The woman warrior Essay Example | Topics and Well Written Essays - 500 words

The woman warrior - Essay Example However, the actualization of her tales represents Maxine living the life of the persona. She uses her position as a warrior to interject her personal and familial experiences to the discriminations of the communist rule in China. In the second section of the chapter, she uses her position to lift the cultural gap portrayed by Chinese emigrants in America under the realm of Chinese Revolution. The significance of the changing roles of women in White Tigers as depicted by Fa Mu Lan represents the conventionalism and flexibility for women to pursue a man’s life. Whatever a man can do, a woman can do, is the typical representation of the diversity of female roles in the story. She manages to maintain her family (her husband and child) and simultaneously takes on her community against authoritarianism. The role of Fa Mu Lan in the context differs with the real actualization and impersonation of her as a man warrior. She wears the traditional male armor, and having an entire battalion of traditional warriors to lead the fight against communists. She undergoes intense training in a secluded place and starves herself to attain warrior-like aspects need to sustain her role to protect her community, strategies that a typical woman cannot withhold. According to Kingston, Fa Mu Lan, having an honorable death due to social status does not determine the ideology of death. Barons and the communists’ status quo in the traditional Chinese community represented dictatorial leadership and demand honorary respect from the subjects. However, Fa refutes this stereotype by defeating the communists’ and beheading the lead baron. With her sword, she slashes the head off and leaves the baron to die. Her sword represents the continuation of the fight against societal alienation from changes in revolution (Helena 23). The use of the forest as a spatial convenience of warrior training is used as a traditional setting that induces

How democracy is related to economic development Essay

How democracy is related to economic development - Essay Example Marx gave as example the histories of both England and France proved that economic development brought about democracy. Marx and his loyal fans, both within the bourgeoisie class, owned property and engaged in business as capitalists and entrepreneurs. Consequently, the bourgeoisie class led to the toppling of the prior authoritarian government, a replacement transition mode (Sfsu, 2014). The toppling resulted to the creation of democratic governments. The prior government was toppled because its policies ran smack against the very grain of laissez faire or free enterprise business concepts, based on transition theories (Sfsu, 2014). Likewise, Max Weber insisted that the fall of the profitability or viability of the bourgeoisie leaders to retain the economic development led to the destruction of the German democratic government. In the same light, Moore espoused the peasants contributed to the establishment of democratic government. The philosophers had espoused economic development leads to a better political communication environment. Further, Laothamatas adhered to Mr. S M. Lipset’s emphasis that an economically developed society will trigger the people to push for the implementation of a democratic state. The above article affirms the concept indicating economic development leads to viable democracy. M. K. Marx affirmed this concept. The bourgeoisie helped establish the economically viable democracy movements. Economic development priorities often led to free political elections within a democratic government. The reading affirms economic development contributes to the establishment and retention of democracy (Lipset, 1959, p 75). The economic wealth or status of the nation affects the nation’s democratic aspirations. Compared to a nation that is burdened with a low or bankrupt economy, a well to do nation (rich) has a better

Responsibilities of Health and Social Care Workplace

Responsibilities of Health and Social Care Workplace 1.2. Assess the responsibilities in a specific health and social care workplace for the management of health and safety in relation to organisational structure. The business dictionary defines organisational structure as: The typically hierarchical arrangement of lines of authority, communications, rights and duties of an organization. Organizational structure determines how the roles, power and responsibilities are assigned, controlled, and coordinated, and how information flows between the different levels of management. Chief executive/ directors/ managers /HR and supporting staffs Health and Safety Commission: The stakeholders and Directors will ensure that a Health and Safety Committee is established and supported. This committee will see to the development, implementation, arrangements of health and safety policies and procedures. They also ensure that the policies and procedures are undertaking and executed. This will be reasonable practicable without risk to the health and safety of those who engaged in, or affected by our operations. They ensure that the provision of this policy is kept under review with regard to changes in legislation, best practice. They propose new Health and Safety Regulations to the Secretary of State. Examples, Noise at work regulations 1989 the commissioner will ensure that a Health and Safety Committee is established and supported. They will come out with development and implementations, They will ensure that the policies and procedures are upheld and executed as reasonably practicable without risk to the health and safety of those carrying out operations. We maintain high standard in the management of health and safety, with the prevention of accidents, the provision of a safe working environment and the safeguarding of employees health wellbeing, it is everyones responsibility to ensure that maintain and achieve our set objectives. As an employer of labour, my organisation recognises the health, safety and the welfare of staff at work , guests , clients any contractors, so far as is realistically practicable. We are committed to the continuous improvement of our Health and Safety system, policies, procedures and methods of working that are designed to ensure the safety, health and welfare of all employees, visitors and anyone else who is likely to be affected by our work activities. COSHH Regulation 2002 stated that employers are responsible for providing, replacing and paying for Personal Protective Equipment (PPE) which should be used by all staff as part of health and safety at work. Health and Safety Committee All representatives sitting on the Health and Safety Committee are to actively promote all aspects of health