Crime Problems Essay -- essays research papers

Crime is a serious issue that affects everyone in society. It affects the victims, perpetrators and their families. Crime has increased drastically within the last decade. More prisons are being built around the world because there is not enough room to hold inmates. The government has made an attempt to reduce crime by funding programs such as prevention and intervention for youth at risk , as well as rehabilitation for prisoners that will be released. Some argue that criminal behavior is due to environment, others believe that it is genetic, and yet others think that it has to do with personality. If there were certain personality traits that could be identified with potential criminal behavior, steps could be taken to try to reduce or diminish the â€Å"criminal personality†. Although personality is not the only factor in criminal behavior, there does seem to be a strong association between the both. Alfred Adler believed that children who failed to solve the vital problem of social interest-who lack cooperation and a desire for contributing to the well-being of others-will always meet significant problems later, during their adult years (Adler, 1998). This could include personality problems or criminal behavior. Personality develops early in life. That is why early childhood aggression and antisocial behavior should be taken seriously. Being able to identify potential criminal behavior is vital for prevention and intervention. Childhood factors shown to relate to the development of antisocial behaviors include a difficult early temperament, low IQ, academic deficiencies and learning problems, lack of empathy, underdeveloped social skills, and negative peer relations. (Sutton,Cowen, Crean, & Wyman, 1999). Environmental factors such as family structure and poverty are also associated with potential criminal behavior. The Federal Bureau of Investigation Report (1993) noted that one violent crime (e.g. aggravated assault, murder) was committed every 22 seconds in 1992, and 15% of those arrested for such crimes were under the age of 18 (Sutton, ete.al. 1999). Juvenile delinquency is becoming more common. The age at which these young kids are committing crimes is getting younger. The crimes they a re committing are getting more serious. They are not only involved in vandalism and shop lifting like many people might assume, but they are involved in life threat... .... Each child is unique and learns in different ways. Therefore, parents, teachers, and mentors must learn to reach children and youth. As members of society we must be aware of negative behavior and/or personality that could possibly lead to criminal behavior in the future. If we take responsibility for the youth of society as a whole, we will not only improve the life of that child, but we will improve the world we live in. The lack of connectedness that is portrayed by the delinquent youth can also be seen by the members of society. The attitude of, â€Å" That is not my kid, therefore that is not my problem† contributes to the criminal society that we live in. I believe that the prevention, intervention and rehabilitation programs are helpful, but I also think that parents have the power to prevent their child from engaging in such acts of crime. After all, a parent should know their child more than any other person in this world. Although, having an antisocial/aggressi ve personality does not necessarily guarantee that a child will become a criminal, I believe that taking the proper steps to insure the positive future for children is the best prevention method that a parent can use.

Pride and Prejudice: Hardships of Women in the 19th Century Essay

Women of the 19th century experienced severe hardships. Elizabeth Bennett’s experiences demonstrated the life of women who where pressured to conform to society’s expectations. After breaking down barriers women today have the freedom to express themselves, be educated and prosperous, and most important of all live without restrictions. â€Å"Pride and Prejudice,† written by Jane Austin, portrays the protagonist of the novel through the eyes of the author. Issues of marriage, class mobility, conforming, and restrictions to marrying, only account for some hardships faced by Elizabeth and other characters throughout the novel. Throughout the novel, characters where faced and forced to overcome obstacles of love. Views of marriage differed from character to character. Women where not educated and where forced to conform to the society’s expectations which kept them from being independent. Therefore, Elizabeth Bennet’s mother, Mrs. Bennet forced marriage upon her daughters. Elizabeth’s engagement to Darcy was criticized because many did not feel the couple was a good match for each other because he was a â€Å"proud† individual, and their economic differences and stature also prohibited the couple to be a good match according to society. Mrs. Bennet was happy when she heard about Darcy’s proposal to Elizabeth stating, â€Å"How rich and how great you will be!†Ã¢â‚¬ ¦ what pin- money, what jewels, what carriages you will have!† (Austen 325). Mrs. Bennet’s focus of life revolved around her daughters or at least one of her daughters marrying wealthy, so t hat not only that daughter will be cared for, but Mrs. Bennett and any unwed sisters will be provided for, as well. Mr. Bennett agreed that, â€Å"the business of her life was to get her daughters married; i... ... she did not love him, knowing that she would have been able to secure her fathers’ wealth. Elizabeth did not conform to her society’s expectations and refused to marry for any other reason than true love, sticking to what she believed in most although opposing the beliefs of her society. She placed a higher importance on interest, attraction, and love. Bibliography Austen, Jane. Pride and Prejudice,3rd ed. Norton, 2001. Williams, Michael. â€Å"Vision: Jane Austen Study Guide,† University of South Africa Pretoria, 2002. Menon, Sindhu. â€Å"The Literature Network.† Jalic Inc. 3 March. 2015. http://www.online-literature.com/austen/prideprejudice/ "Pride and Prejudice." Encyclopà ¦dia Britannica. Encyclopà ¦dia Britannica Online. Encyclopà ¦dia Britannica, 2011. Web. 22 Mar. 2015. .

