20001549402000200660 Essays (912 words) - Laboratory Equipment

20001549402000200660 Empirical formula determination Lab # 66900096000 Empirical formula determination Lab # 6730005673725center Taylor Fitzgerald March 18, 20142420096000 Taylor Fitzgerald March 18, 2014 Short Form Memo-Style Lab Reports TO: Professor Michelle Sama FROM: Taylor Fitzgerald, CIT Student LAB PARTNER: Joe Toomey DATE: March 18, 2014 SUBJECT: Empirical Formula Determination Purpose: The purpose of this lab was to experimentally determine the empirical formula of magnesium oxide, the compound this is formed when magnesium metal reacts with oxygen. Summary: What I have learned in this lab is that the empirical formula that Joe and I gathered from this lab is Mg0, a 1:1 ratio. Procedures: During this lab to start each lab group sets up a ringstand with a ring, placing a clay triangle over the ring. There must be room under the ring for the Bunsen burner (approximately 2-3 inches). Next, connect the Bunsen burner to the gas outlet using rubber tubing. Once a group is given the okay by the instructor, the Bunsen burner may be turned on. Allow the flame to be about 1 inch. Next, dry a crucible by heating it over the flame of the Bunsen burner. Do not let the crucible glow red. Now using the tongs, remove the crucible from the triangle on the ring to a dry area at the work bench. Now the crucible and lid need to cool until each is at room temperature. This took my group approximately 15 minutes. Once they are at room temperature, weigh them both together on an analytical scale. While the crucible and lid are cooling, each group must collect a 25 cm piece of magnesium ribbon. Gently coil the ribbon the fit inside the crucible. My group coiled ours around a pencil. Now that the lid and crucible should be at room temperature, place the magnesium ribbon inside the crucible and place the lid on it. Each group will then measure and record the weight of the magnesium ribbon, crucible, and lid all together. Now, using the tongs, carefully return the crucible, magnesium ribbon, and lid back to the clay triangle above the Bunsen burner. Remove the lid but keep it nearby for the next step. Each group will heat the crucible and magnesium ribbon until the magnesium ribbon ignites. Be sure not to inhale or stare directly at the ignition. When the magnesium ribbon begins burning, place the lid back on top of the crucible using the tongs and remove the Bunsen burner from the area. After the ignition, glowing, and smoke have all disappeared, return the Bunsen burner underneath the crucible and being the heating process again. Remove the Bunsen burner and lift the lid on top of the crucible about every 2-3 minutes to check the reaction. After approximately 15 minutes or so, check to see if the reaction is completed. The magnesium ribbon should have changed to a light gray/white powder (magnesium oxide). If the ribbon material still remains after the 15 minutes, continue using the Bunsen burner until the magnesium ribbon is powdery. Now turn off the Bunsen burner and let the crucible and lid cool completely. Measure and record the combined mass of the crucible, lid, and magnesium oxide for one last measurement. Dispose of the magnesium oxide into the approved waste bucket provided by your chemistry professor(s). Lastly, clean up the group's entire bench area. Results: Item Mass Characteristics (shape/color) Empty crucible + lid 40.98 g N/A - No magnesium strip is involved yet Crucible, lid, and magnesium strip before heating 41.37 g Magnesium strip is present, shiny, silver Crucible, lid, and magnesium strip after heating 41.62 g White/gray powder What was the mass of the magnesium used? Magnesium = 0.38 g How many moles of magnesium were used? Moles of magnesium = (0.38 g / 24.30506 g/Mol) = 0.0156346045 Moles of magnesium Determine the mass of the magnesium oxide formed. 41.62 g - 40.98 g = 0.64 g Determine the mass of oxygen that combined with the magnesium. 41.62 g - 41.37 g = 0.25 g Calculate the number of moles of oxygen atoms that were used. (0.25 g / 15.99943) = 0.01563 Moles Calculate the ratio between moles of magnesium used and moles of oxygen used. Express the ratio in simplest whole-number form. 0.01563 : 0.01563 = 1:1 (ratio) 0.01563 : 0.01563 Based on your experimental data, write the empirical formula for magnesium oxide. MgO Calculate the percent error in your determination of the magnesium:oxygen mole

Abraham Lincoln and the the Civil War Essay Example

Abraham Lincoln and the the Civil War Essay Example Abraham Lincoln and the the Civil War Essay Abraham Lincoln and the the Civil War Essay Abraham Lincoln was the sixteenth president during the civil war. Most people said he was the best president. During the civil war 3 million people went to war and 600,000 died. He was born February 12, 1809, in Harden County, Kentucky. Both of his parents were born in Virginia. Lincoln made extraordinary efforts to study and become smart while he was working on a farm. He also split rails for fences, and worked in a store. He got very little formal education. He became captain in the Black Hawk War. He spent 8 years in the Illinois legislature. He also worked in the courts for many years. His law partner said of him â€Å"his ambition was a little engine that new no rest. † He then ran for Senator, but he lost the election, but he gained a national reputation that won him the Republican Nomination for president in 1860. He built the Republican National Organization. He’s also the one that issued the Emancipation Proclamation that declared to free the slaves that was on January 1, 1863. The Civil War was not entirely caused by Lincoln’s election, but the election was one of the primary reasons the war broke out the following year. Lincoln’s decision to fight rather than to let the southern states succeed was not based upon his feelings towards slavery. Rather, he felt it was his sacred duty to preserve the Union. His first Inaugural speech ended with a message that said â€Å"it shall be peace or the sword. † The southern navy turned away and surrendered after a 34 hour battle. The southern Emancipation Proclamation in 1863, encouraging the border states to outlaw slavery, and helping push through Congress the Thirteenth Amendment to the United States Constitution, which finally freed all the black slaves nationwide in December 1865. Lincoln was reelected in 1864. He figured he would not win, but he did. After he was reelected, a person named John Wilkes Booth assassinated Abraham Lincoln. John Wilkes Booth didn’t want blacks to be free. The assassination was a part of a larger plot to eliminate the Northern Government. Lincoln died the following day, after he was shot, and with him the hope of restructuring the nation without bitterness. Lincoln served our country from Mach 1861 until his assassination in April 1865. The deaths of their sons had profound effects on both parents. Later in life, Mary struggled with the stresses of losing her husband and sons, and Robert Lincoln committed her temporarily to a mental health asylum in 1875. WORKS CITED PAGE: civilwar. org/education/history/biographies/abraham-lincoln. html http://en. wikipedia. org/wiki/Abraham_Lincoln

International Relations and International Oganization Research Paper

International Relations and International Oganization - Research Paper Example Scholars find it hard to disown one of two competing subjects as each variable reflects distinct reasons for the competition2. Different global organizations formed by unification of states and the human thought are vital to resolve these issues before they create bad implications to both parties or one of them. Human beings often show competition over available resources, a factor which eventually stirs enmity and long-term dissatisfaction of the party that property loses its bid in the struggle3. Therefore, an intense purpose of the study shall establish the endeavors that several international organizations engage purporting to disintegrate power to an extent that curbs hostilities and ensures that all relations among nations serve the ethical interests of the majority group. The study shall reflect on the arguments encompassed in different articles presenting international organizations’ involvement in global relations and the intrinsic values that they often create thereof4. For example, war crimes reflect a daily rise from continent to continent and between neighboring nations thus; the approach shall implement the specific concerns of these atrocities, reflect on the prevailing interests or conflicting situations, and determine the result after involvement of an independent in ternational organization5. The continuous rate of interdependence among the global economies is a key stimulating factor to the relations among those nations. For instance, a nation that depends on another for the market of its commodities or the supply of raw materials shall focus on ensuring that these relations coexist almost eternally, and shall ensure that all conflicts surrounding that nation of interest do not injure the economic progression. Therefore, the statement of purpose in this context reflects on the international organizations

Juvenile Waivers Essay Example | Topics and Well Written Essays - 500 words - 1

Juvenile Waivers - Essay Example They were treated as juveniles. Special psychological treatments were given o them. Juvenile homes make every effort to turn them into responsible citizens. The main reason for children committing crimes is the domestic violence. Another reason is hardcore video games and movies containing this sort of acts. According to some voluntary organizations dedicated to drug and alcohol de-addiction, in the last few years' lots of youngsters have started approaching them. They also say that drug addicts start taking drugs at 13 or 14, but only come to the rehab centers when the situation becomes grim or out of control. This is because they start taking drugs recreationally and by the time they start having problems and decide to seek help it takes time. Unlike social drinking which doesn't affect one's lifestyle, there is nothing like social drugs because substances like cocaine and heroin are very addictive and illegal. The problem arises because most youngsters believe they won't get addicted not realizing that with drugs like cocaine and heroin it's very easy o get hooked on. And when they get addicted the trouble starts to unfold and slowly indulging in criminal offences comes into the picture. Independent courts act separate from other courts and are found in Connecticut Rhode Island and Utah. States like Alabama doesn't have independent and separate courts.