and safety within the organisation and the areas of responsibility. In particular they are to encourage discussion and understanding of these policies and procedures. Our line Managers Our line Managers are expected to device a method of implementing the over all objectives by demonstrating a positive, proactive approach to health and safety, by certifying that this policy, together with its related procedures are clearly communicated to employees and then implemented, monitored and reviewed. Managers The managers will establish open communication with everyone they are responsible for and will promote any changes in the Health and Safety Policy. It is the responsibility of the Health and Safety Manager to ensure suitable arrangements are in place and implemented. Employees and others Employees and others involved in the organisations activities, have a responsibility to take care of their own health and safety at work and that of any other people who may be affected by their acts. All training provided must be attended by the staff. They are required to conform with this policy and the related corporate procedures that are provided to them. They are to carry out their work without endangering themselves or others. Proactively contribute in the achievement of the organisation objectives of achieving a positive health and safety culture and to co-operate with line managers and colleagues in creating and maintaining a safe and healthy working environment. The staff must bring it to the consideration of line managers attention if there are any health and safety worries regarding unsafe practices, equipment or conditions we are encouraged to use the consultation channels provided, when necessary. We are to assist management in identifying any issues of health and safe ty including getting trained. All significant health and safety information will be circulated appropriately, making use of notice boards, poster, and newsletters or by direct mail. Where activities could affect the health and safety of members of the public, suitable steps will be taken to ensure they are up-to-date of the risks and how they will be measured; wherever possible, it is the wish of the organisation to establish effective consultation with the workforce to safeguard planned systems of work are effective in reducing employee exposure to risk. In conclusion, we all are working together as professionals fully committed to the health and safety principles which enable the organisation to promote high standard of health and safety policy in the organisation. Everyone in the organisation is responsible for reporting and recording all incidents/accidents that happen within the workplace. It is the responsibilities of the client if they are to communicate to report to staff or management and it is the responsibility of the staff to report the incident /accidents to his/her line manager. It is responsibility of the manager to record the incidents/accident s and to report all serious incidents /accidents to the authorities according RIDDOR Regulations 2013 1.3 Analyse health and safety priorities appropriate for a specific health and social care workplace. The Creation of Health and Safety at Work Regulations1999 require every employer to provide employees with information on the possible risks to their health and safety; preventive and protective methods for those risks; backup procedures to identify individuals who have a role within the organisations health and safety controlling system. This contains giving employees information on any process or task that might involve specific risk. This information must be broad and it must make sense to individuals concerned. Codes of Practice and other guidance notes should be made accessible, as well as the organisations own clarification in the form of policies and procedures. This means that data should be constantly reviewed and revised according to modern working practices. The duties of my employer, what they must do regarding my health and safety. Most duties are subject to so far as is reasonably practicable i.e. the protection must be worth the cost. To protect the health, safety and welfare of staff, to provide and maintain safe equipment and safe systems of work, safe use, handling, storage and transport of articles and substances. Safe workplace with a safe entrance and exit.   Provide information, instruction, training and supervision Provide a written safety policy (if there are 5 or more staff) Carry out risk assessments (in writing if 5 or more staff) Provide a health and safety law poster entitled Health and Safety law: What you should know displayed in a prominent position and containing details of the enforcing authority. Employee includes voluntary workers and persons on work experience. Employees The employees duties is to take care of themselves and others To follow safety advice and instructions Not interfere with any safety device To report accidents To report hazards and risks. Staff can ask about health and safety in the work place directly from their managers or the Yours supervisor will usually be your first contact if you have a health and safety issue at work. Your safety representative may come from the union if the workplace is unionised If you have a serious complaint that cannot be settled in the workplace, the Inspector from health and safety executive (HSE) Food hygiene Enforcement: Food safety is one of the areas that health and safety pays close attention to being that majority of clients are vulnerable due to age and health challenging. This means they can be more seriously affected by food poisoning or allergy than some other group of people. As a care provider, food preparation is part of normal day to day services seeing that they are taking care of by following the guidelines under the food and hygiene law for ensuring that food is prepared, stored and handled in compliance with the food hygiene regulations. This includes keeping a record of opened food jars and emptying food from tins with the correct labelling actions are carried out to keep food safe for the maximum time located . According to the Food Standards Agency, the group charged with protecting the public in the United Kingdom with regard to food, avoiding cross-contamination between raw and ready-to-eat food is one of the most important aspects of food hygiene. Separate knives and cutting boa rds must be used for the different foods. Raw food must be stored below other food in the refrigerator to prevent drips that could contain bacteria. Food must be chilled to the proper temperature at all times and must be cooked to a temperature that kills bacteria. Disinfecting food preparation areas and cleaning used equipment also are important food hygiene practices. What the Law Says Keeping food safe is a legal