Health and Social Care P5 Essay

Multi agency working This is where professionals from multiple agencies meet together to work towards the best possible care of an individual. They will combine their skills and do a single assessment to assess needs of the client, as opposed to each professional doing an individual assessment. Working in partnerships with adults using services This is where encouragement for the use of services helps to gain trust between professionals and clients or their families. By ensuring of policies and working routines, then the cared for individual can feel more confident that they will be able to flag up any concerns, worries or comments. It promotes a better relationship between cared-for and care-giver. ISA This protects vulnerable adults from people who may want to abuse them, or have a history of abusing. Before a paid worker or voluntary worker can work with vunerable adults, they will have to be vetted and have a check for no prior history of abuse. If a person has a history of abusing, then they will be placed on a ‘barred’ list and will then be unable to work with vulnerable people. This means that employers will have to receive appropriate references, have a DBS check done and check the barred list, before they can employ a person to work with vulnerable adults. Criminal Records Bureau People who wish to work with vulnerable adults will have to undergo and in-depth DBS check before they are able to work with vulnerable people. This check looks at their criminal history. It will show any previous cautions or convictions. No Secrets This guideline means that any group, no matter how big or small, must have a set of policies and guidelines about what is expected of the people who work within the organisation. It shows staff members what is appropriate and inappropriate care. Codes of Practice This sets standards for all people working with vulnerable people, such as nurses and midwives. Usually these codes of practice are overseen by larger bodies that regulate the workers. People who do not follow the codes of practice may be unable to remain in their chosen profession. National Frame for Good Practice and Outcomes in Adult Protection This sets a group of national standards or service models to show how best to deliver a service. It promotes multi agency working to provide the best level of care for a service user. Dignity in Care initiative This is a campaign to stop lack of respect show to service users and to ensure that their dignity is maintained through good and positive service. It encourages challenging of poor service or lack of respect. Human Rights in Healthcare This is assists and promotes the use of human rights practice in work within delivery of care services, particularly within the NHS. It encourages fairness, respect, equality, dignity and autonomy. Closer working between professionals This means that records to care will be made and then logged so that the care can be noted and continued or altered. It means that concerns can be shared and can bring attention to alterations in behaviour towards particular members of staff or visitors. It can also log any illness. The communication means that care givers can work together to provide continuous care. Care Quality Commission This ensures that all organisations under their watch are working to a set of rules and regulations that apply to them. They regulate the care given and inspect institutions to ensure that optimal care that conforms to regulations is given to all of the people in its care. Organisational policies This teaches staff what is expected of them and what is inappropriate. It also means that a complaints procedure will exist, so that people who  believe there is a problem with care given can be challenged fairly, whilst people are protected from any possible ill-consequences of complaint. This also means that whenever a new policy is enacted, that appropriate staff training will be undertaken and explained. It ensures that staff are well aware of any consequences as a result of abusing. Decision making forums This ensures that the decision making process is kept clear and does not remain secretive. It gives individuals the chance to be an active member in the decisions that affect their care or their lives. They can be supported to make the best decision that will best affect them. It gives an opportunity for opinions and views on it to be explained, alongside with any procedures or guidelines that may be in effect as a part of the decision. Whistleblowing This is where a member of an organisation informs members an employer or a regulatory body of ill-practice within the organisation that they work in. They will be protected by the Public Interest Disclosure act, meaning that they will be appropriately protected from any suffering that may be caused by an individual raising awareness, such as dismissal or bullying. Effective relationship building This means that relationships between service users and professionals are kept appropriate and the duty of care remains the main responsibility. The worker must work to a set of guidelines or accepted values. The relationship between user and worker must remain professional and not overstep boundaries set by professional bodies. It means that whilst the wishes and dignity of the user will still be kept, the relationship will be equal yet maintain the importance of the status of carer and cared-for. Principles of care