You can choose the topic that you prefer Essay Example | Topics and Well Written Essays - 3750 words

You can choose the topic that you prefer - Essay Example At the same time, others firms are diverging, they are rapidly encountering culture and economic. These all based global marketing strategy. First, the firm has to know, why this converge or divergence is required in business and at what perspective they can meet global market’ need as an evolutionary. Then the firm has to divide the markets in to five major segments and each part will be examined with respect of the economic and culture of markets. Next step, the outcomes of each segment has to be examined and implement different strategies in each segment to find out which will be the better market for the business and how the firm will grow. In this article, the firm wants to expand its operation management to fulfill the need of market. However, they are facing complex numbers of issue regarding this. The firm is unable to develop an integrated and unified strategy for the world market. The issue with the firm is competition and potential growth in the world market. So they need a diversification in their strategy to implement this whole process through which the firm can resolve the issues. To work in global market efficiently, the marking managers need to know the behavior of consumers, which is very complex in practical. In most case, the behaviors of consumers are assumed due to its complexity. It is very tough process to understand the behaviors of consumers and to develop a frame work for globalization. However, every marketing manager should understand the behaviors of consumers and to implement in a conceptual framework. This conceptual framework of consumer’ behavior always assists the business to develop in the global market. Through this, the company can satisfy the consumers and fulfill the demand of global market. Even, the conceptual framework also gives leads to the company about the government policies regarding consumers. This whole process is done thru the help of global convergence by protecting