requirement and failure to do so can lead to prosecution. It is essential that food and drink provided in the community and hospital health care environment is managed and handled in a manner that it does not pose any risk to children, families, visitors or staff. All staff involved in working with food must ensure good food hygiene practices at all times. Under the Food Safety Act (GB 1990), water and ice are classed as food and therefore must be handled with the same good food hygiene practices as food. Failure to do so could result in a serious outbreak of food poisoning and potential loss of life. Managers must put in place food safety management procedures based on the principles of HACCP (hazard analysis and critical control point). HACCP is a way of managing food safety. It is based on putting in place procedures to control hazards. In practice, this means that you must follow the procedures that have been put in place to manage food safety hazards in your Trust. The Health and Safety at Work Act HASAWA 1974, came the Manual Handling Operations regulations 1992 was reinforced in January 1993. Manual handling is a major source of injury and the HSE has provided a lot of guidance for employers on how to minimise risk involved in manual handling. Where it is not reasonably practicable to avoid the need for care workers to undertake any manual handling operations at work a risk assessment is done (Stretch Whitehouse, 2007) to plan and asses the weight of the service user we need to transfer while at the same time encouraging clients to move independently. In SMART care, we use the hoist but we also fully engage the client to do certain things he can do by himself. Example moving forward or backward etc. However, people can fall during hoisting for a variety of reasons such as using the wrong size of sling. This can result in discomfort if the sling is too small and a risk of the person slipping through the sling if it is too large. Selection of the wrong type of hoist or sling for the individual or for the specific task can result in inadequate support and increased risk of falling from the sling. For example, access/toileting slings give a great degree of access but very little support and their use should therefore be restricted to toileting purposes, where appropriate. For this and many reasons, the care workers in SMART care have taken short cuts and have ended up injuring their backs. On other occasion they have left a vulnerable person unattended in a hoist, or in a position where they might be at risk of falling. References www.businessdictionary.com/definition/communication www.food.gov.uk/sites/default/files/multimedia/pdfs/publication www.hse.gov.uk/pubns/books/lawpocketcard.htm http://www.hse.gov.uk/riddor www.safenetwork.org.uk/getting_started/pages Government Working together to Safeguard Children March 2010 www.businessdictionary.com/definition/communication www.food.gov.uk/sites/default/files/multimedia/pdfs/publication www.hse.gov.uk/pubns/books/lawpocketcard.htm http://www.hse.gov.uk/riddor www.ofsted.gov.uk www.safenetwork.org.uk/getting_started/pages

Communication Self-Analysis :: Communication

1. I just started working at a clothing store, and it was a great way for me to engage in new conversations and identify the elements that go into interpersonal communication. An essential to this conversation was that she was willing to take her time to get to know me and teach me how to do things, and I was able to listen. The following contexts existed in my conversation with a fellow girl coworker. The physical dimension was that we were at our workplace where it is dimly lit, there wasn’t a lot of people around, and we were surrounded by clothing. The temporal dimension was that it was nighttime when we were working so we were both tired, and I was a new employee whereas she had been working here for a long time and had more experience then I did. That also goes along with the social-psychological dimension in that we had a new relationship, she was in a higher rank then I was in our work, and the situation was friendly and easy going since we were just beginning to know each other. The cultural dimension came into play in that she is originally from Texas and I am from Utah. When she talked she had a slight southern accent and would use slang words like â€Å"ya’ll†, words that I usually don’t use. Noise had a big impact on the conversation as well. There was much physical noise around us from the loud music playing in the store, the sound of people walking the mall, the music playing in the mall, and other conversations going on around us. There wasn’t any obvious physiological noise, but there was psychological noise going on with myself as I wasn’t so focused on what she was saying and more worried about if I was doing my job right and in making a good first impression. Also, it was my first time meeting this person so I tried hard not to make any preconceived ideas about the girl. Semantic noise was the southern accent the girl had. When speaking to this girl I did realized how context and noise can influence the way a conversation goes. 2. One of my roommates just got a new boyfriend, and from the moment I met him my perception process told me that I wouldn‘t like this guy. The first thing I noticed about him was that he reeked of smoke and alcohol.

Calls for marijuana legalization

The debate on whether or not to legalize marijuana trade and consumption has been ranging on for a long time with calls for its total ban equally as loud as those that fight for its legalization. This debate a times takes a religious and an emotional perspective. This paper seeks to strengthen with factual support, the calls that support its legalization. It will give reasons behind those views as well as analyze the opposing sentiments. All this is in the believe that marijuana, if legalized, stands to be more beneficial to the society that the way it is today.My first argument towards legalization of marijuana will take an economic perspective. Billions of tax payer’s money is going to waste in the process of arresting and prosecuting people accused of consuming marijuana. This money should be channeled towards other important purposes like healthcare and providing education to our children (Barnett P.G 166-171).Every year in America, thousands of people are huddled in drug courts faced with all manner of charges ranging from consuming to trafficking marijuana, then handed undeserving sentences. Process requires millions of dollars. This is money that should be spent in giving counseling and treatment to the affected rather than trying to catch the few of the majority that smokes. It is apparent that our money is surely going to waste; the war against drugs has never been won and is not going to be won any time soon.The