Disease Trends and Healthcare Delivery Essay

Statistics make the world go round, literally. A certain population or ethnicity and their disease trends can really have an effect on what can happen in the future for our healthcare systems. Demographics and Disease trends can go hand in hand with one another because disease trends are so constant and unnoticeable that it continues daily, therefore having a particular group being affected by the same disease. Some people do not believe it, but all you have to do is look at the statistics and you will then see how greatly they affect one another. To break things down, demographics are groups of people with a common link, such as; age, gender, race, education level, income level, even marital level, and etc. When in an environment where links are similar or the same, the same trends start to get picked up as well, linking you within the range of your demographics. Environment plays a big role in demographics as well. If you live in the same neighborhood or community as someone, you a re linked to that person no matter how small of importance it is. With everyone doing the same trends which come to them like habits, they may never go away until they know that it is a problem. Environment happens to play a role as well when it comes to demographics. Environment happens to play one of the main roles. Habits become hard to break because of the type of environment you around. Who, what, and the things that you are surrounded by happen to make up part of your environment, and it becomes hard to control when you get used to it and things become normal to you. An example would be; you stop by McDonald’s every day to get a Sunday just as a snack for the day. If that McDonalds was not so close to you, you would probably not have picked up the habit to buy an ice-cream. When you have so much or an environment surrounded by you that comes natural or that you see as being natural, it does not seem like anything to you because you do it in your everyday life. These things can affect your health and can affect the future of healthcare if gone unnoticed for so long. Even people play a big role in your environment. An example would be say you had a significant other. He liked to eat out all the times, but you liked home cooked meals bec ause that is what you were used to, but one day you said you did not feel like cooking and you at out. The one day changed everything and now you eat out just as much as your significant other and have begun to gain weight. It has become a habit for you to eat  out every day even though it seems so easy to stop. Well, that takes us to our next topic, obesity rates have gone up significantly throughout the United States. Of all countries, the United States has the highest rate of obesity. United States obesity rates have gone up from 13 percent in 1962 and estimates have increased to 19.4 percent in 1997, 24.5 percent in 2004, 26.6 percent in 2007 and 33.8 percent for adults and 17 percent for children in 2008. 2010 reports from CDC were said that numbers were still increasing counting 35.7 percent of the total American population for adults were obese and 17 percent of American children. Factors affecting obesity would be none other than environment. The fact that the United States has so many fast food services; people cannot help but to get dragged into being obese. Food is always in their faces or else they see someone else eating and crave it as well. Obesity has accounted for 100,000 to 400,000 deaths in the United States per year. It has increased healthcare use and expenditures. Obesity rates has cost society about 117 billion dollars in direct and indirect costs. It has exceeded the health care cost and will continue if obesity rates continue to go up. Obesity rates happen to be ore that that of the smoking and drinking rate and accounts for 6 percent to 12 percent of national health care expenditures in the United States. To reduce obesity rates, people can try switching up their everyday environments by going to a gym, going to a track, or even by just going to the grocery store and buying more healthy foods to cook and it. If people were to exercise as much as they eat, obesity rates could be lowered. The fact that children see things and grow up eating things they shouldn’t grows with them. Obesity rates could change in the future if children were taught to eat more healthy foods and not just foods that just taste good. There are so many things that people can do to reduce their own obesity rate. They can exercise three times a week and also eat healthier. Overall living a healthier lifestyle would help greatly. Changing the people around you as well can help. Choose to be around people that care about their health a lot or care about their health just as much as you do. You could always have a workout buddy, someone that will encourage you to lose weight. Even the fact of knowing that you could possibly be obese leading to more and more diseases can trigger something in someone to make them want to become a bit healthier. In the future of health care in the United States, there is really no way of  telling whether obesity rates will go down, but they could possibly go down drastically if the government, communities, societies, schools and cities come together annually and create awareness and activities that encourage people to lose weight, stay fit, and become healthy. Age can affect the U.S health care systems just as much obesity can. There are more modern medicines to help with diseases that were in the past. Let’s face it; now that we have more modernized medicines and machinery, people are living longer than they were 200 and 300 years ago. Today 40 million people in the United States are ages 65 and older. The number is to more than double to 89 million by 2050. The United States has a smaller share of older person than many developed countries, and its populations are graying at a slower pace. Environment definitely plays a role. People are aging slower within the United States because of more and more technology being implemented within the health care system. At this rate everyone will live until they are 100. It is not a bad thing to live a long life, but it does get harder when you get older and it often puts a burden on health care systems as well. The reason being is because elderly are more likely to be in high demand of healthcare and, getting help is expensive. The aging trend is likely to increase due to environmental factors. The more and more people that get older, the more and more nursing homes, and assisted living facilities begin to be built in order to help support the elderly. Retirement homes, assisted living facilities, nursing homes, happen to play a huge role in the United States. These homes often need a lot of co-workers as well which is really needed and in demand. Things that could be done to reduce health complications related to aging would be; living a healthy lifestyle no matter how old you are, and possibly make health care a little less expensive. Avoiding disease is the number one way to avoid getting complications in life when aging. Possibly choose a health plan that implements preventive health care and always get monthly and yearly screenings that could be recommended by your doctors. Health care delivery will most likely adapt to the United States environment in the future to provide care for age- related health issues by implementing more preventative care, making sure that everyone has health care coverage or are able to get it. Doctors and nurses need to be good at their jobs as well in order to catch even the hardest complications. There needs to be more  hospitals willing to accept a broad range of health issues and of course the number one thing would be price reduction. Health care will become a bit better than what it is now especially with the HITECH act in which is trying to put good use of information technology within hospitals, and physicians’ offices. As long as technology is always improving, the healthcare system improves as well. Health care delivery for obesity adaptation may get worse before it gets any better in such that people will not realize their issues until it is too late. In doing so this will trigger a movement within the United States that would make people want to change and teach their children as well. Health classes will begin to be implemented into elementary schools very early to teach children what are good foods and what aren’t. Instead of having soda machines and vending machines, machines are just going to be light snack machines and juice. In conclusion, healthcare delivery will change no matter what the case is. People play a big role in the future of health care and vice versa. With that being said, the only way for better adaptations would most probably be people actually wanting to change and more research within the world. With research, there comes, new inventions, with new inventions, there comes better technology to have in order to play roles within our lives. In order for change to occur within the future of health care, there has to be a change within people. When people decide to get up and take preventative measures within their own lives, and then their children life will then be when they decide to help the future of health care. Reference Jacobsen, L. (2009). America’s Aging Population. Retrieved from http://www.prb.org/Publications/PopulationBulletins/2011/americas-aging-population.aspx (2013). Obesity in the United States. Retrieved from http://en.wikipedia.org/wiki/Obesity_in_the_United_States â€Å"Statistics Related to Overweight and Obesity†. CDC. 2006. Retrieved 2009-01-23 F as in Fat: How Obesity Policies are Failing in America, 2008, Trust For America’s Health, pp. 10–11. Note: Defines â€Å"overweight† as BMI ≠¥25, â€Å"obese† as

