à ¯Ã‚ ¿Ã‚ ½.The next curve is for the year 9 estimates of angle 6.The LQ for this curve is approximately 37à ¯Ã‚ ¿Ã‚ ½.The median is approximately 42à ¯Ã‚ ¿Ã‚ ½.The UQ is approximately 48.5à ¯Ã‚ ¿Ã‚ ½.This means that the IQR is 48.5 37 which equals 11.5à ¯Ã‚ ¿Ã‚ ½.From these curves I was able to find out that the IQR for the angles in both year groups was higher than the IQR for the line. Also, I found the year 11s had lower IQRs in both of the angle and the line estimates. This could suggest that the year 11s were better at estimating.Percentage error tables show what errors people made. To find the actual percentage error for the above table you have to do a calculation which is:(error/actual length of line) x 100This is a percentage error table for the year 9 estimates of the line and angle.LINE ANGLEEstimate (e)ErrorPercentage error (%)Estimate (e)ErrorPercentage error (%)61.430.43602781.826.21.634.78532060.614.5-0.1-2.173 526.0661.430.433526.0661.430.433526.0650.48.709562187.884-0.6-13.0440721.2150.48.70501751.5261.430.4340721.214-0.6-13.0430-3-9.094-0.6-13.0420-13-39.3950.48.70451236.3650.48.7040721.2150.48.703526.064-0.6-13.043526.0661.430.4340721.214.600.0030-3-9.0950.48.703526.064.5-0.1-2.1740721.214.5-0.1-2.17451236.363.5-1.1-23.91431030.303.5-1.1-23.91431030.303-1.6-34.78501751.524.5-0.1-2.17451236.3661.430.4330-3-9.0950.48.703526.0650.48.70451236.36Total6.6143.48Total238721.21The mean percentage error is worked out by dividing the total percentage error by how many pieces of data there are. So, for the mean of the year 9 line errors, the calculation would be 143.48/27 which equals 5.31%. This is quite a low percentage of error which means that the year 9s were quite good at estimating the line. For the angle it would be 721.21/27 which equals 26.71%. This percentage is so high because someone estimated 95à ¯Ã‚ ¿Ã‚ ½, which means that mean is made higher by this anomalous result. This shows th at the year 9s were better at estimating the length of the line.The next table is for the percentage error of year 11 estimates.LINE ANGLEEstimate (e)ErrorPercentage error (%)Estimate (e)ErrorPercentage error (%)50.48.7040721.2150.48.703526.0650.48.7040721.214-0.6-13.04451236.3650.48.70451236.3650.48.70451236.3650.48.70451236.363-1.6-34.7840721.2150.48.70461339.3950.48.7030-3-9.094-0.6-13.0430-3-9.093.5-1.1-23.9140721.214.5-0.1-2.1729-4-12.1250.48.70451236.364-0.6-13.0430-3-9.094.5-0.1-2.1730-3-9.093-1.6-34.7830-3-9.094-0.6-13.043526.0683.473.9140721.214-0.6-13.0430-3-9.0983.473.9140721.214-0.6-13.04451236.3661.430.43451236.3650.48.70451236.3650.48.703526.0650.48.70501751.524-0.6-13.04451236.364-0.6-13.043526.064-0.6-13.04451236.3650.48.703526.064.5-0.1-2.1740721.2151.430.43451236.3661.430.4340721.21Total6.2134.78Total206624.24The mean for the above table of the line errors would be 134.78/33 which equals 4.08%. This is a relative low percentage which means from the mean there doesn t seem to be much error for the line. For the angle the mean is 624.24/33 which equals 18.92%. This means that from the mean you can see that from the year 11 data, they were better at estimating the length of the line.From these calculations, I have found that the year 11s were better at estimating by finding the mean percentage error, as both of the mean percentage errors for the line and angle, were lower than the year 9 errors.From the percentage error tables I can plot a scatter graph to see how much error there was for the angle compared to the line for both year groups. These graphs were drawn from the actual error made. For example, if someones error for the line was -2, because they guessed 2 below the actual angle size, and their error for the angle was 3, because they guessed 3 above the actual length of the line, then the co-ordinate for their error would be (-2,3).This scatter graph shows the error of the year elevens. If someone had estimated exactly right and therefor e had no error then there mark would be at the point (0,0) on the scatter graph. The point circled in pink is an anomalous result. This is a result which is out of the pattern of the rest of the results.This scatter graph shows the error of the year 9 estimates. The pink circled result is again an anomalous result.Stem and leaf diagrams put the data in numerical order in an easy to read table. This is a stem and leaf diagram to show Angle estimates:Year 9ANGLE 6Year 110295,5,5,5,5,5,5,0,0,030,0,0,0,0,0,5,5,5,5,55,5,5,5,3,3,0,0,0,0,040,0,0,0,0,0,0,0,5,5,5,5,5,5,5,5,5,5,5,63,0,050067859KEY 0 2 9 means that the year 9 estimate was 20 and the year 11 estimate was 29.This is a stem and leaf diagram to show the Line estimates:YEAR 9LINE 2YEAR 115,5,030,0,56,5,5,5,5,0,0,0,040,0,0,0,0,0,0,0,0,5,5,50,0,0,0,0,0,0,050,0,0,0,0,0,0,0,0,0,0,0,0,02,0,0,0,0,0,060,080,0KEY 0 4 0 means the year 9 estimate was 4.0 and the year 11 estimate was 4.0 also.On the stem and leaf diagrams I have highlighted s ome of the values. These are the medians for each stem and leaf diagram. For the Year 11 line, the median is 50, for the year 11 angle it is 40. For the year 9 line the median is 50 and the median for the estimates of the angle by year 9s is 40.I can also find the mode for each group of data. The mode of the estimate of the year 9 line is 5.0 . The mode for the year 11 estimates for the line is also 5.0 . This shows that in both year groups most people guessed the same value, which means, from this mode, you cannot see who is better at estimating.The