cost of maintaining prisoners arraigned in court over marijuana related cases each year is estimated at 1.2 billion dollars ever year. â€Å"This does not include the cost of investigating, arresting, a prosecuting and hundreds of thousands of marijuana users arrested every year† (Wayne Hall 7) this to me is a waste of scarce resources, money that otherwise should not be used had we legalized marijuana.Still in economics, the marijuana is a multibillion industry that continues to place food on the table for millions of people wor ld wide, illegally of course. I this industry can operate legally; it would be able to secure jobs to millions more as well as contributing billion of dollars through taxation. About 11 billion dollars would be gained from the tax. (Douglas Mc Ray58). The marketing of marijuana has its illegal form is left to black marketers this meaning that currently their proceeds go untaxed.Economic benefits of legalized marijuana will be two fold. First it will be from the funds saved from the efforts to suppress and eradicate its use and then secondly benefits arising form its taxation. Marijuana should be legalized, either way whether legalized or illegal consumption still takes place. There are no statistics available to indicate that its continued legal suppression over maybe the last 30 years has had any consequent impact. Prohibition does not decrease its use. Its popularity does not wane. Netherlands has done it. It’s a good case study that â€Å"marijuana legalization would not be the disaster that opponents say it would be† (Douglas Mc Kay29-32)Debate still rages on possible effects of legalizing marijuana on the health of the users. There are those who argue that it has adverse effects on health while others argue that the overall effects are insignificant. Without looking at the worst case scenarios and moderation cases, both studies indicate that there are no known risks associated with its overdose; possible side effects can only be exhibited in the behaviors of the user. (Douglas Mc Kay) these results indicate that marijuana use is not more hazardous than alcohol consumption unlimitedly.Tobacco use is equally harmful and yet it is legal. The reason why marijuana remains illegal is due to its peeved adverse effects on the users. The government in its social responsibility role has to limit its use/abuse. This to me is based on flimsy grounds. Alcohol and tobacco are also harmful, then why ban one poison and out rightly allow the usage of the oth er one. This is illegal.Marijuana decriminalization will allow people to explore the possible medicinal values it has.   Studies have shown, according to Baker D.(2003 561-567),that â€Å"cannnabinoids† (contained in marijuana) provide a novel therapeutic target, not only for controlling symptoms, but also slowing disease progression through inhibition of neurodeneration.† Disregarding its side effects, marijuana is hailed to have many therapeutic benefits like subsiding pain in cancer patients; it is basically used with pain and muscle stiffness in patients.Criminalization of marijuana literally closes these windows of research. Doctors and health experts are barred from researching with it or administering on any of their patients despite indications that it can effectively be used for medical purpose with only some mile side effects. We are making it inaccessible to millions of people out there who are maybe suffering form cancer and would want some relieve.This co untry boasts of respect and exercising fundamental freedoms for all persons. Freedom of worship is a key right that should be exercised by all and is guaranteed by the constitution. Rastafaricinism, a dominant faith in Jamaica openly advocates for marijuana usage citing its religious importance. Criminalization of marijuana clearly is in contravention of people right not only to exercise their right of worship but also their basic right to choice as long these choices do not affect others negatively.I believe it should be left to the respective individuals to make an informed choice on whether consume or not.   They should be left alone to exercise the individual liberty. People to, are left alone to choose on whether to drink or smoke, despite their known harms, people too, I think should be given leeway over marijuana.As I had noted earlier, the proponents of marijuana criminalization are equally vocal and belief they have solid reasons as to why the status quo should remain. Ma rijuana just like most drug is known to induce addictive tendencies, this is where it’s continued use results to the user being hooked to it to an extent that they are unable to function normally without it. I must say however, that addiction is not limited to marijuana alone alcohol, tobacco and also other prescribed drugs are known to cause addddiction even of a higher degree than cannabis.There is also the argument that it legalization is tantamount to sending a message especialy to underage that its consumption is indeed good. It will remove the stigma currently associated with it making it attracitve to most persons. Although this point contains some truth in it, legalization of cannabis in Netherlands failed to indicate this. Although there were a few new users, occassioned by its decriminalization, their numbers were not significantly high to warrant any concerns.Initial lifting of the ban would see maybe attraction of new users but this would later change as Clement k (1999,p49) says,..on the other hand,the disappearance of the forbidden fruits characteristics of marijuana would tend to lower consumption.†This waters down the argument that lifting the ban would see increased use.Marijuana too if allowed will lead to more expenses on the government to cope with its abuse. There will be increased allocations for the rehabs and medical facilities tao cope with its possible rampant usage. It would also mean more investments to cope with drug related crimes resulting from drug abuse. I must insist however, that findings doen do not ling marijuana to any violent crimes more than they do other drugs or specifically to alcohol. Also, a simple lsot benefit analysis refutes that the government stands to spend more on rehabs. By legalising it, any additional investment on rehabs is surpassed by the savings on marijuana policies and prosecutions in additional to the likely benefits emanating from taxes. (Aldrich, M. et al, 75-81).ConclusionThere is a n eed to review the current laws banning marijuana use in total.It is more beneficial to the general society and particularly the government.The benefits gained through taxation and employment creation surpasses the negative effects of rehabilitating the abusers.In addition too are the millions of dollars the federal government would save.The resources used to fight marijuana through prosecutions,arrests and maintaining thousands in jail would be rechanelled to other vital sectors.The benefits outweigh the costs,this should be the sole logic behind its legalisation.More benefits too stand to be reaped through the possible advances made in the medicine sector.It will see more research being done to establish ways in which marijuana can be used to ease pain in cancer patients as well as other medicinal values being explored.Works CitedAidriach, M. and T. uikuriya. â€Å"Savings in Carlifornia Marijuana law enforcement costsattributable to the Moscone Act of 1976†. Journal of Psyc hoactive drugs 20, 1998.75-81. Appraisals of the adverse health effects of cannabis use: Ideology andEvidence. June 1999.The FAS Drug policy Analysis Bulletin. Washington DC. Accessed on 3rd August 2007.https://fas.org/#3>Baker D. Pryce G. The therapeutic Potential of cannabis in multiple sclerosis. Expertopinion on investigations drugs. 