The Sociological Definition Of Family

The Sociological Definition Of Family At the outset itself, this quotation describes the position of women in practically most society. Women are there to make children, are mothers and wives, act as the house-maid, take care of their husbands and families, bear male authority, being deprived of high status jobs and position of power. So, there is this element of discrimination which has prevailed through time. 2.1.1 Definition of Family According to Sociologists, the family is an intimate domestic group of people related to one another by bonds of blood, sexual mating, or legal ties. It has been a very resilient social unit that has survived and adapted through time. So, the element of time referred to above, is again present here. The family acts as a primary socialization of children whereby the child first learns the basic values and norms of the culture they will grow up in. a child needs to be carefully nurtured, cherished and molded into responsible individuals with good values and strong ethics. Therefore, it is important to provide them the best childcare so that they grow up to be physically, mentally and emotionally strong individuals. Similarly, The United States Census Bureau (2007) defines the family as a relatively permanent group of two or more people who are related by blood, marriage or adoption and who live under the same roof. Stephen (1999) defines the family as a social arrangement based on marriage including recognition of rights and duties of parenthood, common residence for husband, wife and children are reciprocal economic obligations between husband and wife. The family is seen as the main pillar block of a community; family structure and upbringing influence the social character and personality of any given society. Family is where everybody learns to love, to care, to be compassionate, to be ethical, to be honest, to be fair, to have common sense, to use reasoning etc., values which are essential for living in a community. Yet, there are ongoing debates that families values are in decline. Moreover the same family is viewed as an oppressive and bankrupt institution. George Peter Murdock (1949) defines the family as a universal institution. According to him, the family is a social group characterised by common residence, economic corporation and reproduction. It includes adults of both sexes at least two of whom maintain a socially approved sexual relationship and one or more children owned or adopted of the sexually cohabiting adults. However, K. Gough (1959) criticises Murdock definition and argues that the family is not universal. The critics were founded in the Nayar society. 2.1.2 Women and the Family The main role of women according to John Bowlby (1953) is particularly to act as mothers and as such their places are at home to take care of their children in their tender age. He states that juvenile delinquencies among young children are the result of psychological separation from mothers. The mental stability of children rests solely on their mothers. Therefore there is a need for a close and intimate mother and child relationship. However, Oakley (1974) uses the example of Alor, an island in Indonesia to refute Bowlby statement. In small-scale horticultural societies, women are not tied to their offspring, and there is no apparent side effect to it. Moreover, she does not see the intimate and close relationship necessary. Research has proved that mothers return to work after childbirth and that the children of working mothers are less likely to be delinquent than non-working mothers. Crouch (1999) describes the benefits gained by wives and mothers as the mid century social compromise. Duncan et al. (year) argue that women who define themselves as primarily mothers are located at all points on the social spectrum. Patricia Day Hookoomsing (2002) states that, plans and projects are designed and implemented by men. It is assumed that if men as heads of the family will reap the benefit from projects designed, automatically women and children will benefit. 2.1.3 The Darker Side of the Family / Erosion of Family Life Earlier in this review of literature, it is shown that the family is warm and supportive. However, many writers have questioned the darker side of the family. The fact that women spend most of their time either at work or doing household chores can lead to emotional stress in the family. The twentieth century family is mostly nucleus and thus children at times feel isolated and lacking the support of their extended kins: grandparents, aunts, cousins etc. They become introvert and their stress level rise to such an extent that when explosion occurs, it can have dramatic results. This may lead to violence, psychological damage, mental illness, drug intake, crime etc. The breakdown of children may lead to quarrel between parents. In the long run, marriages may fail and consequently lead to divorce. Incidence that may appear trivial can blow out of proportions and cause drastic consequence within the family. The mass media is increasingly bringing to people attention the sexual, physical and emotional abuse of children through neglect. Similarly, The National Society for the Prevention of Cruelty to Children (2000) parts that around 10% of children suffering from serious abuse or neglect at home by natural parents. Domestic violence is very prevalent in any society. It is estimated that one in four women are victim of domestic violence. 2.1.4 Conception about Family and Work Families and work have often been illustrated as separate entities, with women being linked to the home and men to the workplace. This separation unfortunately emanated by the sociology of the family being carried out as a separate domain from the sociology of work and occupations. However this assumption does not stand good in view of the increased participation of married women in the workplace. Early work by Rhona Raraport and Robert N. Raraport (1969) on dual-career families has talked about the benefits and strains of families with dual-earners. There are, however, many questions still to be answered concerning the interaction of family and work. Harkness and Waldfogel (1999) advocate that the formation of a family touches mostly female rather than male labour force behaviour. The withdrawal from labour after childbirth may lead to a depreciation of human capital. This may affect career commitment to employers and affect career progression. There are changes in family arrangements which prompt changes in production arrangements (Zaretsky 1976). Consumption was favoured to production within the household. Market relation became overruled by a capitalist market society and instead of economy being embedded in social relations, social relations are embedded in the economic system (Polanyi 1957). Dapne Johnson (1982) relates that the hours of work and schooling are organized at such time that it has become difficult to single-parent and dual-worker family. Moreover, school holidays add up to the problems of who will look after the child. Full-time married or cohabiting women generally have less time for leisure, as they are often expected to do two jobs their paid work and unpaid housework inside the family, Ken Brown (2008). 2.2.0 WOMEN AND WORK For most of us, work occupies a larger part of our lives than other single type of activity. In our modern societies having a job is important for maintaining self-esteem and to live in better conditions. According to Ken Brown (2008), work is the production of goods and services that usually earns a wage or salary or provides other rewards. The work may be effected in the formal or informal economy. He argues that work is an important element in occupying, directing and structuring the individuals time the demands of working life involve a high degree of self discipline if jobs are to be kept. It is, for most people, the single biggest commitment of time in any week, and it is perhaps one of the most important experiences affecting peoples entire lives. Work affects the amount of time and money available for family life. Work and family life have always been interdependent, but the increased employment of mothers just like nowadays the number of women working has risen from 66.2 million in 2009 compared to 1950 where it was 18.4 million. Pauline Wilson and Allan Kidd (1998) refer to work as a distinctive and clear cut activity. Work refers to the job or occupation undertaken. Work is both the place where one goes in order to do ones job and the activity that ones does. Sociologies increasingly recognize however that it is not easy to define work. The definitions concentrate solely on paid employment and are too narrow. Keith Grint (1991) also states the same thing and even presents a number of definitions to prove what he says:- Work can be seen as that which ensures individual and societal survival by engaging in nature. The problem is that many activities which cannot be seen are often regarded as work. Work cannot be defined simply as employment. Activities in which people are employed are also performed by people who are not employed. Examples include washing, ironing, etc., Work cannot be defined as something which can be done whether it is liked or not. Work can finally not be seen as non-leisure activities. Activities may be leisure for some but work for others. Work and leisure would be hard to separate if it goes together. 2.2.1 Reasons for working in paid employment Women work in paid employment for a number of reasons. These are as follows: Job satisfaction Money Company and friendship Status and identity To get out of the home and feel free To be independent 2.2.2 Functionalist Perspectives on Work and family Functionalist has given their views on the link between work and family and they argues that family meets the need of the workplace. The family adapts itself in the requirement of the society. They are living in a post modern way of living which is very good for the society as they prefer not to have children because of their career and some prefer to stay single and they are going according to the needs of the society. Functionalist like Talcott Parsons (1981) argues that women are proving the love care and affection to the family and men are maintaining order and security within the family. He also argues that it strengthens the conjugal bond between husband and wife and they complement each other. Young Willmott (1973) lay emphasis on how family evolved with the requirement of the society. Hence, both men and women are equal and they share the household work like for example man help in the kitchen when a woman is taking care of the child. P and B Berger argues that the bourgeois family already teach the child what the society want that is strict moral values and value economic success. Hence, how far does it apply in our modern society is highly debatable. On the other hand the functionalists have been criticized by E.Leach (1966) where he argued that the nuclear family is stressed. They are exploited by the capitalist and alienated; they work because they have no choice. Laing on the other hand argued that they are privatized they do not want people to know what is happening in their yard or house and they do not want people to come at their place without informing. Parents inculcate fear and suspicious in children so that they fear to revolt. 2.2.3 Feminist Perspectives on the work and family However, the Marxist feminists like Margaret Benston (1969) argue that capitalist pay women for their work at minimum wage scale. At present, the support of the family is a hidden tax on the wage earner that is his wage buys the labour power of two people. Thus, at work place women get jobs in the primary labour markets which are insecure, low wage income easily hired or fired. Even if women work they still have to act as a safety valve for the husband and men are still like a boss in the house. Hence, this lead to the work-family role conflict and tension are aroused by conflicting role pressures. Fran Ansley (1972) on the other hand wife still act as a safety valve and men are like boss at home. 2.3.0 WORK-FAMILY CONFLICT 2.3.1 Defining work-family conflict Howard (2008) in summarizing the definitions put forth by prior scholars (Greenhaus Beutell, 1985; Boyar, Maertz, Pearson, Keough, 2003) conceptualized work-family conflict as a type of inter-role conflict where both work and family issues exert pressures on an individual, creating a conflict where compliance with some set of pressures (family matters) increases the difficulty of complying with the other set of pressures (work matters). Work-family conflict has been shown to be related to negative work outcomes such as job dissatisfaction, job burnout, and turnover (Greenhaus, Parasuraman Collins 2001, Howard, Donofrio Boles 2004), as well as to outcomes related to psychological distress, and life and marital dissatisfaction (Kinnunen Mauno 1998, Aryee et al., 1999). Conflict occurs when the demands from one of these domains (home, work, personal, family etc) interferes with the other and causes imbalance (Frone et al., 1992, 1997). In todays hectic society, home and work are two colliding forces (Greenhaus and Powell, 2003) that has often lead to an imbalance, where women, lives to achieve fulfillment and satisfaction (Auster, 2001, Chalofsky, 2003). On the other hand, (Zedeck, 1992) suggests that a persons work experience influence his or her behaviour at home, influencing basic behaviors towards self and family members. Balancing the demands of work and the responsibilities of the family is an ongoing concern in organizational leadership. Today we are busier than ever. Gone are the days of the 40-hour work week, the two-hour lunch, leaving the office on time, and forgetting about office demands until the next business day. With todays advanced technology, such as cell phones with paging and instant messaging, wireless Internet access, and the mobile office following us wherever we go, it is easier to take the office on the road when we travel, even when the road leads home. Once at home, office responsibilities easily impose on family time with a quick call here and an email reply there limiting the amount of quality time available for the family. This incompatibility between the pressures of work and family concerns generates role conflict and stress on the follower. Researchers define the incompatibility between the domain of work and the domain of family as work-family conflict. Conflict between these domains occurs when participation in one role is more difficult due to participation in the other role. Today, work-family conflict (work interfering with family) is more prevalent than family-work conflict (family interfering with work) though both can occur. However, regardless of the direction of causation, when one domain is discordant with another domain, the result is conflict and increased stress on the individual. Current research focuses on the causes of work-family conflict, balance of time, involvement and satisfaction, quality of life, and the outcomes between the two domains. 2.3.2 Types of work-family conflict Greenhaus and Beuthell (1985) identified three major types of work-family conflict: (a) Time-based, (b) Strain-based and (c) Behaviour- based conflict. Time-based conflict occurs when time devoted to one role makes it difficult to participate in another for example, when mothers have to do overtime at work with little notice might make it difficult for them to meet family obligations, like picking up children from school. Time-based conflict, is the most common types of work-family conflict when multiple roles reduce the time and energy available to meet all role demands, thus, creating strain (Goode, 1960) and work-family conflict (Marks, 1977). Strain-based conflicts suggests that strain experienced in one role intrudes into and interferes with participation in another role; for example, mother who is anxious about their childs illness might not be fully concentrate on her job and this can cause mistakes in her work. Behaviour-based conflicts occur when specific behaviors required in one role are incompatible with behaviour expectation in another role. That is, when work roles cause problems at home or when home role causes problems at work. All these three forms are formulated based on the role theory, which conceptualize conflicts as reflecting incompatible demands on the person, either within a single or between multiple roles occupied by the individual (Kahn et al., 1964). (Carlson et al., 2000) also argue that another form of work/ family conflict is the Worry-based conflict in modern industrial society. On the other hand, increasing living cost, marital distress and parental stress may erode the stability of the family life (Lu, in press), causing worries which interfere with work. (Carlson et al., 2000) thus, defined worry-based conflict in terms of pervasive and generalized worries experienced in one role into and interfering with participation in another role. In a study, Fu and Shaffer (2001) identified several family and work specific determinants of FWC and WFC conflict, respectively. Testing these across the three forms of conflict-time, strain, behaviour-based they found that the family- specific variables were only effective in predicting time-based FWC conflict. As a group, the work-specific variables had much stronger effects and role conflict, role overload and hours spent on paid work were especially influential in explaining both time-based and strain-based forms of WFC conflict. Family conflicts was to be strong risk factor for the onset of elevated need for necessary need for recovery from work and fatigue. 2.3.3 Theoretical explanation of work-family conflict Two of the theoretical explanations of work-family conflict are: Attribution theory, Compensation theory. Attribution theory states that there are internal and external explanations for work-family and family-work conflict. An internal explanation is the desire to be successful in multiple roles such as mother or father, brother or sister, leader or follower, and peer or subordinate. Internally, work-family conflict occurs when what needs to be accomplished at work interferes with personal values. Personal values may include spending daily quality time with family or simply rejuvenating after a hard day of work. Internally-generated conflict can also occur when family responsibilities such as attending a school function or taking care of a sick child spills over into the responsibilities of work and increases the time spent away from the job. External causes of work-family conflict may occur when your work assignment takes more time to complete than what you have to give or when your spouse is unwilling to support your endeavors both at home and at work. Conflict between these domains al so occurs when the amount of control followers have over their job is limited and flexibility is minimal. Compensation theory suggests that people add more to one domain than the other in order to balance what lacks in either. In other words, life balance is a direct result of the amount of time and psychological resources an individual decides to commit to both domains. If the individual commits more time or psychological resources to work, then work-family conflict may increase. If an individual decides to commit more resources to family than work, but work requirements increase, then family-work conflict also increases. While the conflict between work and family may be inevitable, researchers such as Friedman and Greenhaus (2000) suggest alternative methods for reducing the stress. For example, more autonomy in the workplace and networking with peers can increase the quality of work life. Their studies show that individuals who work for organizations that allow individuals to spend more time at home, work in virtual employment, and make flexible time arrangements, tend to perform better as a parent than those who do not have these opportunities. Maintaining satisfaction on both fronts is important for reducing the feelings of conflict. 2.3.4 Concept of childcare The concept of childcare does not refer to supervision and responsibility of feeding, clothing and other aspects of daily care for a child for a certain period of time. Infact, childcare includes the responsibility of maintain a healthy balance of love, affection and discipline, to help all-round development of the child. Child care is one of the biggest challenges for working families. Due to their work responsibilities, parents are not always able to stay with their children and take care of them, as they would like to. Parents would like to leave their children in care of such individuals whom they can entrust their precious kids without any worries or misgivings. For a long time, childcare was the responsibility of the extended family. With industrialization and modernization of the family has created rather serious childcare problems. As the extended family weakens, help from relatives before more difficult for women who cannot afford childcare services, may have no choice than to opt out of the labour market. (Situation analysis of women and children in The Republic of Mauritius October 2003). When childcare arrangements break down, employed parents are more likely to be absent, late, report being unable to concentrate on the job, to have higher levels of stress and more stress-related health problems and to report lower parental and marital satisfaction (Galinsky Hugues, 1987; NCJW, 1987; Shin et al; 1987). Whenever a child is sick, very often it is the mother who has to take leave to take care of the child. Women spend more hours per week on household and on childcare than men do. And motherhood is a career liability for women while for men, being married and having children are both associated with higher earnings and being in upper level positions. (Friedman and Greenhaus, 2000). For many women parents stress starts as school ends. Most mothers cope with their child-care needs by taking children with them to work; leaving children with relatives, neighbors or older siblings and for short period of time leaving them alone. Children who are too old for day-care must stay in an empty home for several hours after school before the parents return from work. Such children are sometimes called latchkey children because they are usually given a key of the house or apartment so that they can let themselves in when they get home. Although the recent practice of women working brings financial independence, it also exalts difficulties and stresses to the working womens lives. This stress damages not only the quality of life and health of those who are victims (Parasuraman and Greenhaus 1992; rice et al; 1992). It can drive to unsatisfactory behaviors at jobs: delays, absenteeism, lack of motivation, reduced output (Beuthell et al., 1991) and in more difficult relation within the family (Duxbury and Higgins 1991; Parasuraman et al., 1992; Frone et al., 1992) This increased participation of women in the labour force has changed the traditional roles of men and women within the two-parent family resulting in the phenomenon of the dual-career couples (Kirrane and Monks, 2004). Balancing work, housework, and child-care responsibilities can become strenuous which can result in work-family conflict. This can be very costly to both organizations and employees within them (Posig and Kickul, 2004). As highlighted by Allen, Herst, Bruck, and Sutton (2000), the increase of dual-career couples with young children and changes in the traditional family structural configurations have resulted in changes in home and family responsibilities for both men and women. Even for todays dual-career couples, the transition to parenthood tends to mark a reversion to a more traditional division role, with women doing the lions share of the household maintenance and child care planning (Carler, 1996; Hoschchild, 1989) Childcare is a conceptually distinct and especially burdensome aspect of household labour (Berk, 1985; Rexroat Shehan, 1987). Floge (1986) points out that one major problem facing dual-earner couples are arranging for child care. As the number of dual earner couples with children increases, responsibility for child care arrangements becomes an increasingly important aspects of household labour. In addition to bearing the responsibility of proving adequate child care to their children, women may also experience feelings of guilt if they do not at least devote some of their free time to this task. Some women may therefore reduce their amount of sleep of free time resulting in the accumulation of strain and stress (Sterwart D.Friedman, Jeffrey H.Greenhaus, 2000). In addition to globalization the other key that has affected dual-income working families is the dramatic change in the characteristics of labour force. We all know that that due to globalization there has been a change in the nature of work and this has resulted from long hours of working. We cannot just blame families especially women for not being able to provide enough care for their children we must also know why the situation is like this. Hence, families has no other choice because may be they fear from being unemployed and nowadays being unemployed is not a pleasant thing due to the high standard of living. Families may no choice except from working because of being a single parent who has to take up all the responsibilities alone or because of being poor. However, we do agree with the fact that parents are working for the benefit of the family but how far is it really benefitting the family? When both parent work especially for long hours of work it become impossible to take care of the house and children because of exhaustion, stress and sometimes office work is being continued at home. This usually creates conflicts at home like domestic violence, long disputes where it affects not only parents but children as well. Sometimes everything end up by a divorce, the child will not be able to say anything but it affect him mentally and are unable to cope at school. Moreover, even if children are grown up they still need care and affection of parents which parents are unable to provide them due to their work overload. In a recent research womens work burden and human development in Mauritius (Feb 2006), Miriam Blin found that most women from EPZ felt they could not give their children care and attention they needed. Many could not provide care their children required. This, is why sometime children does not work well at school just like (see, for example, Behrman et al., 1999 Lack of parental support and involvement, as well as the absence of early stimulation, together with the breakdown of the family structure have been found to be important factors affecting childrens performances. Therefore, parents should find time to know what children are doing at school. Nowadays, children spend most of their time on Facebook and due to the fact that there is no parental control they have access to other site like pornography. Children are sometimes infl uence by peer group and may be this is why there is juvenile delinquency, like school truancy, smoking. There is lack of communication at home and children are unable to communicate with parents. Parents take children for granted where they think that their values and way of thinking do not differ. In a way parent should be involve with children so that they know what their children are doing and build a close relationship between both of them because it is not money that count, love, care and affection are more important and we should not forget that todays children are future adults citizen whom lies the responsibility to ensure the social and cultural harmony. Seeing all this we can say that we cannot apply the functionalist view in our society but we should also know that all society and all family differ.