Marriage versus living together

Trends are showing that there is a change in the way intimate relationships are constituted throughout the world. Cohabiting, in the absence of a marriage contract has only become so common over the past few decades. This practice of living with a partner in an intimate relationship that does not involve signing a marriage contract, referred to by any of the terms consensual unions, cohabiting unions, cohabitation, or living together, is evidenced both in developed and developing countries. Mokomane (2005) notes an increase over marriage in this type of relationship (p. 57). Kenny and McLanahan also observe that, in the U. S., cohabitation has surpassed marriage as the preferred mode of intimate unions. It is useful, therefore, to understand differences between a cohabiting and a marriage relationship, in order to determine why couples are now choosing this type of union over marriage. The most apparent difference between a marriage and a cohabiting relationship is in their institutional makeup. A marriage is, by its very nature, a contractual type of relationship where both couples agree to an intimate union. A cohabiting relationship is also an agreement between two partners. The difference is that a marriage requires signing a legal document representative of the couples’ legal obligations to each other. In a cohabiting union such legality is not evident. Marriage relationships are therefore more permanent than cohabiting unions specifically because of this legality. A cohabiting couple may choose to end a relationship at any time without facing much external difficulties but a married couple has to apply for a divorce. Simply put a married couple is legally accountable for either staying together or separating while this is not so in the case of cohabiting couples. Mokomane (2005) notes a further difference in the average age of individuals who enter either union. She has observed that couples in a cohabiting relationship are usually much younger than those in a cohabiting relationship. From her research it was discovered that cohabiting men and women average 37.9 and 32.8 years respectively while their married counterparts average 51.3 and 45.6 years respectively based on the 2001 census in the U.S. (p. 63). This is suggesting that cohabiting relationships is usually the first choice relationship and later there is the transfer into a marriage union when the individual gets older. It has also been noted that married couples earn more than their cohabiting counterparts. According to Clarkberg (1999), income is usually a predictive factor for individuals to get married. She argues that, since individuals with more income seem to be the ones that enter into married unions, then a good income is probably seen as a requirement for entering into a married union. Clarkberg cites research conducted in Puerto Rico in which it was concluded that cohabiting relationships are ‘a poor man’s marriage’(p. 947). Markowski, Croake and Keller also found that cohabiting couples had more lifetime partners than married couples suggesting a higher rate of promiscuity in the former group. They establish that cohabiting couples are more likely than married couples to have had more than six sexual partners (p. 33). Finally research has found that there is a higher rate of domestic violence among couples that are cohabiting than those that are married. Even further there are also higher rates of homicide within this group (Kenny & McLanahan, 2006). In consistent cases it has been found that the rate of domestic violence among married couples is significantly less than the rate among cohabiting couples. These researchers estimate that cohabiting relationships are between two and four times more likely to involve domestic violence than married relationships. It appears therefore that, for reasons of economy and because of its perceived permanence, more persons are staying away from marriage relationships, at least in their younger days, while enjoying the privileges of an intimate living relationship with their partner. Related essay: â€Å"My Ideal Wife† Reference Clarkberg, M. (1999). The price of partnering: The role of economic well-being in young adults’ first union experiences. Social Forces, 77(3), 945-968. Kenny, C. T. & McLanahan, S. S. (2006, Feb). Why are cohabiting relationships more violent than marriages? Demography, 43(1), 127-140. Markowski, E. M., Croake, J. W. & Keller, J. F. (1978, Feb). Sexual history and present sexual behavior of cohabiting and married couples. The Journal of Sex Research, 14(1), 27-39. Mokomane, Z. (2005). A demographic and socio-economic portrait of cohabitation in Botswana. Society in Transition, 36(1), 57-73.   