2003, 561-567.Barnett, P.G. â€Å"The cost-effectiveness of substance abuse treatment†. Current psychiatryreports. 1999. 166-171.Thornton, M. â€Å"Prohibition US. Legalisation: Do Economists reach a conclusion of drug  policy? Ludwig Von Mises Institute 2002. pp.27Marijuana Legalisation: the time is now. 1998. The psycheditic Library home page.Clement K W; Daryal M.: The Economics of Marijuana Consumption.Crawley, Australia: University of Western Australia Economic Research center.1999, p 49.  indicate that marijuana resutls aviors of the user. ases, both studies indicate that there are no known risks associated with it

Statistics Coursework

1st Hypothesis – For my first hypothesis I will investigate the relationship between the number of TV hours watched per week by the pupils against their IQ. I am going to use the columns â€Å"IQ† and â€Å"Average number of hours TV watched per week† taken from the Mayfield high datasheet. I think that there will be a relationship between them and will attempt to reveal it. 2nd Hypothesis – For my second hypothesis I will investigate the relationship between â€Å"Average number of TV hours watched per week† and â€Å"weight (kg)†. I think that there will not be any major relationship between as they will not affect each other greatly. I will present my analysis and the results in graphs and tables and explain the results using the correlation of the graphs and arrangements of the figures. I will select a number of pupils to base my data on and will use random sampling to ascertain the correct number of male and female pupils needed to make the investigation fair. Stratified Sampling I do not want to use all of the data in the database for my analysis so I will need to take a sample of the number of people in the school. I would like to take about 10% of the overall figure. I will also need to use stratified sampling to make it an equal proportion of the number of males and females in the school to make it fair. The total number of pupils at the school is 813 so I will need to take 10% as my number, 81.3 is rounded down to 81. The overall ratio for boys and girls in the school is: 414:399 Now I will need to do my sampling Males = 414 multiplied by 81 = 41 813 Females = 399 multiplied by 81 = 40 813 Random Sampling Now I have the number of samples I will need to select the samples I will be taking. To do this I will use random sampling. I will take random samples until I have 81. I can do this on Excel using the following formula: = round(round()*120. Once I have gathered the samples I am ready to start analyzing my samples. Analysis Hypothesis 1 Males The first thing I need to do in my analysis is to analyze my graphs which are the source of the investigation. I have created scatter graphs to show the relationship if the two data sources for my first hypothesis. I have separated them into male and female graphs as there is a separation in the numbers. First male scatter graph: This first graph presented a bit of a problem. There was an anomalous result that affected the trend line and the scale of the graph. I decided to create a new graph that didn't include that 1 piece of data. This way it would help me to analyze the rest of the data. Second male scatter graph: This graph showed the data much clearer and I could then start analyzing it. There is no correlation between the 2 sets of data. This means that it is unlikely that there is a relationship between IQ and Average number of TV hours watched per week. In this it may be that my hypothesis is incorrect. There is only a very slight gradient on the trendline that leans towards a negative correlation, but the gradient is not steep enough to draw any conclusions about the relationship between the two sets of data. I will have to use the cumulative frequency graphs and boxplots to see if any conclusions can be made. Cumulative frequency graphs for IQ and Average number of TV hours watched per week: From these graphs I could create box plots and compare the two sets of data. Before that I analyzed the cumulative frequency graphs to draw initial conclusions. The majority of the IQs for males are between 90 – 105, this shows that the data is quite spread out as this section only covers a small area of the graph. For the TV hour's graph, again the data is spread among 1 main area; in this case it is between 5-25. There is almost a straight line near the top of the graph; this shows that there is likely to be some anomalous results and 0 pupils in between that result and the main bulk. Now I will create box plots so I can compare the two graphs together. Box plots for cumulative frequency graphs of IQ and average number of TV hours watched per week: (for interquartile ranges look at copies of graphs at the back) From the box plots I can see that the data spread is relatively the same apart from a possible anomalous result in the TV hour's data. This similarity is the reason why the scatter graph had no correlation and therefore no relationship. This means that my hypothesis is wrong. Hypothesis 1 Females Again I will start with the scatter graphs. As with the male graph I had an anomalous result that spread out the data and scale down the graph so most of the relevant data couldn't be analyzed. I then did another graph without that specific piece of data. Scatter Graphs 1 and 2 to show the relationship between IQ and average number of TV hours watched per week for Females: As you can see on both the graphs there is no correlation between the two sets of data. This again means that my first hypothesis is unlikely to be correct. There is only a slight gradient on the trend line which is not steep enough to draw any conclusions from it. There is another anomalous result on the graph but it doesn't affect the trend line and my conclusions so I left it on the graph. I will now crate cumulative frequency graphs