The Scarlet Pimpernel :: Free Essay Writer

The Scarlet Pimpernel In 1792, during the French Revolution, a figure named the Scarlet Pimpernel saved many aristocrats from the French. Using daring plots and disguises he escaped from the French and his archenemy, Chauvlin. The richest man in England, Sir Percy Blakenley was married to the most beautiful woman in France, LadyMarguerite Blankenley. Sir Percy was an important character in The Scarlet Pimpernel. The story took place in both England and France. It started in Paris, France at the scene of the guillotine. Some of the story took place at The Fisherman's Rest in Dover, England. Otherparts of the story took place at Sir Percy's house in Richmond, England, The Chat Gris in Calais, France, Lord Greenville's Ball and The Covent Garden Theatre in England. The theme of the story was love and courage. It showed how much Sir Percy cared about and loved Marguerite. Marguerite once loved him, but now took him for granted and thought of him as a fop. It also limned how brave Sir Percy, The Scarlet Pimpernel, was in risking his life for the lives of the aristocrats. The Scarlet Pimpernel and a small band of devoted followers had dedicated their lives and fortunes to saving the innocent aristocrats of the French Revolution and the horror of the guillotine. They risked their lives on numerous occasions and rescued many French noblesse bringing them to the safety and security of England. Sir Percy Blakenley, one of the wealthiest men in England, was married to Lady Marguerite St. Just who was thought to be themost beautiful and smartest woman in Europe. She was perceived asa traitor to the French cause for having betrayed the Marquis de St. Cyr and his whole family to the bloody guillotine. This she was duped into doing because of her brother, Armond, who was almost killed by them for having dared to love the daughter of anaristocrat. Therefore Sir P ercy showed no love toward Marguerite and acted the part of a fool. She thought Sir Percy to be a vain,pompous dandy and could not conceive how she ever married him. Inspite of this she still had feelings of love for him. Sir Percy loved her deeply, though he also hated and detested her for what she did. He was emotionally torn between love and hate though he would have given or done anything for her, save revealing his true identity as The Scarlet Pimpernel.

Fine Sediment Analysis and the Impacts of Fine Sediment Pollution in Brampton Arm of River Nene