The Role of Classroom Interaction in a Primary Level Classroom

The function of schoolroom interaction in a primary degree schoolroom has been the centre of focal point in many surveies conducted in this field. Most of the surveies were carried out by western research workers in which the schoolroom interaction was critically studied to happen its impact on the future mentality / public presentation of a pupil. These surveies cover assorted spheres of schoolroom interaction covering teacher-student and peer-peer interaction. Since we have a curious socio-economic apparatus in Pakistan which differs from the western civilizations, there is null that needs to be filled up by analyzing a schoolroom interaction in the model of the predominating teaching civilization of our state. Furthermore, in our civilization learning profession has ne'er been of premier importance instead professions like technology, medical or being a pilot in PAF is more preferable. This has resulted in encephalon drain in the instruction profession. Young alumnuss who do non happen any occupation, bend to the learning profession as a last resort. Furthermore, in the socio-economic apparatus of our state instruction is a low-income profession, which force instructors to merely go through the category room clip learning nil, and have their ain tuition centres to learn at eventides. Additionally the pupils from the less fortunate backgrounds find it hard to prosecute in peer-peer interaction or have violent attitudes in class-room interaction which, at times, proves counter-productive. This survey aims to research kineticss of category room interaction in a primary degree category room and outlines a scheme to work out this job.Statement of ProblemTo analyze category room interaction in a primary category room environment.Scope of StudyThis survey will place the cardinal factors that contribute to category room interaction in a primary degree category room. It will besides individual out hindrances being faced by schools in Pakistan to heighten category room interaction. This survey may be used as a helpful usher in explicating a composite policy towards heightening category room interaction in the school systems in Pakistan.Significance of this SurveyAfter independency in 1947, no serious idea was of all time given to construction our educational system on a solid and balance, exhaustively worked out termss that should bring forth a regiment of good groomed, socially confident and focussed work Equus caballuss to turn to the intensifying issues of a freshly born p rovince. Quiet opposed to that a fire combat policy, non wholly sing the socio-religious and the socio-economic background of the people, was the chief drive force behind preparation of our instruction policy. This resulted in state of affairs where the intelligent and bright pupils from a university would stop up using for occupations other so the instruction professions and finally the rejected batch found a topographic point in our schooling system as instructors where they would vent off their egocentric and societal want on their pupils. This survey would try to bring out the fact that deficiency of category room interaction is because of inept and incapable instructors. Furthermore, if any efforts to relieve the category room interaction are done, so the deficiency of vision by such instructors proves to be the chief hindrance. This survey would be a valuable service to the society and the state and will function the intent to be a guide line in explicating a composite instruction policy to eliminate the defects of the present twenty-four hours primary school system. This instruction policy can be implemented for both the populace every bit good as private sector schools.Literature ReviewBoth Kenneth and Bruffee ( 1984 ) have observed that the degree of pupils ‘ engagement in schoolroom interaction is straight relative to the grade of instructor ‘s engagement. They have farther outlined that the most direct manner to make category room interaction is that the instructor follow the rules of collaborative acquisition in which the instructor prepares a acquisition job or undertaking and so delegate little groups of pupils to work out the job collaboratively. Teachers ‘ engagement in schoolroom interaction was besides advocated by Hill ( 1969 ) who opines that the instructor ‘s function is most of import in planing the undertaking. Once groups have begun work, the instructor should make no more than unobtrusively supervise the procedure because the group needs to decide the procedure themselves. Prediction of human behavior under a peculiar set of fortunes have been studied by assorted research workers which conclude that human head tend to larn fast when acquisition is conducted in groups. Meyers ( 1986 ) besides pointed out that larning is fast provided the collaborative undertakings are decently designed.|3|Research QuestionsThe survey shall take to the reply the followers: – ( a ) Is the category room interaction in our schooling system degrading or is reforming? ( B ) How strongly the instructor ‘ behavior towards heightening the category room interaction at primary degree category room affects the overall response of the pupils. ( degree Celsius ) How long will it take to reform the category room interaction job in our schooling system?HypothesisThe survey is based on the undermentioned hypothesis: – â€Å" Incapable instructors are the chief ground why category room interaction is non prevailing in our primary category room environment. †Definition of Key Words/ FootingsIn this survey, following nomenclatures are used and explained below. ( a ) Peer-peer Interaction: This term signifies all interactions that occur between the participants of common age groups i.e. pupils of the same category in a school. ( B ) Socioeconomic Index. Socioeconomic index of a instructor is a composite appraisal of his instruction and wealth whereas for a pupil it is based on the business, instruction and wealth of his parents. ( degree Celsius ) Collaborative Learning: Collaborative acquisition means planing larning undertaking or job by the instructor which is so handed over to the pupils to work out collaboratively. ( vitamin D ) Analysis of variance: A Analysis of Variance is a aggregation ofA statistical theoretical accounts and associated processs in which the observedA varianceA in a peculiar variable is classified into constituents which can be attributed to assorted beginnings of fluctuation.Research MethodologyA intercrossed theoretical account consisting of both the qualitative and quantitative research methodological analysiss will be employed for informations acquisition for this survey because it tend to execute better in set abouting any research. For this intent, questionnaires and interviews of the pupils and instructors will be the chief beginning of informations assemblage. Population and Sampling. The population for this research will be the pupils and instructors of the selected schools. This survey will include pupils and instructors of primary degree, both from private schools every bit good as public sector schools. From private sector Beacon House and City School have been chosen and from the populace sector, F.G. School and Army Public School have been chosen. All of these schools are situated in Karachi, Hyderabad and Nawab Shah country. A sum of 30 schools dwelling of 20 private and 10 public sector owned schools have been chosen. Sample size will consists of 15 pupils and 20 instructors from public sector school whereas 10 pupils and 15 instructors will organize up the sample size from the private sector schools. The entire sample size will be 950 that will include 550 instructors and 400 pupils in all. Research Instruments. As outlined earlier, this research will