to see if they can help me to draw conclusions. Cumulative frequency graphs for the IQ and number of TV hours watched per week: I will now analyze the graphs before drawing box plots to compare the graphs. The IQs graph is much more erratic which means that the data is spread over a larger range. Although there is 1 area where the data is concentrated and the gradient very steep, between 95-105. The TV hours graph is much smoother and the data less spread. The data number of hour's increases steadily to a certain point then it goes flat until the end. This means that there is a n anomalous result somewhere. I know that it can only be 1 or 2 anomalous because the point where it goes flat is at about 38 and there are only 39 sets of data in the graph. I will now look at the box plots to compare the two cumulative frequency graphs. Box plots for cumulative frequency graphs of IQ and number of TV hours watched for females: The box plots for these graphs show me that the IQ data has a much larger range and that it is quite evenly spread. I can see this because the interquartile range is quite large and the median evenly spread. There may be a few exceptions as 1 pupil is likey to have a very low IQ which is why the lowest value is so low. The TV hour's data seems to be much more concentrated and the data is generally lower. This shows that there can't be any relationship between them as they each grouped in certain areas. Also the box plot for TV hours shows that there is likely to bge an anomalous result as the highest value is so far out of the upper quartile. Hypothesis 2 Males In this hypothesis I will be comparing the Average number of TV hours watched per week and Weight, to see if there is any relationship between them. I will again start with Males and the Scatter graphs. Scatter graphs 1 and 2 to show the relationship between Weight and the Average number of TV hours watched per week for males: In these scatter graphs there is a slight negative correlation. This means that as the number of TV hours goes up Weight goes down. This may not be an accurate graph as there are a few anomalous results that may have caused the trend line to be that gradient. If this is so my hypothesis would have been correct, if it is not the gradient of the trend line isn't steep enough to say that it is 100% certain that it is accurate. I will need to use the cumulative frequency graphs to draw complete conclusions. Cumulative frequency graphs for the number of TV hours watched and Weights of males: These two graphs look quite different; the weights graph has most of its data concentrated in the middle of the range, between 30-50 and looks like a normal cumulative frequency curve. Whereas the number of TV hours has most of its data concentrated at the beginning between 0-30, showing that there is likely to be an anomalous result at the end of the range. These anomalous results on the TV hours graph are what caused the slight negative correlation on the trend line. I will be able to make complete conclusions after looking at the female sample and seeing if that graph follows suit. The box plots for these graphs will look quite different and will make it easy to make a simple comparison. Box plots for Cumulative frequency graphs IQ and Weight for males: From the box plots I can see that the two sets of data are almost identical in range which would cause a straight line on the scatter graph it is because of the anomalous results on the TV hours which caused the slight negative correlation. The weights box plot shows me that the data is quite evenly spread in the middle of the range apart from a very heavy person at the end which is why the highest figure is so far apart from the upper quartile. Overall the box plots show me that the similarity in the data means there is no relationship and hypothesis was correct. Hypothesis 2 Females Again I will start with the scatter graphs to show the relationship between Number of TV hours watched and weight. The graphs should be similar to the males and the conclusions the same. Again I had an anomalous result and had to create a second scatter graph without it there. Scatter graphs 1 and 2 to show the relationship between the Number of TV hours watched per week and Weight: The second scatter graph in this section, without the anomalous result completely changed the trend line. The first graph looks a lot more like the male graph whereas the second follows my hypothesis a lot better. In graph 1 there is a slight gradient on the graph which points towards a negative correlation, like those of the male sample. On the graph without the anomalous result there is clearly no correlation whatsoever as the line is nearly horizontal. I will take the results of the male sample to be wrong as I said earlier there are a few anomalous results which caused the trend line to be at that gradient. Now I will look at the cumulative frequency graphs to see what results I get from them. Cumulative frequency graphs for Average number of TV hours watched per week and Weight for Females: As on the males graph the TV hours for females have a lot of anomalous results. But for the scatter graphs I cancelled them all out which gave no correlation. If the line at the top of the TV hours graph is blanked out the two graphs look almost identical. This is why the scatter graph got a near horizontal trend line. The box plots for these to graphs will look alike apart from there will be a much longer line at the end of the TV hours graph because of the anomalous results. Box plots of cumulative frequency graphs for Number of TV hours watched and weights of females: These box plots show me the same as the males did, that the data is almost identical if placed 1 on top of the other. This is what caused the horizontal line in my scatter graphs and proves my hypothesis. Conclusion Hypothesis 1: My first hypothesis has been proved incorrect. The scatter graphs show that there is no correlation between the two sets of data. For my hypothesis to have been correct there would have needed to be a strong positive correlation. The cumulative frequency graphs and box plots again proved my hypothesis incorrect, the similarities in the two sets of data's box plots showed that there was no relationship and showed why the scatter graphs showed a straight line. Both the male and female samples showed that my hypothesis was incorrect although some anomalous results created a slight negative correlation in both it was obvious that it was still wrong. Hypothesis 2: My second hypothesis was proved correct. The scatter graphs showed that there was absolutely no correlation on the graphs which means no relationship. Although the male graphs did show a a negative correlation it was proved to be made by a few anomalous results by the cumulative frequency