Being an effective primary school teacher

Being an effective primary school teacher Being an effective primary school teacher Introduction This essay discusses the question, â€Å"What do you consider to be an effective primary teacher?†. With reference to recent research, government initiatives and your own experience, the essay explores this question, based on my own educational principles and the ways in which these will underpin your professional practice in the future. The essay begins by reviewing the Government policies and initiatives that are relevant to the research question, discussing, in particular, the document  Excellence and Enjoyment – A Strategy for Primary Schools  (DfES, 2003) and the subsequent Primary Strategy framework for primary education. The essay then moves on to discuss the aims of these policies and initiatives and the implications these have had for schools and teachers. The assessment framework is discussed, and how this impacts on teacher effectiveness is also noted. The essay then moves on to looking at the qualities of effective teachers, and effective teaching in a primary setting, and concludes that some of the facets of Government policies and initiatives – such as continual assessments – run counter to my ethos of effective teaching and actually serve as little other than distractions from pure teaching time, through all the administration such assessments bring and the amount of time this takes away from lesson planning, for example. Recent policies and initiatives in primary education In terms of Government policy towards primary education, in 2003, the Government launched the policy document  Excellence and Enjoyment – A Strategy for Primary Schools  (DfES, 2003)  which set out a vision for the future of primary education built, formally, on the striving for higher standards through the formulation of a rich and varied curriculum which is aimed at developing children in a number of ways. As explained by the DfCSF (2008), the key to making this vision a reality lies in the need to empower primary school children to take control of their own learning, to be innovative and to develop their own character. The DfCSF (2008) also noted that the aims of the policy  Excellence and Enjoyment – A Strategy for Primary Schools  (DfES, 2003)  should also be achieved through schools being able to set their own targets, based on challenging but realistic targets for the progress of each individual child, with LEA targets being set after this. In addition, the policy document  Excellence and Enjoyment – A Strategy for Primary Schools  (DfES, 2003)  encourages schools to network to learn from each other and to develop good practice, in partnership with parents in order to help children as far as possible and to forge links between schools and communities (DfCSF, 2008). The policy document  Excellence and Enjoyment – A Strategy for Primary Schools  (DfES, 2003)  was intended as an enabler, with leadership in schools being strengthened in terms of professional development of teachers towards the whole curriculum, and in terms of helping schools themselves design broad curriculum that links different areas of the curriculum and which thus provides children with as wide as possible a range of learning experiences (DfCSF, 2008). The policy document  Excellence and Enjoyment – A Strategy for Primary Schools  (DfES, 2003)  argues that the best primary schools are those that offer a broad and rich curriculum, and that, based on this it is fundamental that schools develop their own distinctive character through taking ownership of the curriculum, by being creative and innovative, using tests, targets and tables to help every child to develop his or her potential (DfES, 2003). Essentially, the policy document  Excellence and Enjoyment – A Strategy for Primary Schools  (DfES, 2003) urged the promotion of excellence in primary teaching through building on the success of the National Literacy and Numeracy Strategies, using the new Primary Strategies to extend this success in to other areas of the curriculum, including in foreign languages, sport and creativity, amongst other areas, measuring the success of this curriculum through assessments (DfES, 2003). The Assessment Process: its implications for teaching practice and childhood attainment There are many ways in which assessment activities can take place in the classroom, including monitoring normal classwork activities, using specific assessment tests designed by the teacher, designating assessment tasks as part of normal classwork, providing assessment tasks as part of homework assignments, and others, which are the domain of higher educational levels than the foundation stage, such as the use of standardized National Curriculum tests and/or formal examinations (Kyriacou, 1999; p.107). Kyriacou (1999; p.107-109) details each of these assessment protocols, showing, for example, how, although monitoring classroom activity is a part of the normal routine of a teacher, the monitoring, when it becomes investigative and active can become a form of assessment (Kyriacou, 1999; p.107; Kyriacou, 1997). In this way, the monitoring can inform teaching practice, through leading to suggestions for improvements in how learning is delivered, based on observations of areas in which t he children are failing to learn as quickly or as thoroughly compared to other areas, for example. In this way, monitoring and assessment can be a route through which teaching can be improved and teachers can become more effective. In terms of how the assessment is actually made (i.e., the actual process of assessment), evidence is collected through an ongoing process, via the teachers knowledge of the child, information from other contributors who are in regular contact with the child, anecdotes about significant moments in the child’s development, and focused assessments, based on observation where observation is understood to mean â€Å"the practice of watching and listening to a child as they engage in an activity and demonstrate specific knowledge, skills and understanding† (NAA, 2007). As pointed out by Kyriacou (1999; p.106), it is imperative that an adequate record of the child’s achievements, and their assessment, is kept, and that portfolios of children’s work are kept in order to exemplify the standards that are being sought, and so that teachers can use these records as a benchmark to build upon, through which improvements to teaching practice can be made and teacher effe ctiveness be improved. Teachers thus need to be competent in many areas in order to ensure that the assessment process goes smoothly for all concerned and that the