All right Sediment Analysis and the Impacts of Fine Sediment Pollution in Brampton Arm of River Nene Introduction:All right deposits are recognized as the most common and important beginning of pollution in the riverine system ( Robinson, 1973 ) . The sediment burdens delivered to watercourses starts from a figure of upstream primary and secondary deposit beginnings, including cultivated Fieldss and bank eroding ( Collinset Al.1997 ) . Erosion procedures and sediment bringing are the built-in portion of aquatic systems that influence the geomorphology, habitat distribution and H2O quality. The aquatic communities are besides extremely adapted and they are able to get by with the natural baseline deposit inputs. Whereas, the healthy fresh water ecosystems besides require the proper inputs of deposits into the system to keep the home ground and alimentary fluxes ( Collinset Al.1997 ) . At the planetary graduated table suspended solids concentrations in many rivers has increase dramatically in the recent old ages ( Walling, 2006 ) . Existing grounds suggests that natural deposit burdens have been well exceeded in many catchment countries in the UK, peculiarly since World War II ( Evans, 2006 ) . The deposit lading into the rivers and channels may be due to some natural procedure and some are due to anthropogenetic activities. The anthropogenetic activities which are majorly involved in the sediment pollution of the rivers are: addition in the countries of cultivable cultivation, taking to the greater countries of bare and exposed dirts susceptible to erodings by winter rainfall ( Greig,et Al.2005 ) ; mechanized farm patterns which compact the dirt increases overflow and dirt eroding ( McMellinet Al.2002 ; Bilotta,et Al.2007 ) ; Intensification of agribusiness patterns by the usage of multiple cropping on cultivable land technique ( Heanetet al. ,2001 ) and increased bank eroding due to the loss of natural hydrology. Excessive all right deposits in suspension or deposited can hold negative impacts upon all the life phases of fish, peculiarly salmanid in many parts of United Kingdom ( Collins and Walling, 2007 ; Collinset al. ,2008 ) . The impacts upon the ecosystems will depend on several cardinal factors like: the concentration of all right deposits in the suspension ; the continuance of exposure to the deposits ; and the chemical composing of the sediment atom size ( Bilotta and Brazier, 2008 ) . These all factors can do the finding of the impacts of mulct suspended atoms on the vegetations and zoologies of the river and watercourse. Effectss of Higher Fine Sediment contents:the relationship between the higher mulct suspended deposits on fish varies mostly, as it depends upon the life phase, clip of twelvemonth, size of the fish, and the composing of the all right deposits and handiness of off-channel home ground ( Bashet al. ,2001 ) . The exposure magnitude and the continuance and the frequence of exposures ( Servizi and Martens, 1992 ) are other countries of concern. For illustration, in reappraisal of the published literature the threshold degrees of the mulct suspended deposits are based on the dose-response experiments which examines the impaired growing, reduced eating and mortality, i.e. 27-80,000 mh/l for Mollusca and 4-330,000mg/l for assorted fish species ( Berryet Al.2003 ) . These scopes of the badness of consequence of SS concentration are map of associated stressors which includes atom size, species life, phase of life, temperature, the presence of certain deposit associated contaminations and the s ediment burden continuance ( Swietliket al. ,2003 ) . Due to the complex nature of the interaction of such stressors, it is improbable that a comprehensive list of genus-based critical suspended deposit concentration marks can be developed in the short term ( USEPA, 2003 ) . The higher content of the finer suspended can besides ensue in the decreased reproduction and the growing of the fish through the debasement of engendering home grounds and surrounding eggs and yolk-sac Fry. For illustration Salmonid eggs requires clean and good oxygenated environment during the embryologic development phase, so eggs are laid in permeable crushed rock beds with interstitial pore infinites which allow the transition of oxygenated H2O, inordinate all right deposits in the H2O can choke off these interstitial pores, blockading the circulation of the fresh oxygenated H2O, which may cut down the egg endurance ( Carling, 1984 ; Mageeet al. ,1996 ) . Furthermore, other effects of the higher all right deposit contents in the river watercourse on the fish biology includes: gill irritation/traumas, tumours and gill flaring ( Berg, 1982 ; Schleiger, 2000 ) ; addition in the plasma glucose in their blood systems ( Servizi and Martens, 1987 ) ; cut downing the migrating population of fish ( Newcombe and Macdonald, 1991 ) , the turning away reaction of the fish compels them to travel off from the country of higher pollution ( Sigleret al. ,1984 ; Bashet al. ,2001 ) ; all right deposits exerts an of import control on the transportation and destiny of a broad scope of agricultural and industrial contamination ( Warrenet al. ,2003 ) , so the deposits can act as vectors for the transportation of the pollutants in the H2O organic structures, many of the pollutants which are transferred by the all right deposits have abilities to poison the H2O system, and do it unsuitable for the aquatic life to last ( Nealet al. ,1999 ) The above treatment can be summarized by stating that the high concentration of the mulct suspended deposits can negatively impact the fish population by cut downing ; 1 ) the diverseness of sensitive species, 2 ) overall population copiousness, 3 ) the proportion of the omnivores within the overall population. All these factors can hold impacts even at sub deadly concentrations of the mulct suspended solids, cumulatively cut downing the resiliency of fish species and hence their opposition to environmental emphasiss including other signifiers of the H2O pollution, marauders, disease and over development. The cause and effects of biological and chemical debasement are good documented ( Fozzard, 1994 ) . Soulsby et Al. ( 2001 ) carried a prelimary survey on the engendering home ground utilized by Atlantic Salmon ( Salmon Alar ) and Sea Trout ( Salmo trutta ) . The high contents of the suspended mulct deposits in the crushed rocks provide cheques on the pink-orange productions in the low-land watercourse ( Sear, 1993 ; Brogan and Soulsby, 1996 ; Acornely and Sear, 1998 ) . The addition in the all right deposits in fresh water ensuing from the anthropogenetic activities and inordinate urban development is the possible stressor for fish and therefore may do population diminution. Additionally it can be said that the turbid H2O and to a great extent silted bed deposits can degrade the watercourse home ground for unattached immature salmonids in affected watercourses ( Lisle and Lewis, 1992 ) . The UK criterions for the mulct suspended deposits were set up by the EU Freshwater Fish Directive ( FFD ) . The FFD defined some bounds for the all right deposits in the fresh water i.e. the suspended deposits should non transcend the average one-year value of 25 mg/l ( EU, FFD ) . This was the lone criterion guideline which should be achieved where possible. No imperative criterions ( the criterions which must be met ) were in being for the all right deposits in the UK ( S & A ; T Briefing Paper ) . A assortment of sediment grain size fractions have been considered by the environmental directors and research workers in their attempts to place aquatic impacts in past. There is immense figure of documents published in past that gave different criterions of the all right deposit content in the aquatic systems, some of them are mentioned below: After the amendment of the British Columbia Water Quality Guidelines for turbidness, suspended and benthal deposits, the new guidelines recommended that streambed composing at Salmonid engendering sites should non transcend 10 % of & lt ; 2mm, 19 % of & lt ; 3mm, and 25 % of & lt ; 6.35mm ( Caux et al. , 1997 ) ; McNeil and Ahnell ( 1964 ) , they suggested that an addition in sediment volume of atoms less than 0.833 millimeter would ensue in both reduced permeableness and generative success ; Whereas, harmonizing to Chapman ( 1988 ) silver salmon and buddy salmon endurance was reciprocally relative to increase in particle size less than 3.3 m illimeter. Furthermore, he besides reported that any per centum of 6-12 millimeter atoms above 15 % or of atoms less than 6mm above 25 % reduces the opportunities of endurance of salmon eggs. Reasoning the above treatment, irrespective of the specific grain size of the deposits, it is readily evident that an surplus of all right grain deposit has possible to negative impacts on the biodiversity of the aquatic ecosystems. It has already been discussed that big figure or surplus of all right deposits would impair the reproduction behaviour of the fish and other life signifiers in aquatic ecosystems. Purposes of present survey:the chief purpose of the present survey is to happen out the all right deposit content of the Brampton Arm of the River Nene, and to compare the all right deposits in the watercourse with the above literature. The criterions proposed by Caux et Al. ( 1997 ) , would be used as mention for this survey. Furthermore, on the footing of the consequences of the experiment, we would hold a clear image of the all right deposit contents of the river watercourse under survey, to pull a decision whether the all right deposits would impact the fish biodiversity in the river watercourse or non. Methods:The method employed for the aggregation of the all right river bed deposits was simple Mac Neil Sampling technique. Three pails of the Mac Neil samples were collected at the site. The samples were dried and so sieved through the BS criterion screens in research lab to hold the clear image of the sediment contents of the subsurface river bed. For the surface grain size appraisal, Wolman technique was used to acquire an estimation for the surface grain size proportions. Consequences:the consequences are attached as graphs in annexures of this study. The analysis of the consequences which are attached asGraph 1shows that the riffle sample of the Bramptom arm of the river Nene was ill sorted ; the deposits were flaxen class crushed rock. It had big sum of class crushed rock and low measure of the really all right sand. The statistics of the sample are attached asTable 1in the annexures of this study. The measure of the really all right sand is below 2 % , whereas, it had big proportion of the medium crushed rock nowadays in it. The analysis of the ripple sample shows that the part of the river watercourse had really low all right deposits in it, which would impact the biodiversity and the fishing home grounds and angling community in the river. TheGraph 2,attached as an annexure, shows the consequences of the analysis of the 2nd sample which was attained at the Pool/Riffle Glide subdivision of the river watercourse. Again the sample was ill sorted and there was a loss of 1.2 % of the sample during the sieving operation. The sample showed the belongingss of sand crushed rock. There was a big proportion of the crushed rock nowadays in the sample, whereas, the sand is merely approximately 25 % . Further grain size distribution shows that the sample contained all right sand of approximately 2.9 % , and the really all right sand is below 1 % . As the sample can be classified as flaxen Gravel, it had major proportion of class crushed rock atoms, whereas, the all right crushed rock is non in important per centums. TheTable 2attached as the annexure shows the sum-up of the sample which was analyzed. Uniting both the samples, the loss of weight on sieving is 3.3 % . The comparative hapless sorting and the loss of weight of sample, h as decidedly impacted the consequences of the analysis. The loss may be termed as general sieving mistakes and mistakes related to the human carelessness. Discussions:on the footing of the consequences, certain decisions are drawn to acquire clearer image of the type of the river deposits present in the Brampton Arm of the river Nene. There was an overall loss of 3.3 % of the sample during the sieving operations. The mistake impacted the consequences of the analysis. Furthermore, the samples were ill sorted, which may be as a consequence of general human mistakes or deficient clip for screening operations. The tabular arraies and Graphs have been attached with this study to back up the treatment which was carried out in the literature above. As there are no perfect empirical criterions available by which we can compare the consequences of the experiment to happen out the measure of the all right deposits present in the river watercourse. For the interest of convenience, the criterions put frontward by British Columbia Water Quality Guidelines for turbidness, suspended and benthal deposits were used to compare the sum of all right deposits in the river watercourse. As mentioned above in literature part of the study, the all right deposits & lt ; 2mm should non transcend 10 % by proportion in the river watercourse. Sediments mensurating & lt ; 2mm if exceeds in the per centum proportion would hold hurtful impacts upon the fish home grounds and the other biodiversity nowadays in the river H2O. The overall consequences drawn from the experiments, and by graphs and tabular arraies, we came to a decision that the entire per centum of the atoms and the deposits less than 2mm were 12.2 % . The per centum is rather higher than the criterions proposed by the British Columbia Water Quality Guidelines. The dirt fish life in the country under survey may be held responsible for the addition in the all right deposits in the river system. Another ground behind this scenario may be that the country from where the Brampton arm of river Nene base on ballss had a big arable land around it, so any agricultural activity transporting out may be able contributes the all right deposit pollution in the river watercourse.