Letter to Editor-Conservative View-Sci/275

SCI/275 Environmental Science Letters to the Editor (Conservationist) Dear Editor: As is evidenced in recent world news and events, taking steps to energy independence is paramount to the continued survival of the United States as a Nation. Growing increasingly dependent on the natural resources in other parts of the world further compounds the possibility for this independence. As a conservationist, it is my belief that we as a country can begin to develop the means required to carefully and sensibly manage our natural resources in an effort to usher in this independence.For instance, the Bridger Teton National Forest houses 3. 4 million acres of land that has gone untouched, and undisturbed by increases in population and industrialization. As such, the resources here have gone untapped. Consider being able to provide incentives for the local industries such as forestry, and mining in the area, to use more environmentally friendly technologies to not only increase the effectiveness of harvesting in these areas, but to also minimize the damaging effects of this harvesting to the surrounding landscape and ecosystems.The implantation of environmental taxes requiring those businesses that take advantage of this opportunity, to pay an amount equal to the harm they cause on the environment will further increase the use of more eco-friendly technologies. A(n) tradable permits system is also a consideration, limiting the total amounts of pollutant that can be released, allowing both persons and businesses to buy and sell rights to emit and reduce emissions at the least cost to them.These efforts will not only prove useful in making us the independent, nation that has been the basis of our existence, but will also provide us the means to sustain the natural resources required for our continued existence, and ability to support the ever growing human population. We MUST consider more effective means to tap into the natural resources that the earth has provided us as its inhabitants, without focusing on the monetary gain from the same which will lead to a harmonious balance of both conservation and economic independence as a nation. Sincerely, Concerned Citizen

The Relationship Between Life Expectancy at Birth and Gdp Per Capita

The relationship between Life Expectancy at birth and GDP per capita (PPP) Candidate: Teacher: Candidate number: Date of submission: Word Count: 2907 Section 1: Introduction In a given country, Life Expectancy at birth is the expected number of years of life from birth. Gross domestic product per capita is defined as the market value of all final goods and services produced within a country in one year, divided by the size of the population of that country. The main objective of the present project is to establish the existence of a statistical relation between Life Expectancy (y) at birth and GDP per capita (x).First, we will present in Section 2 the data, from an official governmental source, containing Life Expectancy at birth and GDP per capita of 48 countries in the year 2003. We will put this data in a table ordered alphabetically and at the end of the section we will perform some basic statistical analysis of these data. These statistics will include the mean, median, modal cl ass and standard deviation, for both Life Expectancy and GDP per capita. In Section 3 we will find the regression line which best fits our data and the corresponding correlation coefficient r.It is natural to ask if there is a non-linear model, which better describes the statistical relation between GDP per capita and Life Expectancy. This question will be studied in Section 4, where we will see if a logarithmic relation of type y=A ln(x+C) + B, is a better model. In Section 5 we will perform a chi square test to get evidence of the existence of a statistical relation between the variables x and y. In the last section of the project, other than summarizing the obtained results, we will present several possible directions to further investigation. Section 2: Data collectionThe following table shows the GDP per capita (PPP) (in US Dollars), denoted xi, and the mean Life Expectancy at birth (in years), denote yi, in 48 countries in the year 2003. The data has been collected through an online website (2). According to this website it represents official world records. Country| GDP – per capita (xi)| Life Expectancy at birth (yi)| 1. Argentina| 11200| 75. 48| 2. Australia| 29000| 80. 