follow a loanblend theoretical account which is a combination of both the qualitative and quantitative research methodological analysiss. Following the traditions of qualitative methodological analysis, in the flesh interviews with both the pupils and instructors will be conducted. The instructors will be asked the grounds for worsening category room interaction and what are the salary construction that is being followed in prevalent schools. The pupils will be asked to sketch the grade of interest/ counsel provided by a certain instructor. To cover up the quantitative sphere, questionnaires will be used for informations assemblage. The questionnaire will be given to both instructors and pupils. The instructors will foreground their income inside informations, the figure of household member they have to back up, their matrimonial position, the educational makings they have, did they follow the instruction profession by wil l or by irresistible impulse. The pupil will be subjected to reply inquiries such as the educational degree of their parents and the income or household wealth. Two separate questionnaires will be prepared ; one for the instructors and one for the pupils. The questionnaire for instructors should be prepared in such a manner as non to pique them, and for the pupils it should be prepared harmonizing to the comprehension degree of the pupils. In both instances it should be a combination of multiple pick inquiries ( MCQs ) and make full in the clean type of questionnaire. The interviews required will be 15-20 proceedingss long for the pupils and 20-25 proceedingss long for the instructors.Plan of Data Analysis18. ROOTS and SPSSA ( Statistical Package for the Social Sciences ) package bundles will be used for the information analysis. These package bundles provide Descriptive statistics, Bivariate statistics, anticipation for results and anticipation for placing groups which helps in effectual informations analysis. Statistical theoretical accounts like ANOVA ( Analysis of Variance ) will be used to analyse lending factors across the instructors and pupils groups belonging to different socio-economic groups. Recursive abstraction and mechanical techniques will be employed for the qualitative analysis. Bar charts, Pie charts and Histograms will be used for statistical comparing of informations.Validity and Reliability19. Interviewer documentation and equal debriefing will be used as a method of set uping the cogency of qualitative analysis whereas the questionnaires distributed among the selected schools shall organize up the quantitative analysis.Ethical Considerations20. The research will be carried out in such a mode that confidentiality of all of the information is purely ensured. This will be done by nearing the direction of the selected schools. They will be taken into assurance on the purpose and the principle behind the research. The participants of the survey will be provided full confidentiality by maintaining their names confidential. Furthermore, entree to the information of this survey will be provided to the pa rticipants of the survey merely.Summary of the Proposal21. The intent of this research is to analyze the kineticss of category room interaction in a primary degree category room. The survey was based on the premise that category room interaction in a primary degree category room is affected by multiple factors. These factors include socio-economic standing of the instructors, the ability/competency degree of the instructors and the socio-economic standing of the pupils. Among these the first two are the premier factors in finding the overall degree of the category room interaction. The research theoretical account employed for informations assemblage and analysis is a mixture of both the quantitative and qualitative methods. For the intent of this survey, the population will be defined as all the instructors and pupils of the selected schools of primary degree educational section of Pakistan enrolled. Sample size will be 900 that shall include 500 instructors and 400 pupils from 30 different private and public schools.Time Activity Chart22. Research shall be conducted for continuance of six months, with consequence from 01 October, 2011 boulder clay 01 April, 2012. Timelines for assorted activities of the research are as follows: –Datas AnalysisUndertakingTable 1: Time Activity ChartFinalization of participants30Time line ( Weeks )Result FinalizationReading trials, questionnaire readying272421183691215

The Religion Islam

| The Religion Islam| The Religion Islam What is Islam? The word Islam means submission to the will of God. The religion of Islam is the acceptance of and obedience to the teachings of God which the Muslims—followers of Islam—believe God revealed to his last prophet. Muslims believe that there is only one God. The Arabic word for God is Allah which means, the one and only true God who created the whole universe. According to Muslims, God sent a number of prophets to mankind to teach them how to live according to His law. To the Muslims, Jesus, Moses and Abraham are respected as prophets of God.Muslims believed in the prophets as messengers of God, but according to their beliefs, God’s final message to man was revealed by the prophet Muhammad. Who is the prophet Muhammad? Muhammad was born in Mecca in the year 570. His father died before he was born and his mother died shortly after. Therefore he was raised by his uncle. Muhammad was raised illiterate. He could no t read or write, and remained that way for the rest of his life. As he grew up, he was known to be the truthful, honest, trustworthy, generous, and sincere. Muhammad was very religious, and had long disliked the decadence and idolatry of his society.Muhammad was claimed to receive his first revelation from God through the Angel Gabriel when he was at the age forty. The revelations continued for twenty-three years, and they are known as the Quran. When Muhammad started preaching the truth which God revealed to him, he and his group of followers suffered persecutions from the non-believers. It got so bad for Muhammad and his followers that in the year 622, God gave them the command to emigrate. They migrated from the city Mecca to the city of Yathrib, which is now called Medina. His journey to Yathrib is called Hijra. This marked the beginning of the Muslims calendar.Several years later, Muhammad and his followers returned back to Mecca, where they forgave their enemies. The greater p art of the Arabian Peninsula had become Muslims and within the century of his death, Islam had spread all over the world. Muhammad died at the age sixty-three. Though he was a man, he was far removed from evil appearances and tried only for the sake of God and his reward. Muslims believe that Muhammad was the last prophet of God. They believed that the Holy Quran is God’s last revealed book. The prophet Muhammad claimed that the angel, Gabriel revealed the Quran, which the Muslims call God’s literal word, to him.Muhammad memorized the prophecy and shared it with his companions, and they then wrote it down in a book called the Quran. Muslims believe that the angel Gabriel met with Muhammad once a year to review the Quran and during the last years of his life, he met with Gabriel twice a year. The Quran was said to be revealed fourteen centuries ago. The Quran is the primary source of every Muslim’s faith and practice. This book deals with all the subjects which c oncern human beings: wisdom, doctrine, worship, transactions, law, and more, but its basic theme is the relationship between God and his creatures.This book is known to provide guidance and detailed teaching for society. The Quran was claimed to be revealed to Muhammad in Arabic. What are the Muslims beliefs? Muslims have six main beliefs. The first belief is to believe in God. Muslims believe in one, unique, incomparable God, Who has no son or partner, and that none has the right to be worshipped but Him alone. The second belief is to believe in the Angels. Muslims believe in the existence of the angels and that they are honored creatures. The angels worship God alone, obey Him, and act only by His command.The third belief of the Muslims is to believe in God’s revealed books. Muslims believe that God revealed books to His messengers as proof for mankind and as guidance for them. Among these books is the Quran, which God revealed to the Prophet Muhammad. The fourth belief is that the Muslims should believe in the Prophets and the messengers of God. Muslims believe in the prophets and messengers of God, starting with Adam, including Noah, Abraham, Ishmael, Isaac, Jacob, Moses, and Jesus, but God’s final message to man, a reconfirmation of the eternal message, was revealed to the Prophet Muhammad.The fifth belief of the Muslims is to believe in the Day of Judgment. Muslims