assessment process is something that can be useful for teachers, in terms of improving teaching practices. The need for teachers to be competent in the assessment of children is reflected in the fact that the DfEE (2000) list of standards for teachers lists the ability to assess and record each pupils progress systematically as a competency (Kyriacou, 1999; p.106). In addition, it is fundamental that assessment judgements are agreed amongst all concerned, so that all those involved can make the best, fullest, use of the information. The Primary Strategies outlined in the policy document  Excellence and Enjoyment – A Strategy for Primary Schools  (DfES, 2003)  thus built on the National Literacy and Numeracy Strategies to lead to the development of the Primary Curriculum, with the National Literacy and Numeracy Strategies being embedded in the  Primary Strategy  (under the framework of the Primary Framework for literacy and mathematics that was launched in October 2006) (DfCSF, 2008). This new framework builds on the learning that has taken place since the National Literacy and Numeracy Strategies were launched in 2003, providing new structures and new impetus to the vision embodied in the policy document  Excellence and Enjoyment – A Strategy for Primary Schools  (DfES, 2003), extending, as it does, the support given only to literacy and to numeracy to other subjects (DfCSF, 2008). The overall ethos of the new Primary Strategy framework is that excellent education is an education that is tailored to children’s specific needs, allowing them to engage with the educational process and giving them the start they need to be able to succeed in the context of secondary education (DfCSF, 2008). In terms of the future education of primary children, and how assessments at the primary level affect children’s future educational development, it is well documented that the level of educational attainment of a child (as assessed through Key Stage 1 assessments) cannot – alone – be used as an indicator of how well a particular teacher or school has performed; it is the  relative  progress that needs to be considered in terms of making an assessment of how children’s future educational prospects are affected by the assessment process (Kyriacou, 1999; p. 106). Ways to do this include taking baseline measurements of achievement and comparing these with achievement following a certain time period of education, or taking value-added measurements (Kyriacou, 1999; p.106). In terms of tracking how children progress beyond the primary level, the relationship between Key Stage 1 assessments and attainment in terms of National Curriculum levels needs to be explored (AAIA, 2007). As discussed by AAIA (2007), however, Key Stage 1 attainments cannot be directly related to National Curriculum levels and any such attempts would result in spurious information (NAA, 2006). It is clear, however, that the higher the child’s assessment at Key Stage 1, the more likely it is that the child would attain high levels following the National Curriculum tests (AAIA, 2007). Models of good early years education Cohen  et al.  (2004) provides information on how to plan and organise classes, and shows how the Qualification and Curriculum Authority (QCA) has set out principles for early years education (QCA, 1999; 2000; 2001), on the basis that, â€Å"effective education requires both a relevant curriculum and practitioners who understand and are able to implement the curriculum requirements†¦building on what children already know and can do, encouraging a positive attitude and a disposition to learn and to protect against early failure†. As the QCA (1999, 2000, 2001) point out, early years education should be carefully structured, providing different starting points, depending on what the child can already do, should have relevant and appropriate content, matching the different levels of children’s needs and should provide planned and purposeful activities which provide opportunities for teaching both indoors and outdoors, with teachers who are able to observe and respo nd appropriately to the children under their care. This is on the basis that â€Å"parents are children’s first and most enduring educators† (QCA, 2000, p.9), and that teachers provide a series of stepping stones through foundation stages, through Early Learning Goals, through primary level, which articulates with the National Curriculum which all children from age five are legally bound to follow (Cohen  et al.,  2004; Parliamentary Office of Science and Technology, 2000). Cohen  et al.  (2004) show how key aspects of effective learning at the primary level are active, integrated, socially constructive, cognitively constructive and linguistically rich learning, beginning where the individual learner is at themselves, in terms of their learning process, so that the individual child is the agent of their learning, empowering the children to enable their own learning by casting learning as problem-solving (Morrison, 2000; Cohen  et al.,  2004). As Morrison (2000; p.122) states, â€Å"the intention (of learning) is to extend play, to empower students and to enable them to take responsibility for their own, active and autonomous, learning and to develop in all aspects of their learning†. This is conducted, generally, through four key elements: classroom arrangements (with such things as centres of interest), daily schedules of plan-do-review sessions, key curricular and learning experiences and content and assessments through observation, rec ording and sharing, using authentic assessment and portfolios (Cohen  et al.,  2004). By following such suggestions for enabling learning at the primary level,  continuity  and  progression  are ensured. Continuity  is  generally defined, and understood, as ensuring that the overall aims, values and beliefs that give direction to, and put boundaries around, the scheme of work are consistent, regardless of who is teaching or answering later questions (Fabian and Dunlop, 2002).  Progression  is defined, and understood, generally, as the process through which the schools planned activities gradually extend pupils’ thinking, their exploration of values and attitudes, enrich language, knowledge and strategies through increasingly demanding inputs and challenging explorations, matched to pupils chronological age, readiness and circumstance (Fabian and Dunlop, 2002). Through ensuring continuity and progression, children can be enabled to achieve the goals they want to achieve, within the frameworks that are set them. My personal teaching ethos This section takes one or two of my principles to explain how I intend to be an effective primary teacher, using examples from your my school experiences. In essence, I concur with Cohen  et al.  (2004) that, â€Å"effective education requires both a relevant curriculum and practitioners who understand and are able to implement the curriculum requirements†¦building on what children already know and can do, encouraging a positive attitude and a disposition to learn and to protect against early failure† and I agree with the overall stated ethos of the new Primary Strategy framework is that excellent education is an education that is tailored to children’s specific needs, allowing them to engage with the educational process and giving them the start they need to be able to succeed in the context of secondary education (DfCSF, 2008). Taylor and Hayes (2001) provide a discussion as to how education  should  be delivered, leading me to arrive at several conclusions as to how I should organize my time as a teacher in order to provide the most effective teaching possible to my pupils. I agree with the aims of the Primary Strategy as set out in the policy document,  Excellence and Enjoyment – A Strategy for Primary Schools  (DfES, 2003), which  encourages schools and teachers to network to learn from each other and to develop good practice, in partnership with parents in order to help children as far as possible and to forge links between schools and communities (DfCSF, 2008). The dictates of the assessment processes and the Curriculum mean I have to teach within these boundaries, but this does not mean that lessons have to be rigid and that assessments and tests and Curriculum have to be frightening terms to primary age pupils. One of my responsibilities as an effective teacher is to prepare students, as well as possible, for the assessments and to teach the Curriculum in such a manner that the children’s sense of wonder is upheld (see Allen and Ainley, 2007) and that children’s awareness of themselves as part of a whole and as spiritual beings is also encouraged (Eaude, 2005). My aim as a primary teacher is to foster a sense of enjoyment in the learning process and, through this, to foster a love of learning that will continue well beyond the primary level, encouraging success at the secondary level and forging a lifelong love of learning in each individual pupil, based on a sense of wonder at the world, its contents and its processes. I, personally, agree with Cohen  et al.  (2004), who show how key aspects of effective learning at the primary level are active, integrated, socially constructive, cognitively constructive and linguistically rich learning, beginning where the individual learner is at themselves, in terms of their learning process, so that the individual child is the agent of their learning, empowering the children to enable their own learning by casting learning as problem-solving (Morrison, 2000; Cohen  et al.,  2004). It is my aim as a teacher, wishing to be an effective teacher, to foster the empowerment of children, through developing a sense of the wonder of learning and empowering the children to direct their own learning, within the context of the Curriculum, so that children feel they are capable of learning and are capable of achieving the standards they set themselves. The Success of New Labour’s Policy Towards Primary Education Tymms (2004) look at how successful the changes to primary education have been, following the introduction of the Numeracy and Literacy Strategies and finds that, whilst the introduction of these Strategies contributed to a rise in standards, independent tests of children’s attainment have shown that this rise in standards is not as widespread nor as high as claimed and that, as such, an independent body should be set up to monitor standards over time, with the purpose of testing how Government planning for education is actually being received on the ground, as it were. A recent Oftsted report (Ofsted, 2003) also shows that some of the aims of the National Numeracy and Literacy Strategy were not achieved (with weak subject knowledge being a common failure of schools), suggesting the Government’s approach to primary education needs to be looked at further. Allen and Ainley (2007) back this suggestion, through their analysis of education in the UK, presented in their book  Education make you fick, innit?  Allen and Ainley argue that as institutionalized learning has become more common-place in the Uk, through schools and work-based training programmes, possibilities have been foreclosed for emancipating minds, something that is increasingly being applied to primary level education, through the introduction of the Primary Strategy, for example, and the assessment-based curriculum this embodies, which, argue Allen and Ainley (2007) forces teachers to concentrate more on training children in the Curriculum for the purpose of attaining high scores on the assessments than on actually instilling a sense of wonder in learning. Allen and Ainley (2007) argue that this process is killing the sense of wonder in children, and that, even for primary school children, education, the process of going to school, has become little more than a daily g rind, rather than a joyous process the children are happy to undertake because they enjoy the process and because the process can bring them knowledge and enjoyment. Conclusion This essay has discussed the question, â€Å"What do you consider to be an effective primary teacher?†. With reference to recent research, government initiatives and your own experience, the essay has explored this question, based on my own educational principles and the ways in which these will underpin your professional practice in the future. The essay began by reviewing the Government policies and initiatives that are relevant to the research question, discussing, in particular, the document  Excellence and Enjoyment – A Strategy for Primary Schools  (DfES, 2003) and the subsequent Primary Strategy framework for primary education. The essay then moved on to discuss the aims of these policies and initiatives and the implications these have had for schools and teachers. The assessment framework was then discussed, and how this impacts on teacher effectiveness was also noted. The essay then moved on to looking at the qualities of effective teachers, and effective t eaching in a primary setting, and concluded that some of the facets of Government policies and initiatives – such as continual assessments – run counter to my ethos of effective teaching and actually serve as little other than distractions from pure teaching time, through all the administration such assessments bring and the amount of time this takes away from