and later the inconsistency with the female sample. The female scatter graph showed a near horizontal trend line which was what I needed to prove my hypothesis. The similarities on the cumulative frequency graphs and box plots further proved my hypothesis was correct. Evaluation The investigation went quite well although my first hypothjesis was incorrect it showed that careful analysis of data is needed before drawing conclusions. When I next do an investigation into data I will use histograms to aid me in my analysis as they come in useful when looking for relationships in two sets of data as the cumulative frequency graphs do. I could have made the cumulative frequency graphs a little better as the program I used did not put a scale on the x axis but only the length of the range. Statistics Coursework 1st Hypothesis – For my first hypothesis I will investigate the relationship between the number of TV hours watched per week by the pupils against their IQ. I am going to use the columns â€Å"IQ† and â€Å"Average number of hours TV watched per week† taken from the Mayfield high datasheet. I think that there will be a relationship between them and will attempt to reveal it. 2nd Hypothesis – For my second hypothesis I will investigate the relationship between â€Å"Average number of TV hours watched per week† and â€Å"weight (kg)†. I think that there will not be any major relationship between as they will not affect each other greatly. I will present my analysis and the results in graphs and tables and explain the results using the correlation of the graphs and arrangements of the figures. I will select a number of pupils to base my data on and will use random sampling to ascertain the correct number of male and female pupils needed to make the investigation fair. Stratified Sampling I do not want to use all of the data in the database for my analysis so I will need to take a sample of the number of people in the school. I would like to take about 10% of the overall figure. I will also need to use stratified sampling to make it an equal proportion of the number of males and females in the school to make it fair. The total number of pupils at the school is 813 so I will need to take 10% as my number, 81.3 is rounded down to 81. The overall ratio for boys and girls in the school is: 414:399 Now I will need to do my sampling Males = 414 multiplied by 81 = 41 813 Females = 399 multiplied by 81 = 40 813 Random Sampling Now I have the number of samples I will need to select the samples I will be taking. To do this I will use random sampling. I will take random samples until I have 81. I can do this on Excel using the following formula: = round(round()*120. Once I have gathered the samples I am ready to start analyzing my samples. Analysis Hypothesis 1 Males The first thing I need to do in my analysis is to analyze my graphs which are the source of the investigation. I have created scatter graphs to show the relationship if the two data sources for my first hypothesis. I have separated them into male and female graphs as there is a separation in the numbers. First male scatter graph: This first graph presented a bit of a problem. There was an anomalous result that affected the trend line and the scale of the graph. I decided to create a new graph that didn't include that 1 piece of data. This way it would help me to analyze the rest of the data. Second male scatter graph: This graph showed the data much clearer and I could then start analyzing it. There is no correlation between the 2 sets of data. This means that it is unlikely that there is a relationship between IQ and Average number of TV hours watched per week. In this it may be that my hypothesis is incorrect. There is only a very slight gradient on the trendline that leans towards a negative correlation, but the gradient is not steep enough to draw any conclusions about the relationship between the two sets of data. I will have to use the cumulative frequency graphs and boxplots to see if any conclusions can be made. Cumulative frequency graphs for IQ and Average number of TV hours watched per week: From these graphs I could create box plots and compare the two sets of data. Before that I analyzed the cumulative frequency graphs to draw initial conclusions. The majority of the IQs for males are between 90 – 105, this shows that the data is quite spread out as this section only covers a small area of the graph. For the TV hour's graph, again the data is spread among 1 main area; in this case it is between 5-25. There is almost a straight line near the top of the graph; this shows that there is likely to be some anomalous results and 0 pupils in between that result and the main bulk. Now I will create box plots so I can compare the two graphs together. Box plots for cumulative frequency graphs of IQ and average number of TV hours watched per week: (for interquartile ranges look at copies of graphs at the back) From the box plots I can see that the data spread is relatively the same apart from a possible anomalous result in the TV hour's data. This similarity is the reason why the scatter graph had no correlation and therefore no relationship. This means that my hypothesis is wrong. Hypothesis 1 Females Again I will start with the scatter graphs. As with the male graph I had an anomalous result that spread out the data and scale down the graph so most of the relevant data couldn't be analyzed. I then did another graph without that specific piece of data. Scatter Graphs 1 and 2 to show the relationship between IQ and average number of TV hours watched per week for Females: As you can see on both the graphs there is no correlation between the two sets of data. This again means that my first hypothesis is unlikely to be correct. There is only a slight gradient on the trend line which is not steep enough to draw any conclusions from it. There is another anomalous result on the graph but it doesn't affect the trend line and my conclusions so I left it on the graph. I will now crate cumulative frequency graphs to see if they can help me to draw conclusions. Cumulative frequency graphs for the IQ and number of TV hours watched per week: I will now analyze the graphs before drawing box plots to compare the graphs. The IQs graph is much more erratic which means that the data is spread over a larger range. Although there is 1 area where the data is concentrated and the gradient very steep, between 95-105. The TV hours graph is much smoother and the data less spread. The data number of hour's increases steadily to a certain point then it goes flat until the end. This means that there is a n anomalous result somewhere. I know that it can only be 1 or 2 anomalous because the point where it goes flat is at about 38 and there are only 39 sets of data in the graph. I will now look at the box