Analyse a set of results and investigate the provided hypothesise Essays

Analyse a set of results and investigate the provided hypothesise Essays Analyse a set of results and investigate the provided hypothesise Essay Analyse a set of results and investigate the provided hypothesise Essay Essay Topic: Thesis My name is Khalil Sayed-Hossen, Im a year10 student and am carrying out the Guesstimate coursework task. For this coursework I am going to analyse a set of results and investigate the provided hypothesise. Plan Within the duration of producing this (Guestimate) coursework, I will first investigate the hypothesis given, that people estimate the length of lines better than the size of angles. Once I have done this I will begin to investigate hypothesise of my own. I will need to find away of proving and disproving these hypothesise through analysing relevant data. The data I will be using is from a pooled set of results that members of my class have collected and combined together to form a broad, clearer set of results. To be able to compare a set of results there must be a clear comparison. Since the results of the length of the line were given in the mm and the size of the angle in à ¯Ã‚ ¿Ã‚ ½ (degrees) there is no clear comparison. To be able to compare these two different types of data I will need to calculate the percentage error for each result. This is done by first calculating the differences between the actual size of the angle and the length of the line, i.e. errors, and then by using the formula: Error à ¯Ã‚ ¿Ã‚ ½ Correct à ¯Ã‚ ¿Ã‚ ½ 100 = percentage error Ways in which I can compare this data include, looking at the mean of the results, standard deviation and through producing scatter graphs. Scatter graphs are useful as, once the line of best fit has been drawn we can then analyse the inter-quartile range. I will also use any other methods that become apparent during the duration of this coursework and apply them when investigating my other hypothesis as well. During the course of my investigation I will try and eliminate any bias that might occur. This is most likely to happen when I select a range of data from the pool of results, when selecting specific data I will try and sample as many random data as I can and make sure that it hasnt all come from one person. Collection of data As part of this coursework, a given task was to collect data from random people by asking them to estimate the length of a line in (mm) and the size of an angle in (à ¯Ã‚ ¿Ã‚ ½) degrees. Once these results were taken they were then entered onto an X-cell spreadsheet as raw data. This was carried out by each member of the class, and once each of us had completed this task we pooled our results to give a broad, clearer set of data, which could be used to investigate any hypothesise. Data analysis Once all the data has been collected I will begin to make an analysis and apply it to the given hypothesise in the coursework, and also my own hypothesise. Before I can do this I need to change the data from being just raw data, to data I can compare. As said earlier, this can only be achieved by working out the percentage error for each data point for both line guesses and angle guesses. I will now work out the percentage errors. I will start by splitting investigation into different parts, depending on what methods Im using to prove or disprove the hypothesis of line. I will first select the data from the pool that I will use to analyse. This is not as simple as it sounds though. When selecting data from the pooled set of results we need to take into consideration how many males were asked and how many females were asked, this is called stratified random sampling. We do this to prevent any bias. For example, if our pooled set of results contained 40 males and 90 females and we then selected 20 males and 20 females results to analyse, our data would be bias, as the ratio of women to men or men to women would not be the same as the original set of results, and would have changed significant. Stratified random sampling prevents this, and is achieved in this case by taking the number of males and dividing that by the total number of people, and multiplying this figure by however many samples are needed, this will then give the correct ratio of women to men if the process is then repeated for the amount of women. The formula looks like this- Group (male or female) à ¯Ã‚ ¿Ã‚ ½ total à ¯Ã‚ ¿Ã‚ ½ preferred number of data points I will now use this method to select a set of data points from the pooled set of results. In total there are 167 males and females who estimated the line and the angle, of these, 85 were males and 82 were females. So through knowing this information we can now calculate how many results of men and women are needed in my sample of however many data points by using stratified random sampling. Stratified Random Sampling I want to sample forty angle data points from the total of 167. I will now attempt to do this using the stratified random sampling method and formula. Group (male or female) à ¯Ã‚ ¿Ã‚ ½ total à ¯Ã‚ ¿Ã‚ ½ preferred number of data points Males 85 à ¯Ã‚ ¿Ã‚ ½ 167 à ¯Ã‚ ¿Ã‚ ½ 40 = 20.35 *(say 20) Females 82 à ¯Ã‚ ¿Ã‚ ½ 167 à ¯Ã‚ ¿Ã‚ ½ 40 = 19.64 *(say 20) *Rounded to the nearest whole number to give exact amount needed. So from these results I can see that the ratio of males against females is equal when rounded to the nearest whole number. From gaining this information I can now accurately begin to specifically sample 40 random data points from the pooled set of results. My Sample data line angle age gender 1 2000 45 16 M 2 35 52 12 F 3 50 43 45 F 4 50 45 14 M 5 48 40 46 M 6 55 50 14 M 7 25 45 17 F 8 30 40 45 F 9 37.5 32 44 M 10 60 30 14 M 11 100 70 47 F 12 60 40 15 M 13 30 36 14 F 14 50 35 61 M 15 50 40 45 F 16 60 30 41 M 17 30 40 46 F 18 40 40 16 F 19 45 38 36 M 20 30 45 32 F 21 45 40 66 M 22 65 35 34 M 23 55 35 34 F 24 50 40 62 M 25 40 35 46 F 26 40 40 41 F 27 50 45 14 M 28 55 45 50 M 29 40 9 71 F 30 20 45 16 F 31 50 45 14 M 32 40 50 14 M 33 40 45 41 F 34 60 50 15 M 35 70 75 14 M 36 53.2 47.2 28 M 37 40 35 34 F 38 45 45 45 F 39 37 45 79 F 40 10 45 12 F When selecting the data not only did I have to take into account the ratio of males to females but I also have to consider the fact that each persons results may not be reliable, so to prevent this, my data selection was spread throughout the pool and not all from one section, this was another way of preventing bias and unreliable data. Once I had finished selecting my sample data. I noticed that within my set of selected data there was an outlier or anomaly, this I have highlighted in green. This anomaly must be removed and replaced as it is not a fair representation of the average guess of the length of the line, and when calculating the mean of line guesses, the anomaly would have a large weighted effect and would make the mean of the results insignificant and unreliable. Revised set of sample data line angle age gender 1 40 30 78 M 2 35 52 12 F 3 50 43 45 F 4 50 45 14 M 5 48 40 46 M 6 55 50 14 M 7 25 45 17 F 8 30 40 45 F 9 37.5 32 44 M 10 60 30 14 M 11 100 70 47 F 12 60 40 15 M 13 30 36 14 F 14 50 35 61 M 15 50 40 45 F 16 60 30 41 M 17 30 40 46 F 18 40 40 16 F 19 45 38 36 M 20 30 45 32 F 21 45 40 66 M 22 65 35 34 M 23 55 35 34 F 24 50 40 62 M 25 40 35 46 F 26 40 40 41 F 27 50 45 14 M 28 55 45 50 M 29 40 9 71 F 30 20 45 16 F 31 50 45 14 M 32 40 50 14 M 33 40 45 41 F 34 60 50 15 M 35 70 75 14 M 36 53.2 47.2 28 M 37 40 35 34 F 38 45 45 45 F 39 37 45 79 F 40 10 45 12 F This is set of sample data is going to be used through out my investigation of the length of the line. I will now begin my investigation. Firstly, I will begin by converting all the line and angle data points into their percentage errors. As said in my plan, this is done to implement a clear comparison. I will first need to work out all the errors of the data points. We do this by subtracting the just the original guesses from the correct length of the line and size of the angle. I will use Excel to help me with this as through the use of excel we can use simple formulas to work out equations. Testing the hypothesis The hypothesis states that people estimate the lengths of lines better than the size of angles. I will now test this hypothesis by calculating the mean and of both line results and angle results and compare them. Once I have done this I will then implement other methods, such as standard deviation cumulative frequency graph, and inter-quartile range. Comparing data As I mentioned earlier, we need to be able to compare the line an angle guesstimate data, but at the moment there is no comparison. To be able to compare this data we need to find a comparison. The best comparison is to work out the percentage errors for each line guesstimates, and angles guesstimates, as this is relevant to both the two different units of measure and will be easy to compare. First thoughts and assumptions I think from what I know about angles and lines that the hypothesis is wrong and that people will estimate the size of the angle more accurately. When considering the length of a line its difficult to know just how long it is, this is because an exact line length is difficult to visualise, whereas with an angle we know that 90 degrees is a right angle, 180 degrees is a half, and this we can picture in our minds. So when we see an angle we use the visualisations of sizes of angles that we know to be true to estimate the size of another angle, as they have to be either smaller or bigger than these. But when we try an estimate the length of a line its not so easy, as a line has no limitations, it can be as long as we want, but an angle can be no greater than 360 degrees. Also an angle is a fraction of a circle, but a line can be a fraction of a line than has an unimaginable greatness of length. So baring this in mind, when people estimate the size of the angle I think they will be closer to the correct size, than when they estimate the length of a line. Calculating the percentage errors for line guesstimates line age gender Line error Line percentage errors 1 40 78 M -5 -11.11111111 2 35 12 F -10 -22.22222222 3 50 45 F 5 11.11111111 4 50 14 M 5 11.11111111 5 48 46 M 3 6.666666667 6 55 14 M 10 22.22222222 7 25 17 F -20 -44.44444444 8 30 45 F -15 -33.33333333 9 37.5 44 M -7.5 -16.66666667 10 60 14 M 15 33.33333333 11 100 47 F 55 122.2222222 12 60 15 M 15 33.33333333 13 30 14 F -15 -33.33333333 14 50 61 M 5 11.11111111 15 50 45 F 5 11.11111111 16 60 41 M 15 33.33333333 17 30 46 F -15 -33.33333333 18 40 16 F -5 -11.11111111 19 45 36 M 0 0 20 30 32 F -15 -33.33333333 21 45 66 M 0 0 22 65 34 M 20 44.44444444 23 55 34 F 10 22.22222222 24 50 62 M 5 11.11111111 25 40 46 F -5 -11.11111111 26 40 41 F -5 -11.11111111 27 50 14 M 5 11.11111111 28 55 50 M 10 22.22222222 29 40 71 F -5 -11.11111111 30 20 16 F -25 -55.55555556 31 50 14 M 5 11.11111111 32 40 14 M -5 -11.11111111 33 40 41 F -5 -11.11111111 34 60 15 M 15 33.33333333 35 70 14 M 25 55.55555556 36 53.2 28 M 8.2 18.22222222 37 40 34 F -5 -11.11111111 38 45 45 F 0 0 39 37 79 F -8 -17.77777778 40 10 12 F -35 -77.77777778 I will start by investigating the