13| 3. Austria| 30000| 78,17| 4. Bahamas, The| 16700| 65,71| 5. Bangladesh| 1900| 61,33| 6. Belgium| 29100| 78,29| 7. Brazil| 7600| 71,13| 8. Bulgaria| 7600| 71,08| 9. Burundi| 600| 43,02| 10. Canada| 29800| 79,83| 1. Central African Republic| 1100| 41,71| 12. Chile| 9900| 76,35| 13. China| 5000| 72,22| 14. Colombia| 6300| 71,14| 15. Congo, Republic of the| 700| 50,02| 16. Costa Rica| 9100| 76,43| 17. Croatia| 10600| 74,37| 18. Cuba| 2900| 76,08| 19. Czech Republic| 15700| 75,18| 20. Denmark| 31100| 77,01| 21. Dominican Republic| 6000| 67,96| 22. Ecuador| 3300| 71,89| 23. Egypt| 4000| 70,41| 24. El Salvador| 4800| 70,62| 25. Estonia| 12300| 70,31| 26. Finland| 27400| 77,92| 27. France| 27600| 79,28| 28. Georgia| 2500| 64,76| 29. Germany| 27600| 78,42| 30. Ghana| 2200| 56,53| 31. Greece| 20000| 78,89| 32. Guatemala| 4100| 65,23| 33.Guinea| 2100| 49,54| 34. Haiti| 1600| 51,61| 35. Hong Kong| 28800| 79,93| 36. Hungary| 13900| 72,17| 37. India| 2900| 63,62| 38. Indonesia| 3200| 68,94| 39. Iraq| 1500| 67,81| 40. Israel| 19800| 79,02| 41. Italy| 26700| 79,04| 42. Jamaica| 3900| 75,85| 43. Japan| 28200| 80,93| 44. Jordan| 4300| 77,88| 45. South Africa| 10700| 46,56| 46. Turkey| 6700| 71,08| 47. United Kingdom| 27700| 78,16| 48. United States| 37800| 77,14| Table1: GDP per capita and Life Expectancy at birth in 48 countries in 2003 (source: reference [2]) Statistical analysis: First we compute some basic statistics of the data collected in the above table.Basic statistics for the GDP per capita: Mean: x=i=148xi48 = 12900 In order to compute the median, we need to order the GDP values: 600, 700, 1100, 1500, 1600, 1900, 2100, 2200, 2500, 2900, 2900, 3200, 3300, 3900, 4000, 4100, 4300, 4800, 5000, 6000, 6300, 6700, 7600, 7600, 9100, 9900, 10600, 10700, 11200, 12300, 13900, 15700, 16700, 19800, 20000, 26700, 27400, 27600, 27600, 27700, 28200, 28800, 29000, 29100, 29800, 30000, 31100, 37800. The median is obtained as the middle value of the two central values (the 25th and the 26th): Median= 7600+91002 = 8350 In order to compute the modal class, we need to split the data in classes.If we consider classes of USD 1000 (0-999, 1000-1999, †¦) we have the following table of frequencies: Class| Frequency| 0-999| 2| 1000-1999| 4| 2000-2999| 5| 3000-3999| 3| 4000-4999| 4| 5000-5999| 1| 6000-6999| 3| 7000-7999| 2| 8000-8999| 0| 9000-10000| 2| 10000-10999| 2| 11000-11999| 1| 12000-12999| 1| 13000-13999| 1| 14000-14999| 0| 15000-15999| 1| 16000-16999| 1| 17000-17999| 0| 18000-18999| 0| 19000-19999| 1| 20000-20999| 1| 21000-21999| 0| 22000-22999| 0| 23000-23999| 0| 24000-24999| 0| 25000-25999| 0| 26000-26999| 1| 27000-27999| 4| 28000-28999| 2| 29000-29999| 3| 30000-30999| 1| 31000-31999| 1| 32000-32999| 0| 3000-33999| 0| 34000-34999| 0| 35000-35999| 0| 36000-36999| 0| 37000-38000| 1| Table 2: Frequencies of GDP per capita with classes of USD 1000 With this choice of classes, the modal class is 2000-2999 (with a frequency of 5). If instead we consider classes of USD 5000 (0-4999, 5000-9999, †¦) the modal class is the first: 0-4999 (with a frequency of 18). Class| Frequency| 0-4999| 18| 5000-9999| 8| 10000-14999| 5| 15000-19999| 3| 20000-24999| 1| 25000-29999| 10| 30000-34999| 2| 35000-40000| 1| Table 3: Frequencies of GDP per capita with classes of USD 5000 Standard deviation: Sx=i=148(xi-x)248 =11100Basic statistics for the Life Expectancy: Mean: y=i=148yi48 = 70,13 As before, in order to compute the median, we need to order the Life Expectancies: 41. 71, 43. 02, 46. 56, 49. 54, 50. 02, 51. 61, 56. 53, 61. 33, 63. 62, 64. 76, 65. 23, 65. 71, 67. 81, 67. 96, 68. 94, 70. 31, 70. 41, 70. 62, 71. 08, 71. 08, 71. 13, 71. 14, 71. 89, 72. 17, 72. 22, 74. 37, 75. 18, 75. 48, 75. 85, 76. 08, 76. 35, 76. 43, 77. 01, 77. 14, 77. 88, 77. 92, 78. 16, 78. 17, 78. 29, 78. 42, 78. 89, 79. 02, 79. 04, 79. 28, 79. 83, 79. 93, 80. 13, 80. 93. The median is obtained as the middle value of the two central values:Median= 72,17+72,222 = 72. 195 To find the modal class of Life Expectancy we consider modal classes of one year. The table of frequencies is the following Class| Frequency | 41| 1| 42| 0| 43| 1| 44| 0| 45| 0| 46| 1| 47| 0| 48| 0| 49| 1| 50| 1| 51| 1| 52| 0| 53| 0| 54| 0| 55| 0| 56| 1| 57| 0| 58| 0| 59| 0| 60| 0| 61| 1| 62| 0| 63| 1| 64| 1| 65| 2| 66| 0| 67| 2| 68| 1| 69| 0| 70| 3| 71| 5| 72| 2| 73| 0| 74| 1| 75| 3| 76| 3| 77| 4| 78| 5| 79| 5| 80| 2| Table 4: Frequencies of Life Expectancy at birth with classes of 1 year It appears from the table above that there are three modal classes: 71, 78 and 79 (with a frequency of 5).Standard deviation: Sy=i=148(yi-y)248 =10. 31 The standard deviations Sx and Sy have been found using the following table of data: Country| GDP| Life exp. | (x – x? ) | (x – x? )2| (y – ? y)| (y – y? )2| (x – x ? )(y – y ? )| Argentina| 11200| 75. 48| -1665| 2770838| 5. 35| 28. 64| -8907. 60| Australia| 29000| 80. 13| 16135| 260351671| 10. 00| 100. 03| 161374. 34| Austria| 30000| 78. 17| 17135| 293622504| 8. 04| 64. 66| 137790. 17| Bahamas. The| 16700| 65. 71| 3835| 14710421| -4. 42| 19. 53| -16947. 75| Bangladesh| 1900| 61. 33| -10965| 120222088| -8. 80| 77. 42| 96474. 63| Belgium| 29100| 78. 29| 16235| 263588754| 8. 16| 66. 1| 132501. 29| Brazil| 7600| 71. 13| -5265| 27715838| 1. 00| 1. 00| -5271. 16| Bulgaria| 7600| 71. 08| -5265| 27715838| 0. 95| 0. 90| -5007. 93| Burundi| 600| 43. 02| -12265| 150420004| -27. 11| 734. 88| 332477. 52| Canada| 29800| 79. 83| 16935| 286808338| 9. 70| 94. 11| 164294. 71| Central African Republic| 1100| 41. 71| -11765| 138405421| -28. 42| 807. 63| 334334. 75| Chile| 9900| 76. 35| -2965| 8788754| 6. 22| 38. 70| -18443. 41| China| 5000| 72. 22| -7865| 61851671| 2. 09| 4. 37| -16446. 