mode for the year 9 estimates of the angle is 45 and the mode for the year 11s is 40. This shows that the mode for the year 11s is closer to the actual value of 33. This could suggest that the year 11s are better at estimating in this instance.I can also find the range from the stem and leaf diagrams, by subtracting the smallest value from the largest.The range for the year 9 angle estimates is 95 20 which equals 75, which is the range . For the year 9 line the range is 6.2 3.0 which gives you 3.2 as the range.The year 11 range for the angle is 50 29 which equals 21. For the line it is 8.0 3.0 which equals 5.0.From the ranges you can see that the year 9 angle estimates were more spread out than the year 11 angle estimates, but the year 11 line estimates were more spread out than the year 9 line estimates.These tables are made for use in with the scatter graphs. They show Spearmans Coefficient of Rank. This is basically to find the correlation of the data.This table is for the year 9 data.Year 9 Estimate of lineEstimate of angleLine RankingAngle RankingDifference (d)dà ¯Ã‚ ¿Ã‚ ½4.5608.526-17.5306.256.3532725244.5358.580.50.2553517.589.590.2543548-41659517.527-9.590.25440414-101006502523.51.52.2554017.5143.512.256302532248452017.5116.5272.254.6451120.5-9.590.2554017.5143.512.256352581728943548-41654017.517.50053017.5314.5210.2553517.589.590.254.5408.514-5.530.254.5458.520.5-1214454317.517.5003.543117.5-16.5272.2 5450423.5-19.5380.25445420.5-16.5272.2553017.5314.5210.2553517.589.590.2554517.520.5-39TOTALS-3.53494.25Now, to find the Spearmans coefficient of rank you have to use the formula:p = 1 6?dà ¯Ã‚ ¿Ã‚ ½n(nà ¯Ã‚ ¿Ã‚ ½ 1)In this formula, p is the Spearmans coefficient rank of correlation; d is the difference between one item of data and n is the number of items of data.So, for the year nine pieces of data, ?dà ¯Ã‚ ¿Ã‚ ½ = 12.25 and n = 27, so:1 6 x 3494.25 = 1 20965.5 = 1 1.066620879= -0.06662087927(729 1) 19656Therefore, this means that the spread of the year nine data will have almost no correlation at all when plotted on a scatter graph because if the Spearmans coefficient of rank is close to 1 then it means that the data will be strongly positively correlated. If it is close to 0 then it means there will be extremely little or no correlation, and if it is close to -1 it means that there will be very strong negative correlation. This scatter graph shows this:The thick, black lines on this graph show the actual length of the line and the actual size of the angle. If someone had guessed both pieces of data correctly, there estimate would be marked upon the intercept of those two lines. This graph shows that there is very little correlation, which means my Spearmans coefficient rank of correlation was correct. There is an anomalous result on this graph which is when someone has estimated the angle as around 90à ¯Ã‚ ¿Ã‚ ½. This was extremely far away from the actual size.The circled result is an anomalous result. It shows that someone guessed a very different angle estimate to the rest of the pupils. This person didnt really have a lot of error when estimating the line, they estimated only 0.4cm above the actual length but then estimated 57à ¯Ã‚ ¿Ã‚ ½ above the actual angle size, which again indicates that there is no correlation, showing that this person was not too bad at estimating the line length, but were quite bad at estimating angles. This shows th at this person found lines easier to estimate.This is the table for the year 11 data.Year 11 Estimate of lineEstimate of angleLine RankingAngle RankingDifference (d)dà ¯Ã‚ ¿Ã‚ ½54022.516.563653522.51012.5156.2554022.516.5636445827.8-19.8392.0454522.527.8-5.328.0954522.527.8-5.328.0954522.527.8-5.328.093401.516.5-1522554622.532-9.590.2553022.54.51832443084.53.512.253.540316.5-13.5182.254.5291411316954522.527.8-5.328.0943084.53.512.254.530144.59.590.253301.54.5-39435810-2484032.516.51625643084.53.512.2584032.516.516256445827.8-19.8392.0464530.527.82.77.2954522.527.8-5.328.0953522.51012.5156.2555022.533-10.5110.25445827.8-19.8392.04435810-24445827.8-19.8392.0453522.51012.5156.254.5401416.5-2.56.2554522.527.8-5.328.0964030.516.514196TOTALS-19.8392.04Now, to find out the correlation I will substitute the values for the year 11 data into the formula again:1 6 x 4243.74 = 1 25462.44 = 1 0.709181 = 0.29081933(1089-1) 35904This means that when these values are plotted on a scatter graph they will also have extremely little correlation. This shows that people who may have estimated the smallest value for the size of the angle, didnt necessarily estimate the smallest value for the length of the line.This graph shows the year 11 data.Again, the thick, black lines show the actual length of the line and size of the angle. From this graph you can see that the Spearmans coefficient rank was correct, and that the graph has very little correlation. Also, on this diagram, most of the points are relatively near to the line, but there is an anomalous result, which is at the estimate id 8 cm for the line. This persons estimate for the angle was not too inaccurate though. Again the pink circled point is an anomalous result.The lengths of lines are easier to guess than angles.This was my first hypothesis. I feel, through doing many calculations and graphs, that I was able to prove that this hypothesis was correct.Firstly, I drew some grouped frequency tables. From these I was abl e to see that for the year 9s, the mean for the estimate of the lines