believe in the Day of Judgment which is the day of resurrection, when all people will be resurrected for God’s judgment according to their beliefs and deeds. The last belief of the Muslims is to believe in Al-Qadar. Muslims believe in Al-Qadar, which is divine predestination, but this belief in divine predestination does not mean that human beings do not have free will. Rather, Muslims believe that God has given human beings free will.This means that they can choose right or wrong and that they are responsible for their choices. The belief in Divine Predestination includes belief in four things: God knows everything. He knows what has happened and what will happen, God has recorded all that has happened and all that will happen, whatever God wills to happen happens, and whatever He wills not to happen does not happen and God is the creator of everything. Muslims believe the Sunnah is the practical example of the Prophet Muhammad and that there are five basic pillars of Islam.The Five Pillars of Islam are the five obligations that every Muslim must fulfill in order to live a good and responsible life according to Islam. These pillars are the confession of faith, praying five times a day, giving alms to the poor, fasting during the month of Ramadan, the pilgrimage to Mecca once in a lifetime for those who are able. The confession of faith must be said with conviction, â€Å"La ilaha illa Allah, Muhammadur rasoolu Allah. † This means, â€Å"There is no true god but God (Allah) and Muhammad is the messenger of God. The testimony of faith is calle d the Shahada, a simple method which should be said with conviction in order to convert to Islam. This is the most important pillar of Islam. Prayer is the second pillar of Islam which they call it the Salat. Muslims perform five prayers a day. Prayer in Islam is a direct link between the worshipper and God. They are performed at dawn, noon, mid-afternoon, sunset, and night and are performed anywhere they please. Before performing a prayer, one must be in a state of purification.That means they wash their hands all the way up to their elbows, the mouth and the nostrils are rinsed, and he feet are bathe to the ankles. The third pillar of Islam is almsgiving. The Muslims call it giving Zakat. The meaning of the word Zakat means both purification and growth. To give Zarat means giving a specified percentage of certain properties to certain classes of needy people. The fourth pillar of Islam is fasting the month of Ramadan known by Muslims as Sawm. Every year in the month of Ramadan, Mu slims spend he ninth month of the Islamic calendar observing a community-wide fast from dawn until sundown, abstaining from food, drinks, and sexual relations. Fasting is a method of spiritual self-purification done by cutting oneself off from the worldly comforts. A person fasting gains true sympathy with those who go hungry, as well as growth in his or her spiritual life. The last pillar of Islam is to pilgrimage to Mecca which the Muslims call it the Hajj. It occurs in the month of Dhul-Hijjah which is the twelfth month of the Islamic lunar calendar.About two million Muslims of every ethnic group, color, social status, and culture gather together in Mecca and stand before the Kabah praising Allah together. This is a ritual that is designed to promote the bonds of Islamic brotherhood and sisterhood by showing that everyone is equal in the eyes of Allah. The Hajjis or pilgrims wear simple white clothes called Ihram. They pray at the Haram mosque in Mecca. In the mosque is the Kabah which they turn to while praying. According to the Muslims, the Kabah is the place of worship which God commanded the Prophets Abraham and his son, Ishmael, to build.This is where they asked for forgiveness and for what they wish for. Carrying out the Five Pillars demonstrates that the Muslim is putting their faith first, and not just trying to fit it in around their secular lives. What are the families of Islam like? One of the most striking features of Muslim society is the importance attached to the family. The family unit is regarded as the cornerstone of a healthy and balanced society. A harmonious social order is created by the existence of extended families; children are treasured and rarely leave home until the time they marry.According to the Quran, men and women are equal before God; women are not blamed for violating the â€Å"forbidden tree,† nor will their suffering in pregnancy and childbirth a punishment for that act. How women are seen in Islam? Islam sees a woman, whether single or married, as an individual in her own right, with the right to own and dispose of her property and earnings. A marital gift is given by the groom to the bride for her own personal use, and she may keep her own family name rather than adopting her husband's.Roles of men and women are complementary and collaborative. Rights and responsibilities of both sexes are equitable and balanced in their totality. How do Muslims treat the elderly? The strain of caring for one’s parents in this most difficult time of their lives is considered an honor and a blessing and an opportunity for great spiritual growth. In Islam, it is not enough that we only pray for our parents, but we should act with limitless compassion, remembering that when we were helpless children, they preferred us to themselves. Mothers are particularly honored.When Muslim parents reach old age, they are treated mercifully, with kindness and selflessness. In Islam, serving one’s parents is a duty second of prayer, and it is their right to expect it. It is considered despicable to express any irritation when, through no fault of their own, the old become difficult. Today, Islam is the second largest religion in the world with over one billion followers. According to Muslims, Islam is not a new religion, but it is the same truth that God revealed through all His prophets. For a fifth of the world's population, Islam is both a religion and a complete way of life.

Econometrics Chapter Summaries Essay

2) Basic Ideas of Linear Regression: The Two-Variable Model In this chapter we introduced some fundamental ideas of regression analysis. Starting with the key concept of the population regression function (PRF), we developed the concept of linear PRF. This book is primarily concerned with linear PRFs, that is, regressions that are linear in the parameters regardless of whether or not they are linear in the variables. We then introduced the idea of the stochastic PRF and discussed in detail the nature and role of the stochastic error term u. PRF is, of course, a theoretical or idealized construct because, in practice, all we have is a sample(s) from some population. This necessitated the discussion of the sample regression function (SRF). We then considered the question of how we actually go about obtaining the SRF. Here we discussed the popular method of ordinary least squares (OLS) and presented the appropriate formulas to estimate the parameters of the PRF. We illustrated the OLS method with a fully worked-out numerical example as well as with several practical examples. Our next task is to find out how good the SRF obtained by OLS is as an estimator of the true PRF. We undertake this important task in Chapter 3. 3) The Two-Variable Model: Hypothesis Testing In Chapter 2 we showed how to estimate the parameters of the two-variable linear regression model. In this chapter we showed how the estimated model can be used for the purpose of drawing inferences about the true population regression model. Although the two-variable model is the simplest possible linear regression model, the ideas introduced in these two chapters are the foundation of the more involved multiple regression models that we will discuss in ensuing chapters. As we will see, in many ways the multiple regression model is a straightforward extension of the two-variable model. 