81| Colombia| 6300| 71. 14| -6565| 43093754| 1. 01| 1. 02| -6638. 43| Congo. Republic of the| 700| 50. 02| -12165| 147977088| -20. 1| 404. 36| 244614. 57| Costa Rica| 9100| 76. 43| -3765| 14172088| 6. 30| 39. 71| -23721. 58| Croatia| 10600| 74. 37| -2265| 5128338| 4. 24| 17. 99| -9604. 66| Cuba| 2900| 76. 08| -9965| 99292921| 5. 95| 35. 42| -59301. 73| Czech Republic| 15700| 75. 18| 2835| 8039588| 5. 05| 25. 52| 14322. 40| Denmark| 31100| 77. 01| 18235| 332530421| 6. 88| 47. 35| 125482. 46| Dominican Republic| 6000| 67. 96| -6865| 47122504| -2. 17| 4. 70| 14887. 57| Ecuador| 3300| 71. 89| -9565| 91481254| 1. 76| 3. 10| -16845. 62| Egypt| 4000| 70. 41| -8865| 78580838| 0. 28| 0. 08| -2493. 16| El Salvador| 4800| 70. 62| -8065| 65037504| 0. 9| 0. 24| -3961. 73| Estonia| 12300| 70. 31| -565| 318754| 0. 18| 0. 03| -102. 33| Finland| 27400| 77. 92| 14535| 211278338| 7. 79| 60. 70| 113249. 07| France| 27600| 79. 28| 14735| 217132504| 9. 15| 83. 75| 134847. 48| Georgia| 2500| 64. 76| -10365| 107424588| -5. 3 7| 28. 82| 55644. 86| Germany| 27600| 78. 42| 14735| 217132504| 8. 29| 68. 74| 122175. 02| Ghana| 2200| 56. 53| -10665| 113733338| -13. 60| 184. 93| 145025. 00| Greece| 20000| 78. 89| 7135| 50914171| 8. 76| 76. 76| 62515. 17| Guatemala| 4100| 65. 23| -8765| 76817921| -4. 90| 24. 00| 42935. 50| Guinea| 2100| 49. 54| -10765| 115876254| -20. 59| 423. 0| 221629. 32| Haiti| 1600| 51. 61| -11265| 126890838| -18. 52| 342. 94| 208606. 00| Hong Kong| 28800| 79. 93| 15935| 253937504| 9. 80| 96. 06| 156187. 00| Hungary| 13900| 72. 17| 1035| 1072088| 2. 04| 4. 17| 2113. 54| India| 2900| 63. 62| -9965| 99292921| -6. 51| 42. 36| 64856. 98| Indonesia| 3200| 68. 94| -9665| 93404171| -1. 19| 1. 41| 11488. 77| Iraq| 1500| 67. 81| -11365| 129153754| -2. 32| 5. 38| 26351. 63| Israel| 19800| 79. 02| 6935| 48100004| 8. 89| 79. 05| 61664. 52| Italy| 26700| 79. 04| 13835| 191418754| 8. 91| 79. 41| 123290. 86| Jamaica| 3900| 75. 85| -8965| 80363754| 5. 72| 32. 73| -51288. 2| Japan| 28200| 80. 93| 15335| 235 175004| 10. 80| 116. 67| 165641. 67| Jordan| 4300| 77. 88| -8565| 73352088| 7. 75| 60. 08| -66386. 23| South Africa| 10700| 46. 56| -2165| 4685421| -23. 57| 555. 49| 51016. 52| Turkey| 6700| 71. 08| -6165| 38002088| 0. 95| 0. 90| -5864. 06| United Kingdom| 27700| 78. 16| 14835| 220089588| 8. 03| 64. 50| 119146. 94| United States| 37800| 77. 14| 24935| 621775004| 7. 01| 49. 16| 174828. 44| Table 5: Statistical analysis of the data collected in Table 1 From the last column we can compute the covariance parameter of the GDP and Life Expectancy: Sxy =148 i=148(xi-x)(yi-y)= 73011. 6 Section 3: Linear regression We start our investigation by studying the line best fit of the data in Table 1. This will allow us to see whether there is a relation of linear dependence between GDP and Life Expectancy. The regression line for the variables x and y is given by the following formula: y-y  ? =SxySx2(x-x ) By using the values found above we get: y= 62. 51 + 0. 5926*10-3 x The Pearson's correlati on coefficient is: r = 0. 6380 The following graph shows the data on Table 1 together with the line of best fit computed Figure 1: Linear regression. The value of the correlation coefficient r ~ 0. , is evidence of a moderate positive linear correlation between the variables x and y. On the other hand it is apparent from the graph above that the relation between the variables is not exactly linear. In the next section we will try to speculate on the reason for this non-linear relation and on what type of statistical relation can exist between GDP per capita and Life Expectancy. Section 4: Logarithmic regression As explained in reference [3], â€Å"the main reason for this non-linear relationship [between GDP per capita and Life Expectancy] is because people consume both needs and wants.People consume needs in order to survive. Once a person’s needs are satisfied, they could then spend the rest of their money on non-necessities. If everyone’s needs are satisfied, then any increase in GDP per capita would barely affect Life Expectancy. â€Å" There are various other reasons that one can think of, to explain the non-linear relationship between GDP per capita and Life Expectancy. For example the GDP per capita is the average wealth, while one should consider also how the global wealth is distributed among the population of a given country.With this in mind, to have a more complete picture of the statistical relation between economy of a country and Life Expectancy, one should take into considerations also other economic parameters, such as the Inequality Index, that describe the distribution of wealth among the population. Moreover, the wealth of the population is not the only factor effecting Life Expectancy: one should also take into account, for example, the governmental policies of a nation towards health and poverty. For example Cuba, a country with a very low GDP per capita ($ 2900), has a relatively high Life Expectancy (76. 