lesson planning, for example. The main conclusion to the essay is that effective teaching at the primary level should serve to instill a sense of the wonder of learning and should open children’s minds to the possibilities that learning, and the learning process, encompasses. I converge with Allen and Ainley (2007) that the current trend towards assessments, more assessments and yet more assessments is not healthy for children, because it causes stress and can initiate a sense of failure in children who do not achieve high scores on these assessments and also because managing these assessments takes time away from teaching, through all the administration that the tests generate. The argument that these tests do little than to confirm that the education policies the Government is espousing are correct seems valid, and it is, as has been seen, in any case questionable that the standards suggested by the Government, in the Primary Strategy are actually leading to rises in standards (see Tymms, 2004). That the overall stated ethos of the new Primary Strategy framework is that an excellent education is an education that is tailored to children’s specific needs, allowing them to engage with the educational process and giving them the start they need to be able to succeed in the context of secondary education (DfCSF, 2008) is thus a good basis to begin, as an effective teacher, but, in order to avoid boredom in the education process, and psychological problems, due to the huge amount of testing and assessment primary children are subject to, effective teaching not only needs to teach the Curriculum and prepare children for the battery of tests and assessments they will be subjected to, but also needs to foster the empowerment of children, through developing a sense of the wonder of learning and empowering the children to direct their own learning, within the context of the Curriculum, so that children feel they are capable of learning and are capable of achieving the standards they set themselves. Effective teachers are thus not only bound by the dictates of Government policy and teaching research which suggests  how  teachers should teach, but they are, in my opinion, also bound by a responsibility to children, to instill in children a sense of the wonder of learning. In my opinion, and something I will endeavour to achieve in my teaching practice, this sense of wonder can be best achieved through empowering children to realise their potential and to realise they can achieve their goals, through fostering a love of learning. These qualities not only make for an effective teacher but also an inspiring teacher, who will inspire their pupils to want to learn. References AAIA (2007). Assessing children’s attainments in the foundation stage: guidance produced by the AAIA. Available from  http://www.aaia.org.uk/PDF/FAQs%20-%20assessing%20children’s%20attainment%20in%20the%20foundation%20stage.pdf  [Accessed on 29th  February 2008]. Alexander, R. (2004). Still no pedagogy? Principle, pragmatism and compliance in primary education.  Cambridge J. of Education  34(1), pp.7-33. Allen, M Ainley P (2007).  Education make you fick, innit?  Tufnell Press, Reading. Brown, M.  et al.  (1998). Is the National Numeracy strategy research-based?  Brit. J. Educ. Studies  46, pp.362-385. Cohen, L., Manion, L. and Morrison, K. (2004).  A guide to teaching practice.  Routledge Falmer. DfCSF (2008). The National Strategies: Primary. Available from  http://www.standards.dfes.gov.uk/primary/about/  [Accessed on 29th  February 2008]. DfEE (1998).  Teachers: meeting the challenge of change.  London: DfEE. DfEE (2000). Curriculum guidance for K1 stage. Available from  http://www.standards.dcsf.gov.uk/eyfs/resources/downloads/5585_cg_foundation_stage.pdf  [Accessed 29th February 2008]. DfES (2003).  Excellence and enjoyment: a strategy for primary schools.  London: DfES. DfES (2004).  Department for Education and Skills: five year strategy for children and learners.  London: DfES. Eaude, T (2006).  Children’s spiritual, moral, social and cultural development.  Learning Matters, Reading. Fabian, H. and Dunlop, A-W. (2002).  Transitions in the early years: debating continuity and progression for children in early education.  Routledge Falmer. Higgins, S.  et al.  (2002).  Thinking through primary teaching.  Chris Kington Publishing, Cambridge. Kyriacou, C. (1997).  Effective teaching in schools. Nelson Thornes Ltd. Kyriacou, C. (1999).  Essential teaching skills.  Nelson Thornes Ltd. Kyriacou, C. (2005). The impact of daily maths lessons in England on pupil confidence and competence in early mathematics: a systematic review.  Brit J Educ Studies  53(2), pp.168-186. Morrison, G.S. (2000).  Fundamentals of Early Childhood Education.  Prentice Hall. NAA (National Assessment Agency) (2007). Additional guidance on completing foundation stage profile assessments. Available from  http://www.naa.org.uk/downloads/FSP_factsheet-_2007_Guidance_LA_Completing_Foundation_v042.pdf  [Accessed 29th February 2008]. Ofsted (2003). The national literacy and numeracy strategies and the primary curriculum. Parliamentary Office of Science and Technology (2000).  Report on early years learning.  London: Parliamentary Office of Science and Technology. Pollard, A (2002).  Readings  for Reflective Teaching Continuum. QCA (Qualifications and Curriculum Authority) (1999).  Early learning goals.  London: QCA. QCA (Qualifications and Curriculum Authority) (2000).  Curriculum guidance for the foundation stage. .  London: QCA. QCA (Qualifications and Curriculum Authority) (2001).  Planning for learning in the foundation stage. .  London: QCA. Siraj-Blatchford, I, Sylva, K, Taggart, B, Melhuish, E., Sammons, P, Elliot, K. The EPPE Project [1997-2003] Available from http://www.teachernet.gov.uk/teachers/  issue34/secondary/features/steppingup www.standards.dfes.gov.uk/schemes2/  ks1-2citizenship/cit1/2 [Accessed on 29th  February 2008]. Springate, D (2004).  Democracy in Schools: Some European perspectives. Springate, D (2006).  Empowering Children Through their own Research. Taylor, W. and Hayes, D (2004).  The RoutledgeFalmer Guide to Key Debates in Education.  RoutledgeFalmer. Tymms, P. (2004). Are standards rising in English primary schools?  Brit Educ Res J  30(4), pp.477-494. Webb, R.  et al.  (2004). A comparative analysis of primary teacher professionalism in England and Finland.  Comp Educ  40(1), pp.83-107. Webb, R. and Vulliamy, G. (2006). The impact of New Labour’s education policy on teadhers and teaching at Key Stage 2.  FORUM  48(2), pp.145-158. Wilce, H (2007).  Nurture Groups: Can they prevent bad behaviour in the classroom?