4) Multiple Regression: Estimation and Hypothesis Testing In this chapter we considered the simplest of the multiple regression models, namely, the three-variable linear regression model—one dependent variable and two explanatory variables. Although in many ways a straightforward extension of the two-variable linear regression model, the three-variable model introduced several new concepts, such as partial regression coefficients, adjusted and unadjusted multiple coefficient of determination,  and multicollinearity. Insofar as estimation of the parameters of the multiple regression coefficients is concerned, we still worked within the framework of the classical linear regression model and used the method of ordinary least squares (OLS). The OLS estimators of multiple regression, like the two-variable model, possess several desirable statistical properties summed up in the Gauss-Markov property of best linear unbiased estimators (BLUE). With the assumption that the disturbance term follows the normal distribution with zero mean and constant variance ÏÆ'2, we saw that, as in the two-variable case, each estimated coefficient in the multiple regression follows the normal distribution with a mean equal to the true population value and the variances given by the formulas developed in the text. Unfortunately, in practice, ÏÆ'2 is not known and has to be estimated. The OLS estimator of this unknown variance is . But if we replace ÏÆ'2 by , then, as in the two-variable case, each estimated coefficient of the multiple regression follows the t distribution, not the normal distribution. The knowledge that each multiple regression coefficient follows the t distribution with d.f. equal to (n – k), where k is the number of parameters estimated (including the intercept), means we can use the t distribution to test statistical hypotheses about each multiple regression coefficient individually. This can be done on the basis of either the t test of significance or the confidence interval based on the t distribution. In this respect, the multiple regression model does not differ much from the two-variable model, except that proper allowance must be made for the d.f., which now depend on the number of parameters estimated. However, when testing the hypothesis that all partial slope coefficients are simultaneously equal to zero, the individual t testing referred to earlier is of no help. Here we should use the analysis of variance (ANOVA) technique and the attendant F test. Incidentally, testing that all partial slope coefficients are simultaneously equal to zero is the same as testing that the multiple coefficient of determination R2 is equal to zero. Therefore, the F test can also be used to test this latter but equivalent hypothesis. We also discussed the question of when to add a variable or a group of variables to a model, using either the t test or the F test. In this context we also discussed the method of restricted least squares. 5) Functional Forms of Regression Models In this chapter we considered models that are linear in parameters, or that can be rendered as such with suitable transformation, but that are not necessarily linear in variables. There are a variety of such models, each having special applications. We considered five major types of nonlinear-in-variable but linear-in-parameter models, namely: 1.The log-linear model, in which both the dependent variable and the explanatory variable are in logarithmic form. 2.The log-lin or growth model, in which the dependent variable is logarithmic but the independent variable is linear. 3.The lin-log model, in which the dependent variable is linear but the independent variable is logarithmic. 4.The reciprocal model, in which the dependent variable is linear but the independent variable is not. 5.The polynominal model, in which the independent variable enters with various powers. Of course, there is nothing that prevents us from combining the features of one or more of these models. Thus, we can have a multiple regression model in which the dependent variable is in log form and some of the X variables are also in log form, but some are in linear form. We studied the properties of these various models in terms of their relevance in applied research, their slope coefficients, and their elasticity coefficients. We also showed with several examples the situations in which the various models could be used. Needless to say, we will come across several more examples in the remainder of the text. In this chapter we also considered the regression-through-the-origin model and discussed some of its features. It cannot be overemphasized that in choosing among the competing models, the overriding objective should be the economic relevance of the various models and not merely the summary statistics, such as R2. Model building requires a proper balance of theory, availability of the appropriate data, a good understanding of the statistical properties of the various models, and the elusive quality that is called practical judgment. Since the theory underlying a topic of interest is never perfect, there is no such thing as a perfect model. What we hope for is a reasonably good model that will balance all these criteria. Whatever model is chosen in practice, we have to pay careful attention to the units in which the dependent and independent variables are expressed, for the interpretation of regression coefficients may hinge upon units of  measurement. 6) Dummy Variable Regression Models In this chapter we showed how qualitative, or dummy, variables taking values of 1 and 0 can be introduced into regression models alongside quantitative variables. As the various examples in the chapter showed, the dummy variables are essentially a data-classifying device in that they divide a sample into various subgroups based on qualities or attributes (sex, marital status, race, religion, etc.) and implicitly run individual regressions for each subgroup. Now if there are differences in the responses of the dependent variable to the variation in the quantitative variables in the various subgroups, they will be reflected in the differences in the intercepts or slope coefficients of the various subgroups, or both. Although it is a versatile tool, the dummy variable technique has to be handled carefully. First, if the regression model contains a constant term (as most models usually do), the number of dummy variables must be one less than the number of classifications of each qualitat ive variable. Second, the coefficient attached to the dummy variables must always be interpreted in relation to the control, or benchmark, group—the group that gets the value of zero. Finally, if a model has several qualitative variables with several classes, introduction of dummy variables can consume a large number of degrees of freedom (d.f.). Therefore, we should weigh the number of dummy variables to be introduced into the model against the total number of observations in the sample. In this chapter we also discussed the possibility of committing a specification error, that is, of fitting the wrong model to the data. If intercepts as well as slopes are expected to differ among groups, we should build a model that incorporates both the differential intercept and slope dummies. In this case a model that introduces only the differential intercepts is likely to lead to a specification error. Of course, it is not always easy a priori to find out which is the true model. Thus, some amount of experimentation is required in a concrete study, especially in situations where theory does not provide much guidance. The topic of specification error is discussed further in Chapter 7. In this chapter we also briefly discussed the linear probability model (LPM) in which the dependent variable is itself binary. Although LPM  can be estimated by ordinary least square (OLS), there are several problems with a routine application of OLS. Some of the problems can be resolved easily and some cannot. Therefore, alternative estimating procedures are needed. We mentioned two such alternatives, the logit and probit models, but we did not discuss them in view of the somewhat advanced nature of these models (but see Chapter 12). 7) Model Selection: Criteria and Tests The major points discussed in this chapter can be summarized as follows: 1.The classical linear regression model assumes that the model used in empirical analysis is â€Å"correctly specified.† 2.The term correct specification of a model can mean several things, including: a.No theoretically relevant variable has been excluded from the model. b.No unnecessary or irrelevant variables are included in the model. c.The functional form of the model is correct. d.There are no errors of measurement. 3.If a theoretically relevant variable(s) has been excluded from the model, the coefficients of the variables retained in the model are generally biased as well as inconsistent, and the error variance and the standard errors of the OLS estimators are biased. As a result, the conventional t and F tests remain of questionable value. 4.Similar consequences ensue if we use the wrong functional form. 5.The consequences of including irrelevant variables(s) in the model are less serious in that estimated coefficients still remain unbiased and consistent, the error variance and standard errors of the estimators are correctly estimated, and the conventional hypothesis-testing procedure is still valid. The major penalty we pay is that estimated standard errors tend to be relatively large, which means parameters of the model are estimated rather imprecisely. As a result, confidence intervals tend to be somewhat wider. 