8 years), mostly due to the fact that the government provides basic needs and health assistance to the population. Some of these aspects will be discussed in the next section. Let’s try to guess what could be a reasonable relation between the variables x (GDP per capita) and y (Life Expectancy). According to the above observations we can consider the total GDP formed by two values: x= xn + xw, where xn denotes the part of wealth spent on necessities, and xw denotes the part spent on wants.It is reasonable to make the following assumptions: 1. The Life Expectancy depends linearly on the part of wealth spent on necessities: y=axn + b, (1) 2. The fraction xn/x of wealth spent on necessities, is close to 1 when x is close to 0 (if one has a little amount of money he/she will spend most of it on necessities), and is close to 0 when x is very large (if one has a very large money he/she will spend only a little fraction of on necessities). 3.We make the following choice for the function xn= f(x) sa tisfying the above requirements: xn= log (cx + 1)/c, (2) where c is some positive parameter. This function is chosen mainly for two reasons. On one hand it satisfies the requirements that are describe in 2, indeed the corresponding graph of xn/x = f(x) = log (cx + 1)/cx: Figure 2: Graph of the function y= log (cx + 1)/cx, for C=0. 5 (blue), 1 (black) and 10 (red). The blue, black and red lines correspond respectively to the choice of parameter c= 0. 5, 1 and 10.As it appears from the graph in all cases we have f(0)= 1 and f(x) is small for large values of x. On the other hand the function chosen allows us to use the statistical tools at our disposal in the excel software to derive some interesting conclusion about the statistical relation between x and y. This is what we are going to do next. First we want to find the relation between x and y under the above assumptions. Putting together equations (1) and (2) we get: y= aclncx+1+b, (3) which shows that there is a logarithmic depende nce between x and y.Equation (3) can be rewritten in the following equivalent form: if we denote A=a/c, B= b+(a/c)ln(c), C=1/c, y=Aln(x+C)+B . (4) We can now study the curve of type (4) which best fits the data in Table 1, using the statistical tools of excel spreadsheet. Unfortunately excel allows us to plot only a curve of type y= Aln(x) + B (i. e. equation of type four where C is equal to 0). For this choice of C, we get the following logarithmic curve of best fit together with the corresponding value of correlation coefficient r2. Figure 3: Logarithmic regression.To find the analogous curve of best fit for a given value of C (positive, arbitrarily chosen) we can simply add C to all the x values and redo the same plot as for C= 0 with the new independent variable x1= x + C. We omit showing the graphs containing the curve of best fit for all the possible values of C and we simply report, in the following table, the correlation coefficient r for some appropriately chosen values of C. C| r| 0. 00| 0. 77029| 0. 01| 0. 77029| 0. 1| 0. 77028| 1| 0. 77025| 10| 0. 76991| 100| 0. 76666| Table 8: correlation coefficient r2 for the curve of best fit y= Aln(x+C) +B, for some values of C. The above data indicate that the optimal choice of C is between 0. 00 and 0. 01, since in this case r is the closest to 1. Comparing the results got with the linear regression (r ~ 0,6) and the logarithmic regression (r ~ 0,8) we can conclude that the latter appears to be a better model to describe the relation between GDP per capita and Life Expectancy, since the value of the correlation coefficient is significantly bigger. From Figure 3 one the data is very far from the curve of best fit and so we may decide to discuss it separately and do the regression without it.This data is corresponds to South Africa with a GDP per capita of 10700 and a Life Expectancy at birth of 46. 56 (much lower than any other country with a comparable GDP). It is reasonable to think that this anomaly is due to the peculiar history of South Africa which, after the end of apartheid, had to face an uncontrolled violence. It is therefore difficult to fit this country in a statistical model and we can decide to remove it from our data. Doing so, we get the following new plot. Figure 4: Logarithmic regression for the data in Table 1 excluding South Africa. The new value of correlation coefficient r~ 0. 3 indicates that, excluding the anomalous data of South Africa, there is a strong positive logarithmic correlation between GDP per capita and Life Expectancy at birth. Section 5: Chi square test (? 2? test) We conclude our investigation by making a chi square test. This will allow us to confirm the existence of a relation between the variables x and y. For this purpose we formulate the following null and alternative hypotheses. H0: GDP and Life Expectancy are not correlated. H1: GDP and Life Expectancy are correlated * Observed frequency: The observed frequencies are obtained directly from Ta ble 2: | Below y? | Above y? | Total|Below x| 14| 1| 15| Above x| 16| 17| 33| Total| 30| 18| 48| Table 6: Observed frequencies for the chi square test * Expected frequency: The expected frequencies are obtained by the formula: fe = (column total (row total) / total sum | Below y? | Above y? | Total| Below x| 9. 