from the grouped frequency table was 5.21cm, this is 0.61cm longer than the actual length of the line, yet the mean for the year 9s angle estimates from grouped frequency was 45.70à ¯Ã‚ ¿Ã‚ ½, which is 12.7à ¯Ã‚ ¿Ã‚ ½ bigger than the actual size of the angle. There is a lot of difference between these means. Most obviously, the year 9s found it easier to estimate the line, as the difference between the actual length of the line and the mean is much lower. The difference between the size of the angle and the mean is probably higher as there is a result that is 90à ¯Ã‚ ¿Ã‚ ½, which makes the mean much higher. The year 11s mean for the line was 5.12, which is 0.52cm bigger than the actual length of the line. The mean for the year 11s estimates of the angle from grouped data was 41.06à ¯Ã‚ ¿Ã‚ ½, which is 8.06à ¯Ã‚ ¿Ã‚ ½ larger than the actual size of the angle. This shows again that the year 11s found the lines length easier to estimate than the size of the angle.I also did standard deviation which indicates how the data is spread from the mean. For the year 9 angle it was 14.7, which is quite a high spread, showing that the year 9 estimates for the angle size were quite spread apart. For the year 9 line estimates, was 0.99, which means that the results are very compact compared to the mean, which shows that many pupils estimated very close to the mean, showing that the year 9s were better at estimating the lines length.The standard deviation I did for the year 11s showed that the year 11 lines deviation from the mean was 1.07 which is very low, meaning that the results were quite compact. Then, for the angles the spread was 6.5 which is higher than the deviation for the line, showing that the year 11s were better at estimating the line.Next, I drew some histograms, and from these you can see that even though the most densely populated groups were not the groups of 4.5 5 for the line, or 30 35 for the angle, you can see that in the year 11 estimates for line 2, the most densely populated group was 4 4.5cm group. To find this I multiplied the sizes of the groups, in this case 0.5 by the frequency density, 18, which gives 9, which was the highest frequency. And for the angle the most densely populated group was 45 51à ¯Ã‚ ¿Ã‚ ½. Neither of the most populated groups contained the correct estimate for the line or the angle, so it is not possible to tell accurately which one was easier, but you can see that the year 11 estimate for the lines most densely populated group was the group next to the group containing the correct length, whereas this was not the case with the angle.For the year 9 histograms, you can see that the most densely populated group for the angle was joint between the groups of 35 40 à ¯Ã‚ ¿Ã‚ ½ and 40 45à ¯Ã‚ ¿Ã‚ ½ as they both had the highest frequency worked out form the histograms. For the line it was 4.5 5cm group, which is the correct gr oup that contains the actual length of the line. From this you can see that the year 9s were better at estimating the length of the line, which again proves my hypothesis.Next, I drew some cumulative frequency tables and curves. From the year 9 cumulative frequency curve I was able to find the median, which was 5.2cm, this is quite far away from the actual length of the line of 4.6cm. You can also see on the box plot that the actual length of the line is contained at the very edge of the box, nearer the edge of the lower quartile, which means that most year 9s guessed above the length of the line. For the year 9 angle estimates in the cumulative frequency curve you can see that the median is 42 which is very high compared to the actual size of the angle of 33à ¯Ã‚ ¿Ã‚ ½. This means that the year 9s box plot for the angle didnt contain the actual size of the angle in it. This is because someone estimated 90à ¯Ã‚ ¿Ã‚ ½, which is very high. This means that the year 9s, again found th e line easier to estimate.The cumulative frequency curves for the year 11s estimates of line 2 show that the median from the curve was 5cm. This is 0.4cm bigger than the actual length of the line. The actual length of the line is contained in the box, but at the very edge of the lower quartile. This again shows that most people estimated over the length of the line. For the year 11 angle cumulative frequency curve, the median was 43à ¯Ã‚ ¿Ã‚ ½, which is 10à ¯Ã‚ ¿Ã‚ ½ over the actual size of the angle. The actual size of the angle again is not in the box, showing that many people again guessed above it, which leads to show that the year 11s found the line easier to estimate.Next I drew some percentage error tables. For the year 9 line the mean error was 5.31%, which is relatively low, showing that there was not much error at all. The percentage of error for the angle though was 26.71% which is much higher than the percentage error for the line, meaning that the year 9s had less err or when estimating the length of the line.For the year 11 tables, I was able to find the mean percentage error of 4.08% for the estimating of the line, which is rather low showing that the year 11s did not make much error whilst estimating the line. However, their percentage error mean for the angle was 18.92 which is much higher than the line error percentage, showing that the year 11s were better at estimating the length of the line as they had less error.From the scatter graphs drawn by the error you can see that there is no correlation between the errors.From the stem and