6.In view of the potential seriousness of specification errors, in this chapter we considered several diagnostic tools to help us find out if we have the specification error problem in any concrete situation. These tools include a graphical examination of the residuals and more formal tests, such as MWD and RESET. Since the search for a theoretically correct model can be exasperating, in  this chapter we considered several practical criteria that we should keep in mind in this search, such as (1) parsimony, (2) identifiability, (3) goodness of fit, (4) theoretical consistency, and (5) predictive power. As Granger notes, â€Å"In the ultimate analysis, model building is probably both an art and a science. A sound knowledge of theoretical econometrics and the availability of an efficient computer program are not enough to ensure success.† 8) Multicollinearity: What Happens If Explanatory Variables are Correlated? An important assumption of the classical linear regression model is that there is no exact linear relationship(s), or multicollinearity, among explanatory variables. Although cases of exact multicollinearity are rare in practice, situations of near exact or high multicollinearity occur frequently. In practice, therefore, the term multicollinearity refers to situations where two or more variables can be highly linearly related. The consequences of multicollinearity are as follows. In cases of perfect multicollinearity we cannot estimate the individual regression coefficients or their standard errors. In cases of high multicollinearity individual regression coefficients can be estimated and the OLS estimators retain their BLUE property. But the standard errors of one or more coefficients tend to be large in relation to their coefficient values, thereby reducing t values. As a result, based on estimated t values, we can say that the coefficient with the low t value is not statistically different from zero. In other words, we cannot assess the marginal or individual contribution of the variable whose t value is low. Recall that in a multiple regression the slope coefficient of an X variable is the partial regression coefficient, which measures the (marginal or individual) effect of that variable on the dependent variable, holding all other Xvariables constant. However, if the objective of study is to estimate a group of coefficients fairly accurately, this can be done so long as collinearity is not perfect. In this chapter we considered several methods of detecting multicollinearity, pointing out their pros and cons. We also discussed the various remedies that have been proposed to solve the problem of multicollinearity and noted their strengths and weaknesses. Since multicollinearity is a feature of a given sample, we cannot foretell which method of detecting multicollinearity or which  remedial measure will work in any given concrete situation. 9) Heteroscedasticity: What Happens If the Error Variance Is Nonconstant? A critical assumption of the classical linear regression model is that the disturbances ui all have the same (i.e., homoscedastic) variance. If this assumption is not satisfied, we have heteroscedasticity. Heteroscedasticity does not destroy the unbiasedness property of OLS estimators, but these estimators are no longer efficient. In other words, OLS estimators are no longer BLUE. If heteroscedastic variances ÏÆ'i2 are known, then the method of weighted least squares (WLS) provides BLUE estimators. Despite heteroscedasticity, if we continue to use the usual OLS method not only to estimate the parameters (which remain unbiased) but also to establish confidence intervals and test hypotheses, we are likely to draw misleading conclusions, as in the NYSE Example 9.8. This is because estimated standard errors are likely to be biased and therefore the resulting t ratios are likely to be biased, too. Thus, it is important to find out whether we are faced with the heteroscedasticity problem in a specific application. There are several diagnostic tests of heteroscedasticity, such as plotting the estimated residuals against one or more of the explanatory variables, the Park test, the Glejser test, or the rank correlation test (See Problem 9.13). If one or more diagnostic tests reveal that we have the heteroscedasticity problem, remedial measures are called for. If the true error variance ÏÆ'i2 is known, we can use the method of WLS to obtain BLUE estimators. Unfortunately, knowledge about the true error variance is rarely available in practice. As a result, we are forced to make some plausible assumptions about the nature of heteroscedasticity and to transform our data so that in the transformed model the error term is homoscedastic. We then apply OLS to the transformed data, which amounts to using WLS. Of course, some skill and experience are required to obtain the appropriate transformations. But without such a transformation, the problem of heteroscedasticity is insoluble in practice. However, if the sample size is reasonably large, we can use White’s procedure to obtain heteroscedasticity-corrected standard errors. 10) Autocorrelation: What Happens If Error Terms Are Correlated? The major  points of this chapter are as follows: 1.In the presence of autocorrelation OLS estimators, although unbiased, are not efficient. In short, they are not BLUE. 2.Assuming the Markov first-order autoregressive, the AR(1), scheme, we pointed out that the conventionally computed variances and standard errors of OLS estimators can be seriously biased. 3.As a result, standard t and F tests of significance can be seriously misleading. 4.Therefore, it is important to know whether there is autocorrelation in any given case. We considered three methods of detecting autocorrelation: a.graphical plotting of the residuals b.the runs test c.the Durbin-Watson d test 5.If autocorrelation is found, we suggest that it be corrected by appropriately transforming the model so that in the transformed model there is no autocorrelation. We illustrated the actual mechanics with several examples. 11) Simultaneous Equation Models In contrast to the single equation models discussed in the preceding chapters, in simultaneous equation regression models what is a dependent (endogenous) variable in one equation appears as an explanatory variable in another equation. Thus, there is a feedback relationship between the variables. This feedback creates the simultaneity problem,rendering OLS inappropriate to estimate the parameters of each equation individually. This is because the endogenous variable that appears as an explanatory variable in another equation may be correlated with the stochastic error term of that equation. This violates one of the critical assumptions of OLS that the explanatory variable be either fixed, or nonrandom, or if random, that it be uncorrelated with the error term. Because of this, if we use OLS, the estimates we obtain will be biased as well as inconsistent. Besides the simultaneity problem, a simultaneous equation model may have an identification problem. An identification problem means we cannot uniquely estimate the values of the parameters of an equation. Therefore, before we estimate a simultaneous equation model, we must find out if an equation in  such a model is identified. One cumbersome method of finding out whether an equation is identified is to obtain the reduced form equations of the model. A reduced form equation expresses a dependent (or endogenous) variable solely as a function of exogenous, or predetermined, variables, that is, variables whose values are determined outside the model. If there is a one-to-one correspondence between the reduced form coefficients and the coefficients of the original equation, then the original equation is identified. A shortcut to determining identification is via the order condition of identification. The order condition counts the number of equations in the model and the number of variables in the model (both endogenous and exogenous). Then, based on whether some variables are excluded from an equation but included in other equations of the model, the order condition decides whether an equation in the model is underidentified, exactly identified, or overidentified. An equation in a model is underidentified if we cannot estimate the values of the parameters of that equation. If we can obtain unique values of parameters of an equation, that equation is said to be exactly identified. If, on the other hand, the estimates of one or more parameters of an equation are not unique in the sense that there is more than one value of some parameters, that equation is said to be overidentified. If an equation is underidentified, it is a dead-end case. There is not much we can do, short of changing the specification of the model (i.e., developing another model). If an equation is exactly identified, we can estimate it by the method of indirect least squares (ILS). ILS is a two-step procedure. In step 1, we apply OLS to the reduced form equations of the model, and then we retrieve the original structural coefficients from the reduced form coefficients. ILS estimators are consistent; that is, as the sample size increases indefinitely, the estimators converge to their true values. The parameters of the overidentified equation can be estimated by the method of two-stage least squares (2SLS). The basic idea behind 2SLS is to replace the explanatory variable that is correlated with the error term of the equation in which that variable appears by a variable that is not so correlated. Such a variable is called a proxy, or instrumental, variable.2SLS estimators, like the ILS estimators, are consistent estimators. 