Lower Levels of Convictions of Woman for Criminal Offences Essay

Assess explanations that sociologists have offered for lower levels of convictions of woman for criminal offences Sociologists have offered explanations for lower levels of convictions for woman for criminal offences. For example women are treated more leniently and woman are socialised to commit less crime in the first place. Most crime appears to be committed by men. According to recent national statistics men are four times more likely to commit a crime than woman. For example official statistics suggests there are gender differences in the types of crimes committed such as men committee violent crime and woman committee shoplifting. One explanation for the lower levels of convictions of women is that they are treated more leniently. One explanation that has been put forward is that the agents of criminal justice such as police officers, magistrates and judges are men and men are socialised to act in a chivalrous manner towards women. In the 1950s Pollack argued that men felt they had to protect women, so the criminal justice system is more lenient towards them. So there are some crimes that are less likely to turn into official statistics. This then does not give an accurate understanding in rates of offending and official statistics will show the extent of gender differences. Women are also more likely than men to be cautioned rather than prosecuted. For example, the Ministry of justice figures for 2009, show that 49% of female offenders were cautioned compared to only 30% of men. Similarly Hood’s study of over 3,000 defendants found that woman were about one-third less likely to be jailed in similar cases. However, there is evidence against the chivalry thesis. Box argues women who commit serious offences are not treated more favourably than men. He argues women show remorse which may be why they get cautioned rather than going to court. Heidensohn argues that in fact when women commit more serious crimes and deviate from expected norms of behaviour they are punished more harshly. For example stereotypical gender roles influence judges decisions. Heidensohn accepts there has been an increase in female crime but it’s due to poverty and being socially marginalised. Walklate argues that in rape case it is often not the defendant who is on trial but the victim. Steffensmeier argues that women are treated more leniently in court because judges are reluctant to separate woman from their children. Another reason that could explain lower conviction rates for women is that they are socialised to commit less crime in the first place. Functionalists argue that lower levels of females crime can be explained by gender role socialisation and the expressive role that Parsons argues is the one that women take on within the home. While men go out to work and take the instrumental role as provider, women are socialised to be gentle and nurturing. Parsons argues that boys reject feminine role models they engage in what Parson calls compensatory compulsory masculinity and become aggressive and anti-social behaviour which can slip over into an act of delinquency. Cohen argues that boys that are more likely to join gangs to gain stats and identity. Whereas the girls have their mothers as role models which means they are less likely to behave in anti-social ways. However, critics of the sex role theory are that this view is dated and boys and girls have different role models and influences in their life to the ones illustrated by Parsons. Walklate criticises sex role theory for its biological assumptions. Parson assumes that women are best suited to the expressive role as women gave birth to children. Feminists are interested in how patriarchal society controls women, and this control might explain lower levels of criminality and therefore lower conviction rates. Heidensohn argues the most different thing about women’s behaviour is how conformist it is as women commit fewer crimes than men. Heidensohn notes that women are controlled in a number of ways. For example women are controlled by the amount of time they have to spend looking aft er home and children. Also in the public domain women are controlled by the threat of male violence especially sexual violence. The Islington Crime Survey found that 54% of women avoided going out alone. This therefore reduces their opportunity to offend. However Carlen explains how some women commit crime when they are let down by patriarchal society. It is the failure of patriarchal society to deliver the promised deals that removes the controls. Cohen argues that working class women are led to conform by what she calls the class deal which is being offered rewards at work that allow a good standard of living. Also the gender deal where women have rewards from family life by living a normal domestic gender role. Critics argue that this underplays the importance of free will and choice in offending. Adler argues that as society changes so women may turn to crime. He uses the liberation thesis to argue that as women become liberated from patriarchy female crime rates will rise. As society changes so too have women’s roles within it. There is evidence to support this for example female offending rates have risen. However, critics argue that female crime rates started growing in the 1950s before the women’s liberation movement. In conclusion, the main reason why there are lower levels of convictions of women is because the criminal justice system is more lenient towards women. This is due to men protecting the women and official statistics show that men are more likely to commit crime than women.