375| 5. 625| 15| Above x| 20. 625| 12. 375| 33| Total| 30| 18| 48| Table 7: Expected frequencies for the chi square test. We can now calculate the chi square variable: ?2? = ( f0-fe)2/fe = 8. 85 In order to decide whether we accept or not the alternative hypothesis H1, we need to find the number of degrees of freedom (df) and to fix a level of confidence .The number of degrees of freedom is: df= (number of rows – 1) (number of columns –1) = 1 The corresponding critical values of chi square, depending on the choice of level of confidence , are given in the following table (see reference [4]) df| 00. 10| 00. 05| 0. 025| 00. 01| 0. 005| 1| 2. 706| 3. 841| 5. 024| 6 . 635| 7. 879| Table 7: Critical values of chi square with one degree of freedom. Since the value of chi square is greater than any of the above critical values, we conclude that even with a level of confidence = 0. 005 we can accept the alternative hypothesis H1: GDP and Life Expectancy are related.The above test shows that there is some relation between the two variables x (GDP per capita) and y (Life Expectancy at birth). Our goal is to further investigate this relation. Section 6: Conclusions Interpretation of results Our study of the statistical relation between GDP per capita and Life Expectancy brings us to the following conclusions. As the chi square test shows there is definitely some statistical relation between the two variables (with a confidence level = 0. 005). The study of linear regression shows that there is a moderate positive linear correlation between the two variables, with a correlation coefficient r~ 0. . This linear model can be greatly improved replacing the linear dependence with a different type of relation. In particular we considered a logarithmic relation between the variable x (GDP) and y (Life Expectancy). With this new relation we get a correlation coefficient r~ 0. 7. In fact, if we remove the data related to the anomalous country of South Africa (which should be discussed separately and does not fit well in our statistical analysis), we get an even higher correlation coefficient r~ 0. . This is evidence of a strong positive logarithmic dependence between x and y. Validity and Areas of improvement Of course one possible improvement of this project would be to consider a much more extended collection data on which to do the statistical analysis. For example one could consider a large list countries, data related to different years (other than 2003), and one could even think of studying data referring to local regions within a single country.All this can be found in literature but we decided to restrict to the data presented in this project because we considered it enough as an application of the mathematical and statistical tools used in the project. A second, probably more interesting, possible improvement of the project would be to consider other economic factors that can affect the Life Expectancy at birth of a country. Indeed the GDP per capita is just a measure of the average wealth of a country and it does not take in account the distribution of the wealth.There are however several economic indices that measure the dispersion of wealth in the population and could be considered, together with the GDP per capita, as a factor influencing Life Expectancy. For example, it would be interesting to study a linear regression model in which the dependent variable y is the Life Expectancy and with two (or more) independent variables xi, one of which should be the GDP per capita and another could be for example the Gini Inequality Index reference (measuring the dispersion of wealth in a country).This would have been very interesting but, perhaps, it would have been out of context in a project studying GDP per capita and Life Expectancy. Probably the most important direction of improvement of the present project is related to the somewhat arbitrary choice of the logarithmic model used to describe the relation between GDP and Life Expectancy. Our choice of the function y= Aln(x+C) +B, was mainly dictated by the statistic package at our disposal in the excel software used in this project.Nevertheless we could have considered different, and probably more appropriate, choices of functional relations between the variables x and y. For example we could have considered a mixed linear and hyperbolic regression model of type y= A + Bx + C/(x+D), as it is sometimes considered in literature (see reference [4]). Bibliography: 1. Gapminder World. Web. 4 Jan. 2012. ;lt;http://www. gapminder. org;gt;. 2. â€Å"GDP – per Capita (PPP) vs. Infant Mortality Rate. Index Mundi – Country Facts. W eb. 4Jan. 2012. <http://www. indexmundi. com/g/correlation. aspx? v1=67>. 3. â€Å"Life Expectancy at Birth versus GDP per Capita (PPP). † Statistical Consultants Ltd. Web. 4 Jan. 2012. <http://www. statisticalconsultants. co. nz/ weeklyfeatures/WF6. html>. 4. â€Å"Table: Chi-Square Probabilities. † Faculty & Staff Webpages. Web. 4 Jan. 2012. <http://people. richland. edu/james/lecture/m170/tbl-chi. html>.