line. I first calculated the errors, by subtracting the correct length of the line away from the guesses. Once I had calculated the errors I was then able to use the percentage error formula: Error à ¯Ã‚ ¿Ã‚ ½ Correct à ¯Ã‚ ¿Ã‚ ½ 100 = percentage error In excel we do this in the percentage error column by dividing the first data point in the line error column by 45, then by multiplying this by 100 to find the percentage. This found the percentage error for the first data point, to find the percentage error for all the other data points, because the formula is the same for each of the other data points in this column we simply highlight the first data point using the right click of the mouse, drag down and the formula works out the percentage error in each cell. Calculating the percentage error for angle guesstimates angle age gender Angle error Angle percentage errors (%) 1 30 78 M -6 -16.66666667 2 52 12 F 16 44.44444444 3 43 45 F 7 19.44444444 4 45 14 M 9 25 5 40 46 M 4 11.11111111 6 50 14 M 14 38.88888889 7 45 17 F 9 25 8 40 45 F 4 11.11111111 9 32 44 M -4 -11.11111111 10 30 14 M -6 -16.66666667 11 70 47 F 34 94.44444444 12 40 15 M 4 11.11111111 13 36 14 F 0 0 14 35 61 M -1 -2.777777778 15 40 45 F 4 11.11111111 16 30 41 M -6 -16.66666667 17 40 46 F 4 11.11111111 18 40 16 F 4 11.11111111 19 38 36 M 2 5.555555556 20 45 32 F 9 25 21 40 66 M 4 11.11111111 22 35 34 M -1 -2.777777778 23 35 34 F -1 -2.777777778 24 40 62 M 4 11.11111111 25 35 46 F -1 -2.777777778 26 40 41 F 4 11.11111111 27 45 14 M 9 25 28 45 50 M 9 25 29 9 71 F -27 -75 30 45 16 F 9 25 31 45 14 M 9 25 32 50 14 M 14 38.88888889 33 45 41 F 9 25 34 50 15 M 14 38.88888889 35 75 14 M 39 108.3333333 36 47.2 28 M 11.2 31.11111111 37 35 34 F -1 -2.777777778 38 45 45 F 9 25 39 45 79 F 9 25 40 45 12 F 9 25 When calculating the percentage error for the angle guesstimates, we repeat the same process needed to work out the percentage errors for the line guesstimates. Except in this case we divided the errors by 36, as this was the correct size of the angle. Now that I have calculated the percentage errors for all data points of line and angles within my sample data, I will be able to proceed with my fist method of proving or disproving the hypothesis, this will be by calculating the mean of line percentage errors and angle percentage errors. I will then compare both means. Calculating the mean of the line percentage errors Line percentage errors (%) 11.11111111 22.22222222 11.11111111 11.11111111 6.666666667 22.22222222 44.44444444 33.33333333 16.66666667 33.33333333 122.2222222 33.33333333 33.33333333 11.11111111 11.11111111 33.33333333 33.33333333 11.11111111 0 33.33333333 0 44.44444444 22.22222222 11.11111111 11.11111111 11.11111111 11.11111111 22.22222222 11.11111111 55.55555556 11.11111111 11.11111111 11.11111111 33.33333333 55.55555556 18.22222222 11.11111111 0 17.77777778 77.77777778 Line percentage errors (%) -11.11111111 -22.22222222 11.11111111 11.11111111 6.666666667 22.22222222 -44.44444444 -33.33333333 -16.66666667 33.33333333 122.2222222 33.33333333 -33.33333333 11.11111111 11.11111111 33.33333333 -33.33333333 -11.11111111 0 -33.33333333 0 44.44444444 22.22222222 11.11111111 -11.11111111 -11.11111111 11.11111111 22.22222222 -11.11111111 -55.55555556 11.11111111 -11.11111111 -11.11111111 33.33333333 55.55555556 18.22222222 -11.11111111 0 -17.77777778 -77.77777778 To calculate the mean percentage error, we need to use the usual method of calculating any mean result. We need to add up all the percentage error data points and divide by how many data points there are. But before we can do this we need to make any negative percentage error data points positive. If this is not done, when we add up all the data, the negative data will subtract itself from any positive data, and this we do not want, as we are only looking at the percentage of which they were away from the correct, weather or not the guess was too high or too low, is insignificant. Adding all percentage errors To add the percentage errors we need to convert the negatives into positives, as said earlier. I did this in excel by squaring each negative percentage, by using the formula ^2, and then square rooting each percentage. Once I had done this I was able to add up all the percentage errors by first highlighting all the data points in the percentage error column and then by using the formula ? in excel, which means the sum of. This gave me the sum of all the percentage errors for the line, and the angle. The sum of the percentage errors for the line was 981.5555556% and for the angles 795%. Line percentage errors (%) Angle percentage errors (%) 11.11111111 16.66666667 22.22222222 44.44444444 11.11111111 19.44444444 11.11111111 25 6.666666667 11.11111111 22.22222222 38.88888889 44.44444444 25 33.33333333 11.11111111 16.66666667 11.11111111 33.33333333 16.66666667 122.2222222 94.44444444 33.33333333 11.11111111 33.33333333 0 11.11111111 2.777777778 11.11111111 11.11111111 33.33333333 16.66666667 33.33333333 11.11111111 11.11111111 11.11111111 0 5.555555556 33.33333333 25 0 11.11111111 44.44444444 2.777777778 22.22222222 2.777777778 11.11111111 11.11111111 11.11111111 2.777777778 11.11111111 11.11111111 11.11111111 25 22.22222222 25 11.11111111 75 55.55555556 25 11.11111111 25 11.11111111 38.88888889 11.11111111 25 33.33333333 38.88888889 55.55555556 108.3333333 18.22222222 31.11111111 11.11111111 2.777777778 0 25 17.77777778 25 77.77777778 25 24.53888889 23.625 Finding the mean percentage error What I did next was divide both numbers by 40, as this was the amount of data points. I was left with the products, 24.53888889% for the line, and 23.625% for the angles, which were the mean percentage errors. These are highlighted in yellow. The hypothesis states that people estimate lines better than angles. From information I have gathered through calculating the mean result of the percentage errors I have found that my findings contradict the hypothesis, and that people tend to estimate the size of angles better than the length of lines. My assumption that people will estimate the size of the angle better than the length of the line, for reasons mentioned earlier, was found to be true through this investigation. If I were able to make these findings more reliable I would have sampled a larger amount of data from a more extensive pool of data, as this would have decreased the effect that unreliable, bias data had on the mean. I will now investigate through other methods of proving and disproving the hypothesis. Cumulative frequency I could have at this point produced a frequency graph, but due to limitation in time I have decided to produce a cumulative frequency graph as this is a clearer, indicative representation of data, and I will be able to deduce more information from it. If we represent the percentage errors of both line and angle percentage errors individually in frequency tables, we can calculate cumulative frequencies. Once we have done this we can use these new values, when plotted and on a graph, to form a cumulative frequency curve. This is useful as we will be able to find the median from the halfway point, and we will be able to locate the upper and lower quartiles. The upper quartile is 75% and the lower quartile is 25 %. From knowing the upper and lower quartile, we can calculate the inter-quartile range. This is found by subtracting the lower quartile from the upper quartile. The inter quartile range is half of the data distribution and shows how widely spread the data is, if the inter-quartile range is small, then the distribution is bunched together and shows more consistent results, if the inter-quartile range is large, then the distribution is spread and shows a wider variation in results. We can compare both the line inter-quartile range and the angle inter-quartile range, and whichever is smallest, will be the most accurate, as this would mean a smaller percentage error. Line percentage errors cumulative frequency table Line percentage errors (%) Frequency cumulative frequency upper limits 0.-10 4 4 ? 10 11-.20 17 21 ? 20 21-30 5 26 ? 30 31-40 8 34 ? 40 41-50 2 36 ? 50 51-60 2 38 ? 60 61-70 0 38 ? 70 71-80 1 39 ? 80 81-90 0 39 ? 90 91-100 0 39 ? 100 101-110 0 39 ? 110 111-120 0 39 ? 120 121-130 1 40 ? 130 To produce a cumulative frequency table, you first set the boundaries for each group of percentage errors this has been done in the first column. We then count all the percentages that are within the boundaries of that group, and this is then recorded in the frequency column. Once this has been done for each group, we can then calculate the cumulative frequency by adding each of the previous frequency data points to the next, and record each product in the cumulative frequency column. We then state in the in the upper limits column, what the highest percentage error can be. Now that I have produced a cumulative frequency table, I can now start to produce a cumulative frequency graph. Line percentage errors cumulative frequency graph The graph shows the cumulative frequency curve of the line percentage errors. From this curve I can find the lower and upper quartiles. These were; Lower quartile = 13% Upper quartile = 35% From knowing the lower and upper quartiles, I can calculate the inter-quartile range, by simply subtracting the lower quartile from the upper quartile. Inter-quartile range = (35 13) % = 22% The inter-quartile range of the line percentage error, cumulative frequency graph is 22%. I will now investigate the cumulative frequency graph, of the angle percentage error. Angle percentage errors cumulative frequency table Angle percentage errors (%) Frequency cumulative frequency upper limits 010 7 7 ? 10 1120 14 21 ? 20 2130 11 32 ? 30 3140 4 36 ? 40 4150 1 37 ? 50 5160 0 37 ? 60 6170 0 37 ? 70 7180 1 38 ? 80 8190 0 38 ? 90 91100 1 39 ? 100 101110 1 40 ? 110 111120 0 40 ? 120 121130 0 40 ? 