Conclusion and managerial implications Essay

A streak is a short period of good or bad luck. A team is said to have a winning streak when it wins many games consecutively, and to have a loosing streak when it looses many matches in a row. It is quite easy to say that a team has good players, and therefore has a high chance of winning. Upon closer consideration, though, it may become apparent that the skill and style of play of the teams playing against them has an important part to play, and so are other factors like coaching and the spirit in the players. In this work, we have considered some variables that appear likely to influence the team’s chance of winning. Specifically, we chose opponent 3-points per game, team 3-points per game, team free throws per game, team turnovers per game, opponent turnovers per game, team rebounds per game and opponent rebounds per game as key determining variables in determining the winning chance of a basketball team. We had to deal with the occurrence unusually large or small values in the data, since they affect the final outcome. Therefore we formed a multiple regression model for prediction, and modified it until we came up with a model with six variables. Our model can be trusted to predict the chance of a team winning by up to 80%, and the percentage win can be predicted with an error margin 0. 1479 percentage points about 95% of the time. Our model showed us that the more turnovers a team has and the more rebounds from an opponent, the less the chance of winning. However, the more 3-point shots, free throws and rebounds made, and the more turnovers an opponent makes, the greater a team’s chance of winning. 3 TABLE OF CONTENTS Executive summary 2 Objective of the study 4 Data description 5 Technical report 6 – 12 Conclusion and managerial implications 14 Appendices Appendix I: Descriptive statistics for the variables 15 Appendix II: Box plots for the variables 16 Appendix III: Scatter plots, winning chance vs. each variable 17 Appendix IV: Multiple regression details for 8-variable model 20 Appendix V: Residual plots for the 8 variables 21 Appendix VI: Best subsets regression details 23 Appendix VII: Regression details for 5-variable model 24. Appendix VIII: Residual Plots for 5 variables 26 Appendix IX: Regression excluding residual outliers for 5-variable model 28 Appendix X: Regression for 6-variable model 29 Appendix XI: Residual plots for 6-variable model 30 Appendix XII: (a) The final regression model 32 Appendix XII: (b) Residual plots for the final regression model 33 4 OBJECTIVE OF THE STUDY The objective of his study is to create a regression model for predicting the percentage wining of a basketball team among many basketball teams in a particular basketball season. Regression analysis is a method that aids us in predicting the outcome of a variable, given the values of one or more other (independent) variables. The model thus obtained is examined to ascertain the reliability of its prediction. In our analysis, therefore, we are out to examine a multiple regression model that we shall build, and improve on it until we find the best model for the job. We are motivated by the fact that fans of teams every now and then go into arguments (and even betting) about what chance there is for a particular team to win. Winning a game, we believe, is not entirely a chance occurrence. We therefore want to investigate what factors can be expected to determine the winning chance of a team. We do not expect to get a magical model, but that we will have to modify our model until its predictive ability has been greatly improved. The importance of this work lies in the fact that, without accurate knowledge of the most influential factors affecting a phenomenon, one may end up spending a lot of resources (time, energy and money) on a factor that might not be so important, at the expense of the really important factors. This results in a lot of input with no corresponding output, thereby leading to frustration. This can be especially true in sports and related activities. This work is our little contribution to more efficient planning and sport outing for a basketball team. 5 DATA DESCRIPTION The data that we have used is taken from †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ It presents the statistics for sixty-eight (68) teams in a sporting season. Therefore we shall not be going into issues of time series or other techniques that come into play when dealing with data that has been collected over an extended period. The data presents a list of 68 basketball teams. Each team has played a number of games in a particular basketball sporting season. The spreadsheet contains a lot of information on these 68 teams, such as their winning percentage and vital statistics of the games played in this particular season. In this work, we are going to designate a dependent variable (Y) and seven independent variables (X1, X2, X3, X4, X5, X6 and X7). The variables are defined as follows: Y = Winning Percentage X1 = Opponent’s 3-point per game X2 = Team’s 3-point per game X3 = Team’s free throws pr game X4 = Team’s turnover per game X5 = Opponent’s turnover per game X6 = Team’s rebound per game X7 = Opponent’s rebound per game With the above variables, we shall formulate a regression model for the winning percentage of a team in this data. 6 TECHNICAL REPORT 6. 