Solar Energy Research Paper Example | Topics and Well Written Essays - 500 words

Solar Energy - Research Paper Example Since then, harnessing and utilization of the solar energy has increased manifolds. Today, it is used as a renewable source of energy in homes and offices to make the buildings green and the business sustainable. A lot of research has conventionally gone into identification of ways to apply solar energy, and quite a lot of ways have been found in which solar energy can be produced and used in a cost effective way. A system consisting of a whole range of solar panels along with the storage batteries is conventionally employed as the most suitable setup for harnessing the energy derived from the sun. Light incident from the sun is collected by the solar panels which in turn, store the energy thus captured into the storage batteries. As the solar energy is transferred to the batteries, it is possible to also use it to operate different home appliances and machines. The excessive solar energy that is originally stored in the batteries can be later made use of right after the sunset or in the night as required. In homes that solely rely upon sun as a source of energy, people keep batteries that are good enough to maintain power supplies to facilitate the operation of such home appliances as ovens, stoves, fridges, tvs and other digital devices. The stored solar energy is also frequently used to regulate the temperature of the home by operating the temperature controlling devices like heaters and air conditioners. A system consisting of a whole range of solar panels along with the storage batteries is conventionally employed.

Malcolm Glazer's Acquisition Of Manchester United Case Study

Malcolm Glazer's Acquisition Of Manchester United - Case Study Example As we know that the finance department plays a vital role in every organization and ensures that the organization has enough resources and liquidity to meet its legal obligations as well as facilitate its shareholders. The primary goal of the finance manager is to ensure that his company has adequate supply of capital and sufficient statutory reserves. The ultimate goal of every organization is the same "to increase the surplus". But the question is; how the finance manager becomes the part of the success story and how they can maximize the value of their organization The financial manager or the chief financial officer (CFO) is responsible for financing the enterprise and acts as an intermediary between the financial system's institution and markets. While on the other hand, the business manager is responsible for a different kind of work like investing in plants and equipments, undertake research, hire staff and sell the firm's product. Major financial decisions made by the managers of a business are either investment decisions or financing decisions. In investment decisions, managers consider the amount invested in the assets of the business and the composition of that investment. Investment in assets are more beneficial because it produces cash flows for the entity that are needed to meet the operating expenses, pay interest to lenders and taxes to government. In addition to the amount and composition of investment, managers have to decide how to finance them; it pertains to the financing decision which involves generating funds internally or from sources external to the business. Dividend decisions also affect the financing decisions (Bossaerts, 2006). Successful companies have skilled people at all levels inside the company, including (1) leaders who develop and articulate sound strategic visions; (2) managers who make value-adding decisions, design efficient business processes, and train and motivate work forces and (3) a capable work force willing to implement the company's strategies and tactics. Before going

Artificial Intelligence in Health Care Delivery System Essay

Artificial Intelligence in Health Care Delivery System - Essay Example In the market-driven health system, consumers or people decide what goods (health module) to buy and at what cost. Therefore, the prices and the level of services become the crucial factor in the exchange of goods or the healthcare services. In the last few decades, United State has seen a marked shift in the healthcare which has moved from public driven policies to market funded paradigms. Healthcare is funded by private agencies and insurers and not by the government. Health insurance purchased by individuals and employers are the primary source of funding for healthcare delivery. The healthcare-related websites offer huge information about the healthcare services that may satisfy the needs of individuals, the family and the businesses. The websites of private insurance players promoting a wide range of healthcare products have become the main tools of market strategy. AIG is one of the world’s leading insurance and financial services with operations in more than 130 countri es across the globe (aig.com). The company offers a huge range of healthcare products in the insurance area for individuals and businesses. It facilitates options and premium calculations, including providing the people to customise products to suit their affordability, requirement and general welfare. Overall this link broadly gives the information so they are able to query in detail about the diseases and health plans with their service providers. Yes, using information technology in the insurance field greatly promotes artificial intelligence in healthcare. AI can be broadly defined as ‘the study of ideas which enable computers to do the things that make people seem intelligent ... The central goals of Artificial Intelligence are to make computers more useful.