130 I have produced the cumulative frequency table for the angle percentage errors. I can now begin to draw the cumulative frequency graph. Once I have drawn this I will calculate the lower and upper quartiles, and then calculate the inter-quartile range. Once I know the inter-quartile range I will be able to compare the inter-quartile range for the line data and the inter-quartile range for the angle data Angle percentage errors cumulative frequency graph The graph shows the cumulative frequency curve of the angle percentage errors. From this curve I can find the lower and upper quartiles. These were; Lower quartile = 12% Upper quartile = 28% From knowing the lower and upper quartiles, I can calculate the inter-quartile range, by simply subtracting the lower quartile from the upper quartile as I did for the line percentage cumulative quartiles. Inter-quartile range = (28 12) % = 16% Comparing graph data I have found the inter-quartile range of both line and angle cumulative frequency graphs. Theses were, for the line percentage errors- 22%, and for the angle percentage errors-16%. Its clear to see from these results that the inter-quartile range of the angle percentage errors was much less than the inter-quartile ranges of the line percentage errors. There is a difference of 6% percent between the two results. This shows that there was a wider spread of data for the line percentage errors, and that the accuracy when estimating the lines length was not as precise as when the angles were estimated. I have shown through my investigations that when people estimated the length of a line and the size of an angle, results were more accurate when the size of the angle was estimated. My first thoughts were that people would estimate the size of angles better, as angles are a fraction of a circle, which is limited. But the length of a line is un-limited and it is difficult to visualise the correct length of lines. I believe that my thoughts could be true as the mean and inter-quartile range of the angle percentage errors, were more accurate than the line on both occasions. I have investigated this hypothesis using two different methods, and through them have concluded that people estimate the length of angles more accurately. My findings contradict the given hypothesis. Now that I have finished investigating the given hypothesis, I will begin to investigate my own hypothesis. Hypothesis 2 Females estimate the length of lines and size of angles better than males The above hypothesis is a hypothesis of my own and is one which I will now begin to investigate. I will use the same method of comparing percentage errors as used in the previous investigation. First thoughts Without analysing the comparisons between the results given from the different sexes, its difficult to say weather or not females were more accurate, as at first glance, it is not obvious. Data analysis To be able to compare male and female estimates, I must first divide my sampled data into two sections, one section of male estimates and another section of female estimates. Earlier in my investigation I specifically selected 20 male data points and 20 female data points using Stratified random sampling, to eliminate bias. This is now useful to me as than there is an equal amount of female and male data points, so I will be able to use an analyse my original set of sampled data. I will now separate male and females guesses into two columns and compare the mean of the percentage errors. I will be able to mix line and angle percentage errors as I am comparing how females and males estimate lines and angles generally and not line and angles individually. Male Line and Angle percentage errors Line and Angle percentage errors (%) 1 11.11111 2 4.444444 3 6.666667 4 8.888889 5 11.11111 6 13.33333 7 15.55556 8 17.77778 9 20 10 22.22222 11 24.44444 12 26.66667 13 28.88889 14 31.11111 15 33.33333 16 35.55556 17 37.77778 18 40 19 42.22222 20 44.44444 21 16.66667 22 25 23 11.11111 24 38.88889 25 11.11111 26 16.66667 27 11.11111 28 2.777778 29 16.66667 30 5.555556 31 11.11111 32 2.777778 33 11.11111 34 25 35 25 36 25 37 38.88889 38 38.88889 39 108.3333 40 31.11111 ?=948.3333 To calculate the mean percentage error I first need to add up all the percentage errors. To do this, I will use the ? formula in excel, as used earlier. The number highlighted in green is the sums of the line and the angle percentage errors. To gain the mean of the percentage I need to divide them by 40, as this is the amount of percentage error data points. The product I am left with is 23.70833% this is the mean percentage error for male line and angle estimates. Female Line and Angle percentage errors Line and angle percentage errors (%) 1 22.22222 2 11.11111 3 44.44444 4 33.33333 5 122.2222 6 33.33333 7 11.11111 8 33.33333 9 11.11111 10 33.33333 11 22.22222 12 11.11111 13 11.11111 14 11.11111 15 55.55556 16 11.11111 17 11.11111 18 0 19 17.77778 20 77.77778 21 44.44444 22 19.44444 23 25 24 11.11111 25 94.44444 26 0 27 11.11111 28 11.11111 29 11.11111 30 25 31 2.777778 32 2.777778 33 11.11111 34 75 35 25 36 25 37 2.777778 38 25 39 25 40 25 26.41667 If I repeat the same process used for the male percentage errors, to obtain the mean of the female percentage errors, I am left with the product 26.41667%. This is the mean percentage error for line and angle percentage errors. From calculating the mean percentage errors of line and angle percentage errors, for both genders, I have found that males were more accurate at estimating the size angles and length of lines than females, and that this contradicts my hypothesis. To improve the reliability of my findings I will now investigate standard deviation. Standard deviation Standard deviation is useful to measure the spread of the data. Standard deviation gives a more detailed picture of the way in which data is dispersed around the mean, being the centre of distribution. If the difference between the standard deviation and the mean is large, the data is not consistent and is not typical of the mean. To work the standard deviation, I need to subtract the mean percentage error from each percentage error to create a set of deviations. Once I have done this I need to square each deviation to make a set of squared deviations. I can place this information in a table x (x-x) (x-x)à ¯Ã‚ ¿Ã‚ ½ x = percentage error x = mean percentage error I then need to average the set of deviations, by finding the mean of the standard deviations. Once I have done this I will need to take the square root so that the answer is back to the original measure, in this case percentage. This can be represented by the formula V ?(x x) à ¯Ã‚ ¿Ã‚ ½ à ¯Ã‚ ¿Ã‚ ½ n I will now use my male sample percentage error data, to formulate a table Standard deviation table of male percentage errors x (x-x) (x-x)à ¯Ã‚ ¿Ã‚ ½ 2.777778 -20.9306 438.08801 2.777778 -20.9306 438.08801 4.444444 -19.2639 371.0973 5.555556 -18.1528 329.5232 6.666667 -17.0417 290.41828 8.888889 -14.8194 219.61583 11.11111 -12.5972 158.68995 11.11111 -12.5972 158.68995 11.11111 -12.5972 158.68995 11.11111 -12.5972 158.68995 11.11111 -12.5972 158.68995 11.11111 -12.5972 158.68995 11.11111 -12.5972 158.68995 13.33333 -10.375 107.64063 15.55556 -8.15277 66.467659 16.66667 -7.04166 49.584976 16.66667 -7.04166 49.584976 16.66667 -7.04166 49.584976 17.77778 -5.93055 35.171423 20 -3.70833 13.751711 22.22222 -1.48611 2.2085229 24.44444 0.73611 0.5418579 25 1.29167 1.6684114 25 1.29167 1.6684114 25 1.29167 1.6684114 25 1.29167 1.6684114 26.66667 2.95834 8.7517756 28.88889 5.18056 26.838202 31.11111 7.40278 54.801152 31.11111 7.40278 54.801152 33.33333 9.625 92.640625 35.55556 11.84723 140.35686 37.77778 14.06945 197.94942 38.88889 15.18056 230.4494 38.88889 15.18056 230.4494 38.88889 15.18056 230.4494 40 16.29167 265.41851 42.22222 18.51389 342.76412 44.44444 20.73611 429.98626 108.3333 84.62497 7161.3855 13045.912 326.14781 18.059563 Once I had organized the data from smallest to largest in column x, I could calculate column 2(x-x) by subtracting the mean, which is 23.70833, from each percentage error. I then calculated column three (x-x) à ¯Ã‚ ¿Ã‚ ½ by multiplying each data point in column two by power 2, by using the excel formula ^2. Calculating the Standard Deviation Once I had finished formulating the table, I was able to find the Standard Deviation. I need to use the formula V ?(x x) à ¯Ã‚ ¿Ã‚ ½ à ¯Ã‚ ¿Ã‚ ½ n. So I firstly had to work out the sum of the (x-x) à ¯Ã‚ ¿Ã‚ ½ column, the product was 13045.912. I then divided this number by 40, to find the mean of the data, as this is the number of data points and the product was 326.14781.The final calculation I had to make to conclude with the standard deviation was to square root the mean, as I needed to find the original unit of measure, in this case it was percentage. The standard deviation of the male line and angle estimates is 18.1% to 3.sf. Standard deviation table of female percentage errors x (x-x) (x-x)à ¯Ã‚ ¿Ã‚ ½ 0 -26.4167 697.84045 0 -26.4167 697.84045 2.777778 -23.6389 558.79721 2.777778 -23.6389 558.79721 2.777778 -23.6389 558.79721 11.11111 -15.3056 234.26017 11.11111 -15.3056 234.26017 11.11111 -15.3056 234.26017 11.11111 -15.3056 234.26017 11.11111 -15.3056 234.26017 11.11111 -15.3056 234.26017 11.11111 -15.3056 234.26017 11.11111 -15.3056 234.26017 11.11111 -15.3056 234.26017 11.11111 -15.3056 234.26017 11.11111 -15.3056 234.26017 11.11111 -15.3056 234.26017 11.11111 -15.3056 234.26017 17.77778 -8.63889 74.63042 19.44444 -6.97223 48.611991 22.22222 -4.19445 17.593411 22.22222 -4.19445 17.593411 25 -1.41667 2.0069539 25 -1.41667 2.0069539 25 -1.41667 2.0069539 25 -1.41667 2.0069539 25 -1.41667 2.0069539 25 -1.41667 2.0069539 25 -1.41667 2.0069539 33.33333 6.91666 47.840186 33.33333 6.91666 47.840186 33.33333 6.91666 47.840186 33.33333 6.91666 47.840186 44.44444 18.02777 325.00049 44.44444 18.02777 325.00049 55.55556 29.13889 849.07491 75 48.58333 2360.34 77.77778 51.36111 2637.9636 94.44444 68.02777 4627.7775 122.2222 95.80553 9178.6996 26785.15 669.62875 25.877186 Once I had organized the data from smallest to largest in column x, I could calculate column 2(x-x) by subtracting the mean, which is 26.41667 from each percentage error. I then calculated column three (x-x) à ¯Ã‚ ¿Ã‚ ½ by multiplying each data point in column two by power 2, by using the excel formula ^2. Calculating the Standard Deviation Once I had finished formulating the table, I was able to find the Standard Deviation. I needed to use the formula V ?(x x) à ¯Ã‚ ¿Ã‚ ½ à ¯Ã‚ ¿Ã‚ ½ n. So I firstly had to work out the sum of the (x-x) à ¯Ã‚ ¿Ã‚ ½ column, the product was 13045.912. I then divided this number by 40, to find the mean of the data, as this is the number of data points and the product was 326.14781.The final calculation I had to make to conclude with the standard deviation was to square root the mean, as I needed to find the original unit of measure, in this case it was percentage. The standard deviation of the male line and angle estimates is 25.8% to 3.sf. Comparing data From investigating my hypothesis, I have found that through investigating the mean of the percentage errors for male and female estimates, males were more accurate. But when I investigated the percentage errors through standard deviation, I found that females were more consistent with estimating and that female estimates were more typical of the mean than male estimates. But this is irrelevant as the data still shows that males were more accurate as the standard deviation of the male estimates was 18.1% and the standard deviation of female estimates was 25.8%, which is a difference of 7.7%. My findings contradict my hypothesis and males were more accurate at estimating lengths of lines and size of angles. Evaluation I believe that I have investigated both hypotheses as much as I could have in the time I have been given. The conclusions I have come to through my findings were based upon the data pooled by my class. I believe that some of this data may have been unreliable due to errors etc. I believe that with a more extensive pool of data, my findings would have been more conclusive an indicative a true representation. I have reached the end of my investigation. If the time allocation was greater, I could have investigated another hypothesis such as Younger people estimate lines and angles better than older people.

Avogadros Law Definition in Chemistry Terms

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