1 Preliminaries Our first task, having obtained the data, is to examine the descriptive statistics for each of our independent variables. The Minitab result is presented in Appendix I. The data appears to be normally distributed, since the mean and median are close. To further verify this, we will look at the box plots for each of the variables. The box plots reveal that the data is normally distributed, except for â€Å"turnover per game† and â€Å"opponent turnover per game† with one outlier each, and â€Å"home rebound per game† with three outliers. The Box plots are presented in Appendix II. To further understand our data, we still look at the scatter plots of each variable against the winning percentage. This will show us the extent to which each of then influence the winning percentage. Although this is not the final regression model, it presents us with marginal regression relationships between each variable and the winning percentage. The details of the results are presented in Appendix III. The marginal regressions reveal that some of the variables are more influential to the winning percentage than others, but we note that this is not the final regression model yet. On close examination, we observe that Opponent’s 3-point per game accounts for very little of the chances of winning a game, and in fact is negatively correlated with percentage wins of a team. A similar case arises concerning Team’s turnover per game, only that the relationship is even weaker here. The same goes for Team’s rebound per game. The rest exhibit a positive correlation. The strongest correlation observable from the scatter plots is that of Team’s free throws per game, and the weakest positive correlation is that of Opponent’s turnover per game. 6. 2 6. 4. 1 7 Regression analysis is a very useful analysis tool. Moreover, with the aid of modern computers, data analysis is even easier (and sometimes fun) to carry out. The final model we have been able to come up with will help in predicting the winning chance of a basketball team. We would like to state here that our model does not have magical powers of prediction. The predictive accuracy of the model has been stated in the body of this work, and shows us that it does not incorporate EVERY variable that affects the winning chance of a team. It is common knowledge that factors like the co-operation between team management and players, relationship among players, the individual skills of the players and the support of a team’s fans play a very important role in a team’s ability to win a game, and so do many other factors. Yet these factors cannot be quantitatively described so as to be included in the model. Nevertheless, we believe that the variables we have analyzed have very important roles to play, and therefore should not be ignored. We therefore recommend, based on our findings, that a team should strategize its game so as to minimize their turnovers, since from our model they have the strongest negative effect on their winning chance. Similarly, the opponent’s rebound will do damage. On the other hand, a basketball team should, as much as possible, maximize their 3-point shots, free throws, rebounds and the opponent’s turnovers, since according to our model, these have a positive influence on their winning chance. Finally to the sports fan, you can know what to expect from a team if you can observe the above-mentioned variables. So, instead of raising your heart rate in blind anticipation, you can assess for yourself the chance that your favorite team will not let you down. In the meantime, we wish you the best of luck! 8 APPENDIXES 8. 1 APPENDIX I: Descriptive Statistics for the variables 1. Descriptive Statistics Variable N N* Mean SE Mean StDev Variance Minimum Winning percentage 68 0 0. 5946 0. 0197 0. 1625 0. 0264 0. 2333 Opp 3-point per game 68 0 6. 318 0. 107 0. 880 0. 774 3. 788 3-point per game 68 0 6. 478 0. 161 1. 326 1. 757 3. 645 Free throws per game 68 0 14. 203 0. 280 2. 307 5. 323 8. 536 Turn-over, pg 68 0 14. 086 0. 164 1. 355 1. 835 10. 974 Opponent Turn-over,pg 68 0 14. 755 0. 192 1. 583 2. 506 11. 438 Home rebound per game 68 0 35. 380 0. 389 3. 209 10. 297 27. 323 Oppnt rebound per game 68 0 33. 841 0. 258 2. 128 4. 528 28. 970 Variable Q1 Median Q3 Maximum Range IQR Winning percentage 0. 4707 0. 5938 0. 7403 0. 9487 0. 7154 0. 2696 Opp 3-point per game 5. 688 6. 323 6. 956 8. 138 4. 350 1. 268 3-point per game 5. 782 6. 433 7. 413 9. 471 5. 825 1. 631 Free throws per game 12. 619 14. 322 15. 883 19. 568 11. 032 3. 264 Turn-over, pg 13. 116 14. 000 14. 875 17. 656 6. 682 1. 759 Opponent Turn-over,pg 13. 574 14. 769 15. 514 18. 406 6. 969 1. 939 Home rebound per game 33. 304 35. 383 37. 063 45. 548 18. 226 3. 758 Oppnt rebound per game 32. 611 33. 754 35. 047 39. 938 10. 968 2. 436 2. Descriptive Statistics: Winning percentage Variable N N* Mean SE Mean StDev Minimum Q1 Median Winning percentage 68 0 0. 