12) Selected Topics in Single Equation Regression Models In this chapter we discussed several topics of considerable practical importance. The first topic we discussed was dynamic modeling, in which time or lag explicitly enters into the analysis. In such models the current value of the dependent variable depends upon one or more lagged values of the explanatory variable(s). This dependence can be due to psychological, technological, or institutional reasons. These models are generally known as distributed lag models. Although the inclusion of one or more lagged terms of an explanatory variable does not violate any of the standard CLRM assumptions, the estimation of such models by the usual OLS method is generally not recommended because of the problem of multicollinearity and the fact that every additional coefficient estimated means a loss of degrees of freedom. Therefore, such models are usually estimated by imposing some restrictions on the parameters of the models (e.g., the values of the various lagged coefficients decline from the f irst coefficient onward). This is the approach adopted by the Koyck, the adaptive expectations, and the partial, or stock, adjustment models. A unique feature of all these models is that they replace all lagged values of the explanatory variable by a single lagged value of the dependent variable. Because of the presence of the lagged value of the dependent variable among explanatory variables, the resulting model is called an autoregressive model. Although autoregressive models achieve economy in the estimation of distributed lag coefficients, they are not free from statistical problems. In particular, we have to guard against the possibility of autocorrelation in the error term because in the presence of autocorrelation and the lagged dependent variable as an explanatory variable, the OLS estimators are biased as well as inconsistent. In discussing the dynamic models, we pointed out how they help us to assess the short- and long-run impact of an explanatory variable on the dependent variable. The next topic we discussed related to the phenomenon of spurious, or nonsense, regression. Spurious regression arises when we regress a nonstationary random variable on one or more nonstationary random variables. A time series is said to be (weakly) stationary, if its mean, variance, and covariances at various lags are not time dependent. To find out whether a time series is stationary, we can use the unit root test. If the unit root test (or other tests) shows that the time series of interest is stationary,  then the regression based on such time series may not be spurious. We also introduced the concept of cointegration. Two or more time series are said to be cointegrated if there is a stable, long-term relationship between the two even though individually each may be nonstationary. If this is the case, regression involving such time series may not be spurious. Next we introduced the random walk model, with or without drift. Several financial time series are found to follow a random walk; that is, they are nonstationary either in their mean value or their variance or both. Variables with these characteristics are said to follow stochastic trends. Stock prices are a prime example of a random walk. It is hard to tell what the price of a stock will be tomorrow just by knowing its price today. The best guess about tomorrow’s price is today’s price plus or minus a random error term (or shock, as it is called). If we could predict tomorrow’s price fairly accurately, we would all be millionaires! The next topic we discussed in this chapter was the dummy dependent variable, where the dependent variable can take values of either 1 or 0. Although such models can be estimated by OLS, in which case they are called linear probability models (LPM), this is not the recommended procedure since probabilities estimated from such models can sometimes be negative or greater than 1. Therefore, such models are usually estimated by the logit or probit procedures. In this chapter we illustrated the logit model with concrete examples. Thanks to excellent computer packages, estimation of logit and probit models is no longer a mysterious or forbidding task.

Charlotte Bronte’s Aspects Of The Gothic

Charlotte Brontes Aspects Of The Gothic In †Jane Eyre†, Charlotte Bronte places her narrator and central character in the middle of dramatic events. One of these is at the start of the novel when Jane is trapped in the Red Room and the next is when she attends Thornfield Hall to work as a governess. Charlotte Bronte uses certain features of gothic literature to create a tense atmosphere for the reader. Jane Eyre is sent to live with her unfeeling aunt and abusive cousins, after her parents sadly passed away. Jane Eyre leads a very unhappy life as the people whom she grows up with do not treat her like family and blame her for any trouble. Now, Jane Eyre is locked in the Red-Room after an incident with her cousin, for which she takes the blame. As the years pass and Jane grows into a young woman, she is sent to Thornfield to work as a governess and, in the passage, is being shown around the estate. In the Red Room and at Thornfield hall, Bronte establishes a typical gloomy, gothic setting to create suspense and terror. Charlotte Bronte uses powerfully gothic descriptions of objects especially in the Red -Room. The name seems more important because of the alliteration and the fact that the room is identified as ‘red’ makes the reader feel that it is perhaps dangerous. The colour is often associated with blood and death, both of which create fear for the reader. We are told by the narrator that â€Å"The red-room was a square chamber, very seldom slept in, I might say never†. The use of the word †chamber† makes it sound much larger and grander and perhaps more uninviting than a regular room. The fact that the room is hardly ever slept in suggests that it is abandoned by all human company and creates a tense mood for the reader raising several questions about its safety. Bronte, therefore, uses colour to reflect the turmoil of emotions such as rage, fear and frustration which Jane is now experiencing. The objects which Charlotte Bronte describes in the Red Roo m create a typical gothic environment. We are told that the room is decorated very darkly. ‘The chairs were of darkly polished old mahogany’, which suggests that the furniture in the room is sombre, old and heavy. Colours associated with the gothic are generally darker shades, and the Red Room purposely creates images in the reader’s mind of gloomy objects to create a depressing atmosphere. When the writer describes the bed as â€Å"glar(ing) white† and the â€Å"snowy Marseilles counterpane†, this creates a contrast to the surrounding redness of the rest of the room. â€Å"Glared white† uses personification to describe the bedding as antagonistic to Jane as if it is watching her. This creates more torment for the reader. Even though the colour white might seem a much more optimistic colour than red, here it is used to create negative thoughts. The â€Å"snowy white counterpane† presents the bed as being icy cold, like death. When Jane looks in this mirror she sees a â€Å"half imp, half fairy† staring back at her. This introduces an element of the supernatural and suggests that Jane believes evil forces within the room may have possessed her and are reflected in the glass. Charlotte Bronte plays here on the superstitious fears of the reader. The fact that Jane Eyre is trapped in the red-room where her uncle died is terrifying enough but the idea that the room might have the power to drive Jane mad plays on our deepest anxieties. Death is a prominent feature of the gothic and Bronte uses the dead uncle and the possibility that he haunts the room to intensify the atmosphere. When Jane looks in this mirror is the most disturbing moment in the description of the red-room. Horror and fascination are created for Jane at this moment. The description of her †white face† and †glittering eyes of fear† show that Jane appears like a ghost to herself, the word †glittering† hinting a t madness.