leaf diagrams, you can see that the median for the year 9s line estimate is 5.0cm, which is 0.4cm above the actual length of the line, and the year 11 median for the angle was 40. This is 7à ¯Ã‚ ¿Ã‚ ½ above the actual size of the angle, meaning that the year 11s were better at estimating the line. The numbers for the median of the year 9s from the box plots were the same as the year 11s showing that the year 9 s were better at estimating the length of the line also.I then did standard deviation to find out what correlation the scatter graphs would have when I drew them. From doing standard deviation I was able to see that for the year 9 scatter graph there would be extremely little correlation at all, as the answer I got after doing the equation for standard deviation, which is shown on page 17, was -0.066620879, this means that5 there would be VERY slight negative correlation, but hardly any at all, meaning that if a year 9 isnt very good at estimating the length of a line, then they are not necessarily bad at estimating the angle size.For the year 11s the answer I got from working out the standard deviation was 0.290819, which means that there would extremely slight positive correlation, but hardly any at all really. This means that if they were really good at estimating the line, they were not necessarily good at estimating the angle size. The scatter graphs show this data.Overall, I f eel I have managed to prove the hypothesis of the length of lines is easier to guess than angles as correct through many calculations and graphs.Year 11s will be more accurate at estimating.This was my second hypothesis, and through the calculations I have done I feel I have managed to come to a conclusion of proving or disproving this hypothesis.Firstly, I made some grouped frequency tables, which I then found the mean from. The mean for the year 11s estimates of the line from the grouped frequency was 5.12cm which is 0.52 above the actual length of the angle. The mean for the year 9 estimates was 5.21 which was 0.61cm above the actual length of the line. This shows that as the mean was closer to the actual length of the line, the year 11s were better at estimating the length of the line in this case. For the angle estimates from grouped frequency tables, I was able to see that the year 11 mean from the grouped frequency was 41.06à ¯Ã‚ ¿Ã‚ ½ which is 8.06à ¯Ã‚ ¿Ã‚ ½ above the act ual angle size, whereas the year 9 mean from the grouped frequency tables for the angle was 45.70à ¯Ã‚ ¿Ã‚ ½ which was 12.7à ¯Ã‚ ¿Ã‚ ½ higher than the actual size of the angle. This means that the year 11s were better at estimating the size of the angle than the year 9s were.Next, I found the standard deviation for both year groups. I used the equation on page 3 to do this. For the year 9 line estimates I found out that the spread of the data from the mean was 0.9939 which is quite a low spread, meaning the estimates were quite close together. For the year 11 estimates for the length of line 2, the deviation of the estimates was 1.07, which means that the year 9 estimates for the length of line 2 were less spread out than the year 11s.For the year 9 estimates of the angle, the spread from the mean was 14.7 which was quite high, showing that the results were quite spread out. For the year 11 estimates of the angle, the standard deviation was 6.5 which means there was quite a lot of spread, but less than the year 9s meaning that the year 11 results were less spread out in this case.After this, I drew some histograms. From the year 9 histograms you can see, for the line, that the most densely populated group was 5 6 cm which, is not the group where the actual length of the line was contained. For the year 11s, the most densely populated group was also the 5 6 cm group which was again not the correct group where the actual length was, showing that both the majority of the year 9s and year 11s guessed that the line was in the region of 5 to 6 cm.For the year 9 histogram for the angle, the most densely populated groups was joint between 35 40 and 40 45, which did not contain the actual size of the angle. For the year 11s the most densely populated group on the angle histogram was 35 40, which again did not contain the correct size of the angle, meaning that the majority of both year groups estimated in the incorrect group.I then drew some cumulative frequency curves. These showed me the frequency of the data in the form of a curve. For the year 9s, from the curve for the line estimates, I could see that the inter-quartile range of the data was 1.6cm and the median was 5.2cm. For the year 9s curve for the line the inter-quartile range was 1.2cm and the median was 5.1cm. From these results you can see that the year 11s had both a lower median and a lower inter-quartile range. This could suggest that they were better at estimating.For the angle cumulative frequency curve for the year 9s the inter-quartile range was 11.5à ¯Ã‚ ¿Ã‚ ½ and the median was 42à ¯Ã‚ ¿Ã‚ ½. For the year 11s the inter-quartile range was 9à ¯Ã‚ ¿Ã‚ ½ and the median was 43à ¯Ã‚ ¿Ã‚ ½, this means that the year 11s had a lower inter-quartile range value, but the year 9s had a lower median value.After drawing cumulative frequency curves, I was able to draw some box plots. From these I was able to see the inter-quartile range and median easily.For the year 9 box plot f or the line I was able to see that between the highest result and the upper-quartile there is