plots to compare the two cumulative frequency graphs. Box plots for cumulative frequency graphs of IQ and number of TV hours watched for females: The box plots for these graphs show me that the IQ data has a much larger range and that it is quite evenly spread. I can see this because the interquartile range is quite large and the median evenly spread. There may be a few exceptions as 1 pupil is likey to have a very low IQ which is why the lowest value is so low. The TV hour's data seems to be much more concentrated and the data is generally lower. This shows that there can't be any relationship between them as they each grouped in certain areas. Also the box plot for TV hours shows that there is likely to bge an anomalous result as the highest value is so far out of the upper quartile. Hypothesis 2 Males In this hypothesis I will be comparing the Average number of TV hours watched per week and Weight, to see if there is any relationship between them. I will again start with Males and the Scatter graphs. Scatter graphs 1 and 2 to show the relationship between Weight and the Average number of TV hours watched per week for males: In these scatter graphs there is a slight negative correlation. This means that as the number of TV hours goes up Weight goes down. This may not be an accurate graph as there are a few anomalous results that may have caused the trend line to be that gradient. If this is so my hypothesis would have been correct, if it is not the gradient of the trend line isn't steep enough to say that it is 100% certain that it is accurate. I will need to use the cumulative frequency graphs to draw complete conclusions. Cumulative frequency graphs for the number of TV hours watched and Weights of males: These two graphs look quite different; the weights graph has most of its data concentrated in the middle of the range, between 30-50 and looks like a normal cumulative frequency curve. Whereas the number of TV hours has most of its data concentrated at the beginning between 0-30, showing that there is likely to be an anomalous result at the end of the range. These anomalous results on the TV hours graph are what caused the slight negative correlation on the trend line. I will be able to make complete conclusions after looking at the female sample and seeing if that graph follows suit. The box plots for these graphs will look quite different and will make it easy to make a simple comparison. Box plots for Cumulative frequency graphs IQ and Weight for males: From the box plots I can see that the two sets of data are almost identical in range which would cause a straight line on the scatter graph it is because of the anomalous results on the TV hours which caused the slight negative correlation. The weights box plot shows me that the data is quite evenly spread in the middle of the range apart from a very heavy person at the end which is why the highest figure is so far apart from the upper quartile. Overall the box plots show me that the similarity in the data means there is no relationship and hypothesis was correct. Hypothesis 2 Females Again I will start with the scatter graphs to show the relationship between Number of TV hours watched and weight. The graphs should be similar to the males and the conclusions the same. Again I had an anomalous result and had to create a second scatter graph without it there. Scatter graphs 1 and 2 to show the relationship between the Number of TV hours watched per week and Weight: The second scatter graph in this section, without the anomalous result completely changed the trend line. The first graph looks a lot more like the male graph whereas the second follows my hypothesis a lot better. In graph 1 there is a slight gradient on the graph which points towards a negative correlation, like those of the male sample. On the graph without the anomalous result there is clearly no correlation whatsoever as the line is nearly horizontal. I will take the results of the male sample to be wrong as I said earlier there are a few anomalous results which caused the trend line to be at that gradient. Now I will look at the cumulative frequency graphs to see what results I get from them. Cumulative frequency graphs for Average number of TV hours watched per week and Weight for Females: As on the males graph the TV hours for females have a lot of anomalous results. But for the scatter graphs I cancelled them all out which gave no correlation. If the line at the top of the TV hours graph is blanked out the two graphs look almost identical. This is why the scatter graph got a near horizontal trend line. The box plots for these to graphs will look alike apart from there will be a much longer line at the end of the TV hours graph because of the anomalous results. Box plots of cumulative frequency graphs for Number of TV hours watched and weights of females: These box plots show me the same as the males did, that the data is almost identical if placed 1 on top of the other. This is what caused the horizontal line in my scatter graphs and proves my hypothesis. Conclusion Hypothesis 1: My first hypothesis has been proved incorrect. The scatter graphs show that there is no correlation between the two sets of data. For my hypothesis to have been correct there would have needed to be a strong positive correlation. The cumulative frequency graphs and box plots again proved my hypothesis incorrect, the similarities in the two sets of data's box plots showed that there was no relationship and showed why the scatter graphs showed a straight line. Both the male and female samples showed that my hypothesis was incorrect although some anomalous results created a slight negative correlation in both it was obvious that it was still wrong. Hypothesis 2: My second hypothesis was proved correct. The scatter graphs showed that there was absolutely no correlation on the graphs which means no relationship. Although the male graphs did show a a negative correlation it was proved to be made by a few anomalous results by the cumulative frequency and later the inconsistency with the female sample. The female scatter graph showed a near horizontal trend line which was what I needed to prove my hypothesis. The similarities on the cumulative frequency graphs and box plots further proved my hypothesis was correct. Evaluation The investigation went quite well although my first hypothjesis was incorrect it showed that careful analysis of data is needed before drawing conclusions. When I next do an investigation into data I will use histograms to aid me in my analysis as they come in useful when looking for relationships in two sets of data as the cumulative frequency graphs do. I could have made the cumulative frequency graphs a little better as the program I used did not put a scale on the x axis but only the length of the range.