very little difference. The upper quartile was 6.1cm and the highest result was 6.2cm. This shows that the many people estimated higher up on the scale as between the lowest estimate of 3cm and the lower quartile of 4.5cm there is quite a large gap.For the year 11 box plot of the line, I was able to see that the data is quite evenly spread as the box plot is relatively in the middle of the lowest value of 3cm and the highest value of 8cm. The actual length of the line is contained with in the inter-quartile range box, but, towards the very end of the lower quartile side, meaning that most people estimated above it.For the year 9 box plot of the angle estimates I was able to see that the actual size of the angle is not contained within the inter-quartile range. This is because many people guessed above the correct size of 33à ¯Ã‚ ¿Ã‚ ½, meaning that it was not contained within the inter-qua rtile range.The year 11 box plot for the angle showed that many of them also estimated above the size of the angle of 33à ¯Ã‚ ¿Ã‚ ½, as it is again not contained in the inter-quartile range. After analysing these box plots, I do not feel that they have helped me to prove or disprove my hypothesis.Next, I drew some percentage error tables. From these I was able to see what error each individual made in their estimates. I then found how much error they made as a percentage. Overall, for the year 9 estimates of the line, the mean percentage error was 5.31%, which is quite low, means that they did not make too much of an error overall. For the year 11 estimates of line two, the mean percentage error was 4.08%. This is a lower percentage of error than the year 9s meaning that overall, the year 11s made less error in estimating the length of the line.For the year 9 estimates of the angle the mean percentage error was 26.71% which is quite high. This is because there was an anomalous resu lt of 95à ¯Ã‚ ¿Ã‚ ½, which means that the overall mean percentage error value will be higher. For the year 11 estimates of the angle, their mean percentage error was 18.92%, this is still quite high, but lower than the year 9s mean percentage error for the angle meaning that the year 11s had less error in estimating the angle overall again.Next, I drew some scatter graphs which showed how much error someone had made, for example if some had estimated 2à ¯Ã‚ ¿Ã‚ ½ above the actual angle size, and 0.3cm below the estimate of the line, their coordinates would be (-0.3,2). From the scatter graphs I was able to see that there was not really any correlation in either the year 11 or year 9 scatter graphs. Again, after analysing these I do not feel that they help me to prove or disprove my hypothesis.After this, I drew some stem and leaf diagrams. I was able to find the median and mode from these. They also showed me the data in a table, showing each and every result.For the year 9 line e stimates I was able to work out that the median was 5.0cm which was also the median for the year 11s. This means that when all the data is put in consecutive order, that both the year 9s and the year 11s had the same middle estimate. For the angles I was able to see that the median for the year 9s was 40à ¯Ã‚ ¿Ã‚ ½, and the median for the year 11s was also 40à ¯Ã‚ ¿Ã‚ ½. This means that again they had the same middle estimate. I do not feel that these have helped me to prove my hypothesis.Next, I did the spearmans coefficient rank. This shows what correlation the data will have when plotted on a scatter graph. For the year 11s I was able to see that after working out that the correlation would be hardly visible, but would be extremely slightly positive, as the answer I got after working out the spearmans coefficient rank was 0.290819. I then plotted my points on a scatter graph and found out that this was correct. I then worked out the spearmans coefficient of rank for the year 9s . This gave me an answer of -0.066620879, meaning that there would be a very slight negative correlation. I plotted my points on a graph and was able to see that again I was correct. I was shown that for both year groups the estimate for the line didnt necessarily correlate with the estimate for the angle. For example, my results did not show that if someone estimated high for the angle size, they did not necessarily estimate high for the line length.Additionally, from the scatter graphs I was able to see that there were some anomalous results. For example there was a year 9 who estimated the angle at being 95à ¯Ã‚ ¿Ã‚ ½, whereas they estimated the line at being 5cm which showed that they did not estimate too extremely for the length of the line. I do not feel these scatter graphs assisted me in proving my hypothesis.Overall, I feel I have been able to prove this hypothesis as correct through the calculations which referred to it.I think all of my calculations and diagrams were cor rect as they all led to the same conclusion, and through checking my answers I found that they were correct. I think there were not really any major anomalous results, apart from the ones previously mentioned and shown in my calculations.One problem I did have was finding the standard deviation, but I realised this was because I was trying to find it from grouped data, and therefore needed a different formula, and after getting the formula for grouped data I found it a lot easier to do.I managed to prove my hypotheses were correct in most instances and I managed to show many different types of calculations in proving my hypotheses.

Vegetarianism vs Omnivore Essay

Vegetarianism vs Omnivore Essay Vegetarianism vs Omnivore Essay Why Vegetarianism is better for the Health than Omnivorism? Abstract Vegetarianism is the practice whereby a person does not consume animal foods, restricting him or herself to eating vegetable foods only. Such a person is referred to as a vegetarian. Vegetarian diets vary widely, ranging from exclusive non-consumption of animal products at all to consumption of animal products such eggs, dairy products, poultry and fish. Vegetarian diets involve plant based foods like legumes, grains, vegetables, nuts fruits and seeds. Vegetarians vary depending on whether or what kinds of animals are eaten. Strict vegetarians are called vegan and their diets exclude all forms of animal products like fowl, meat, eggs and dairy. On the other hand, a lacto-ovo- vegetarian consumes dairy products such as eggs besides plants products. Whereas Lacto vegetarians consume dairy products in addition to plant products, an ovo vegetarian consumes eggs only, as animal products (Nordqvist). Fruitarian vegetarians consume raw and dried fruit and vegetables only. Cooking is believed to harm the fruits’ nutritional ingredients. Such vegetarians eat all types of fruits, both sweet and non sweet. There are also the semi-vegetarians who consume plant based foods combined with slight amounts of poultry and fish. On the other hand, the macrobiotic vegetarians’ diet consists of cereals, whole grains and cooked vegetables. Omnivorism vegetarians on their part belief in the exclusive consumption of animal and plant based foods. They consume both in moderation. There is a growing concern that the different types of vegetarians like lacto vegetarians are ethical omnivores. An omnivorous diet contains plants and animal foods, but omnivorous persons put more emphasis on meat than on other foods (Nordqvist). As vegetarianism has gained popularity in the 21st century, there are reasons as to this newly emergent dietetic trend. Some of these reasons include the following: 1. How did vegetarianism come about? 2. What are the health benefits of vegetarianism, as opposed to omnivorism? 3. What are the social values for being a vegetarian, as opposed to omnivorism? 4. What is the ethics behind vegetarianism? 5. What is the spirituality behind vegetarianism? 6. How do I become a vegetarian? 7. What might happen if plants cease to exist anymore? History of vegetarianism dates back many centuries from the Asian subcontinent. However, vegetarianism is not historically constrained to the East. As early as the 16 century, Greek philosophers like Pythagoras and others abstained from meat consumption based primarily on their belief in the transmigration of souls. The 18th century vegetarian advocates included Voltaire of France and Franklin Benjamin of America. Although there have always been a followers of vegetarianism, the rapid growth of the movement in the United States occurred principally since the 1970s. Currently, there are millions of vegetarianism, as well as magazines, journals and books published to help them in pursuit of vegetarianism. One key reason for vegetarianism is regards for health. There have been nauseous concerns about sanitation of slaughter methods and production houses. This raises a health concern on possible contamination of the meat itself during the transportation, storage and entire preparation process. In America, there are about one million farmers and ranchers who carry out livestock farming. In comparison there are about six thousand plants that are federally inspected to process. Additionally, only four major packers process about 70 percent of beef and other four packers of 60 percent of pork in the United States. Further, there are concerns that persons concerned with slaughtering do not observe humane slaughter act of 1960. The act necessitates that, before slaughtering, animals must be rendered wholly unconscious, with the least of excitement and discomfort by use of electrical, mechanical and chemical (carbon dioxide gas) methods. Some persons contend that these methods are not as accur ately as they should be. Lack of observation of these principles results in contamination of the meat products considering that meat products are delicate and highly perishable products. In order to live healthy, persons should resort to vegetarianism (Keegan 136). Over consumption of meat products is the leading cause of weight gain, whereas overconsumption of vegetables does not result in weight gain. Meat intake results in calories add up bit by bit compared to fiber rich veggies like green beans. Persons who consume large quantities of meat are obese, while persons consuming veggies are healthier. Since meat consumption has resulted in obesity among many people, fast foods around the world are embarking on selling vegetable meals, which is increasing ground, implying that vegetarianism is better for health than omnivorism (CindyL). Omnivorism results in low life span as compared to vegetarianism conscious living. A survey sponsored by the United States national cancer institute revealed that persons who eat four bits or more daily are at 30percent risk of dying than vegetarians. The study further showed that processed meat also increases death risks. Meat does not contain fiber, which results in the digestive system complications. Further, lack of fiber in the body increases the risk of heart and cancer problems. According to National Cancer Institute, Rectal and colon cancer have resulted in more 51,690 deaths in United States in 2012. While lack of fiber in meats causes diseases, presence of fiber in vegetables protect the body against disease. As such, vegetarianism is better for health than omnivorism. Meats contain saturated fats which are harmful to the body. The meat fats surround the arteries and vessels of the heart, interfering with the proper functioning of the heart. The interference affects free movement of blood in the body, posing the risk of heart attack. Meats contain a high concentration of salts and nitrates. Meats like hot dogs and bacon contain high salts and other preservatives which are not healthy for the body. Further, such foods contain animal fur, which means that they are not prepared in healthy ways. Other preservatives in processed meats are harmful to the body, since they contain chemicals that are carcinogenic. For instance, high concentration of salts and nitrates can cause high blood pressure and trigger other cardiovascular and chronic ailments. In order to live healthy, meat consumption should be balanced with vegetables. Proteins obtainable from animal products can also be obtained in plants, whereas nutrients available in plants cannot be substitut ed for anything else. This is a clear indication that vegetarianism is better for health than omnivorism (CindyL). Vegetable foods are very healthy to the entire body, and especially the heart. Does the omnivorism affect the heart? This is a contentious topic that has been the subject matter of debate for several decades, but doctors and scientists came to agree that diets could cause or prevent heart disease. This view is also held by the seventh day Adventist religious group, who are massive flowers of vegetarian practices worldwide. The religious group holds the following precepts on the health benefits of a vegetarian lifestyle. They assert that Adventist omnivore men who are fifty five years and above are twice more likely to die of a heart ailment than vegetarian Adventists. Men aged 40 to 54 who eat meat more than six times a week are 4 times, likely to suffer from a deadly heart attack than vegetarian men. Women over 55 years who eat meat are 1.5 times at risk of a lethal heart attack than do female vegetarians. As for Christians who are strictly vegetarians, their assertion comes from th e book of psalms 104:14. The verse states â€Å"He causes grass to grow for the cattle, and vegetables for the service of man†. Another verse in Genesis 1:29 that supports this view reads as â€Å"I give you every seed bearing plant on the face of the whole earth and every tree that has fruit with seed in it. They will be yours for food† (Contreras 85). Even the conventional American dietetic association recognizes that a budding body of scientific knowledge holds an affirmative association, between the consumption of a plant based diet and prevention of certain ailments. The meat industry denies the health gains of vegetarian diets. However, research on cancer high blood pressure, obesity and heart diseases indicates otherwise. A recent research on the role of diet in prevention of certain conditions evidences that heart disease is related to excessive intake of fats. The foods that we take are the sole important factor in establishing whether or not we develop heart diseases. Fruits and vegetables are the backbone of a healthy heart. Several studies show that persons who consume a lot of fruits have reduced risk of heart disease. One such example is Finland, where people consume potentially more fruit and vegetables. This change is related directly with decrease in deaths from the heart ailment (Keegan 156). In San Francisco, Dr. Ornish Dean of the California University found that individuals who consumed vegetarian foods that were low in fat had half the number of heart complications as people who ate meat on a typical American diet. Additionally, they shed off thirteen pounds and kept it off for a period of four years. Fruits and vegetables do not have cholesterol and are naturally low in fat concentration, calories and sodium. They are rich in fiber, folic acid, protein and vitamin C. In another study carried out in Italy, high levels of vegetable consumption led to 21 percent reduction in heart attacks and reduced chest pains by 11 percent. A high consumption of fruits also reduces blood pressure. Vegetables and fruits are rich is phytochemicals which help in prevention of heart ailment. Phytochemicals are biologically active minerals and vitamins that fall into plant sterols, flavanoids and plant sulfur compounds (Contreras 94). Plant sterols help to obstruct cholesterol assimilation from the diet or increase excretion of cholesterol in the body. Flavanoids extend the activity of vitamin C by acting as free fundamental scavengers, prevent LDL cholesterol oxidation, inhibit aggregation of platelets and possess anti-inflammatory action. Intake of flavanoids has been shown to decrease the casualty of heart ailment and the incidence of heart attack. Mature males with high consumption of flavanoids were 60 percent less the risk of heart death than low consumers according to the Zutphen elderly study. The study also revealed that most- menopausal women consumers of fruits and vegetables are responsible for 38 percent reduction in heart complications. Broccoli was found essentially noteworthy on reducing heart disease (Contreras 96). Plant sulfur compounds found in the allium family of vegetables has been shown to have preventive and protective properties against cardiovascular disease. Garlic leeks and onions are specifically shown to have these properties. Color pigments; anthocyamins in vegetables and fruits helps to shield consumers from heart disease. They protect heart disease by slowing cholesterol generation. Carotenoids pigments in yellow orange green and red vegetables are powerful anti oxidants that satiate free radicals, strengthen the immune system and protect the body against oxidative damage. Vegetables help to maintain the level of sugar in the blood which reduces the risk of adult onset diabetes (Contreras 91). By logically comparing omnivores and carnivores, it is clear that man was not created to take meat. Meat eaters have claws to help them in their meat eating habits while humans do not have claws. Meat eating animals have sharp front teeth for tearing flesh with no flat molar for grinding. Humans have no pointed front teeth, and have flat molars for grinding, similar to that of herbivores. Meat eaters have a larger intestine capacity that is thrice their body length in order to aid rapidly decaying flesh to bypass through rapidly. Human on the other hand have an intestinal track that is ten times their body size. Another physiological difference that shows that vegetarianism is better for a man’s health is that, meat eaters have a powerful hydrochloric acid in the stomach to aid meat digestion while humans have a weaker stomach acid. This shows that humans overwork their digestive system by consuming meat. Humans have a properly developed salivary gland, which is vital for pre- digesting grains and vegetables. Meat eaters do not have salivary glands in the mouth. These distinctions clearly show why vegetarianism is the healthy way for human beings (The Vegetarian Resource Group). A certain group of researchers investigated for the symbolic meaning of eating by contrasting the beliefs and values of omnivores and vegetarians. They contrasted a wide assortment of omnivores and vegetarians on the right wing totalitarianism, social domination orientation, eating values and human values. The participants tending towards omnivorism differed from those inclining towards veganism in two chief ways. The omnivores were more likely to sanction hierarchical ascendancy, and they placed less importance on sentimental states. Consequently, the approval or rejection of meat covaried with the approval or denunciation of the values related with meat; that discovery suggests that persons eat meat and embrace its representation in manners unswerving with their self definitions (Keegan 169). The spirituality behind vegetarianism is traceable in old religions like the Jewish and Christianity. Jewish dietary laws follow the context of mosaic laws, and they prescribe a healthy living whether one is Buddhist, Chinese or Muslim. Religious reasons are the primary reasons as to why some people adopt vegetarianism. Some religions assert the belief of re-embodiment, a multilfe relationship between animals and humans, thus, eating flesh is forbidden. As such eating flesh is considered as a taboo in religion. Vegetarianism becomes the best solution for such persons as they observe their diets (Keegan 159). Environment ethicists and advocates assert that a diet devoid of meat relates to environmental concern. Persons concerned on the planet ability to produce sufficient quantities of food to sustain a burgeoning population think that lessening meat consumption will aid. For illustration of this point, an acre of land will yield a greater quantity of edible plant than animal meat. One acre of land is capable of producing as much as 385 pounds of alfalfa seed from one pound of seed. If in turn the 384 pounds are sprouted for food, the yield will be around 3180 pounds of consumable sprouts. As such, consumption of plant can make a significant environmental difference. As part of ethics associated with omnivorism, taking the life of the poor animal is bad as it involves killing. In order to live a conscious life, a person can use the gradual or rapid change. Gradual change into vegetarianism is preferable because the slow change tends to become more of a lifestyle and long term move. Additionally, rapid change may not go well with some people; their digestive systems may not accommodate such so fast. A gradual change entails increasing intake of legumes, fruits, vegetables and whole grains while lessening meat intake at the same time. For persons who want to become vegetarians, they can follow the following guidelines. First, choose one whole grain products like cereals and rice. Ensure that the diet is diverse. Consider low or non fat dairy foodstuffs. Reduce intake of eggs consumed per week to three or four in one week. It is necessary to plan well on what foodstuffs to purchase when shopping. Read through the food labels before purchasing. Finally, it is essential to identify a specialist store where one will be obtaining supplies. If plants cease to exis t, there would be no life for both the omnivores and the vegetarians. Safeguard of plants should therefore be enhanced in order to drive healthy and conscious living. writing service provides students with quality custom written papers. All academic essays are written by professional  essay writers from scratch!