5946 0. 0197 0. 1625 0. 2333 0. 4707 0. 5938 Variable Q3 Maximum IQR Variance Range Winning percentage 0. 7403 0. 9487 0. 2696 0. 026 o. 7154 8. 2 APPENDIX II: Box Plots for the variables 8. 3 APPENDIX III: Scatter Plots (With Corresponding Regression Equations) Regression Analysis: Winning percentage versus Opp 3-point per game The regression equation is Winning percentage = 0. 729 – 0. 0212 Opp 3-point per game S = 0. 162686 R-Sq = 1. 3% R-Sq(adj) = 0. 0% Regression Analysis: Winning percentage versus 3-point per game The regression equation is Winning percentage = 0. 397 + 0. 0304 3-point per game S = 0. 158646 R-Sq = 6. 2% R-Sq(adj) = 4. 7% Regression Analysis: Winning percentage versus Free throws per game The regression equation is Winning percentage = 0. 058 + 0. 0378 Free throws per game S = 0. 138185 R-Sq = 28. 8% R-Sq(adj) = 27. 7% Regression Analysis: Winning percentage versus Turn-over, pg The regression equation is Winning percentage = 1. 14 – 0. 0387 Turn-over, pg S = 0. 155019 R-Sq = 10. 4% R-Sq(adj) = 9. 0% Regression Analysis: Winning percentage versus Opponent Turn-over,pg The regression equation is Winning percentage = 0. 293 + 0. 0204 Opponent Turn-over,pg S = 0. 160503 R-Sq = 4. 0% R-Sq(adj) = 2. 5% Regression Analysis: Winning percentage versus Home rebound per game The regression equation is Winning percentage = – 0. 243 + 0. 0237 Home rebound per game S = 0. 144773 R-Sq = 21. 9% R-Sq(adj) = 20. 7% Regression Analysis: Winning percentage versus Oppnt rebound per game The regression equation is Winning percentage = 1. 44 – 0. 0249 Oppnt rebound per game S = 0. 154803 R-Sq = 10. 7% R-Sq(adj) = 9. 3% 8. 4 APPENDIX IV: Multiple Regression Details Regression Analysis: Winning perc versus 3-point per , Free throws , †¦ The regression equation is Winning percentage = 0. 633 + 0. 0224 3-point per game + 0. 0176 Free throws per game – 0. 0622 Turn-over, pg + 0. 0414 Opponent Turn-over,pg + 0. 0267 Home rebound per game – 0. 0296 Oppnt rebound per game – 0. 0172 Opp 3-point per game Predictor Coef SE Coef T P Constant 0. 6327 0. 2123 2. 98 0. 004 3-point per game 0. 022369 0. 007221 3. 10 0. 003 Free throws per game 0. 017604 0. 005720 3. 08 0. 003 Turn-over, pg -0. 062214 0. 007380 -8. 43 0. 000 Opponent Turn-over,pg 0. 041398 0. 006398 6. 47 0. 000 Home rebound per game 0. 026699 0. 004175 6. 39 0. 000 Oppnt rebound per game -0. 029645 0. 004594 -6. 45 0. 000 Opp 3-point per game -0. 01724 0. 01130 -1. 53 0. 132 S = 0. 0747588 R-Sq = 81. 1% R-Sq(adj) = 78. 8% Analysis of Variance Source DF SS MS F P Regression 7 1. 43486 0. 20498 36. 68 0. 000 Residual Error 60 0. 33533 0. 00559 Total 67 1. 77019 Source DF Seq SS 3-point per game 1 0. 10906 Free throws per game 1 0. 53614 Turn-over, pg 1 0. 24618 Opponent Turn-over,pg 1 0. 13117 Home rebound per game 1 0. 13403 Oppnt rebound per game 1 0. 26527 Opp 3-point per game 1 0. 01302 Unusual Observations 3-point Winning Obs per game percentage Fit SE Fit Residual St Resid 2 4. 59 0. 79412 0. 63575 0. 02114 0. 15837 2. 21R 27 6. 60 0. 76667 0. 60456 0. 01272 0. 16211 2. 20R 30 6. 21 0. 50000 0. 65441 0. 01571 -0. 15441 -2. 11R 45 4. 75 0. 25000 0. 39253 0. 02404 -0. 14253 -2. 01R R denotes an observation with a large standardized residual. 8. 5 APPENDIX V: Residuals plots for the 8 variables 8. 6 APPENDIX VI: Best Subsets Regression Best Subsets Regression: Winning perc versus Opp 3-point , 3-point per , †¦ Response is Winning percentage O O H p O F p o p p r p m n p e o e t e n 3 3 e r r – – t n e e p p h t b b o o r T o o i i o u T u u n n w r u n n t t s n r d d – n p p p o – p p e e e v o e e r r r e v r r r e g g g , r g g a a a , a a Mallows m m m p p m m. Vars R-Sq R-Sq(adj) Cp S e e e g g e e 1 28. 8 27. 7 161. 5 0. 13818 X 1 21. 9 20. 7 183. 5 0. 14477 X 2 46. 9 45. 3 106. 1 0. 12021 X X 2 41. 2 39. 4 124. 4 0. 12658 X X 3 55. 2 53. 1 81. 7 0. 11126 X X X 3 54. 9 52. 8 82. 9 0. 11172 X X X 4 73. 8 72. 2 24. 9 0. 085772 X X X X 4 65. 1 62. 9 52. 4 0. 098958 X X X X 5 77. 7 75. 9 14. 6 0. 079790 X X X X X 5 76. 8 74. 9 17. 6 0. 081431 X X X X X. 6 80. 3 78. 4 8. 3 0. 075569 X X X X X X 6 78. 1 75. 9 15. 5 0. 079781 X X X X X X 7 81. 1 78. 8 8. 0 0. 074759 X X X X X X X 8. 7 APPENDIX VII: Regression Analysis with Five Variables Regression Analysis The regression equation is Winning percentage = 0. 528 + 0. 0250 3-point per game – 0. 0631 Turn-over, pg + 0. 0471 Opponent Turn-over,pg + 0. 0349 Home rebound per game – 0. 0336 Oppnt rebound per game Predictor Coef SE Coef T P Constant 0. 5280 0. 2213 2. 39 0. 020 3-point per game 0.025031 0. 007617 3. 29 0. 002. Turn-over, pg -0. 063103 0. 007859 -8. 03 0. 000 Opponent Turn-over,pg 0. 047061 0. 006531 7. 21 0. 000 Home rebound per game 0. 034908 0. 003176 10. 99 0. 000 Oppnt rebound per game -0. 033572 0. 004713 -7. 12 0. 000 S = 0. 0797903 R-Sq = 77. 7% R-Sq(adj) = 75. 9% Analysis of Variance Source DF SS MS F P Regression 5 1. 37547 0. 27509 43. 21 0. 000 Residual Error 62 0. 39472 0. 00637 Total 67 1. 77019 Source DF Seq SS 3-point per game 1 0. 10906. Turn-over, pg 1 0. 13137 Opponent Turn-over,pg 1 0. 15696 Home rebound per game 1 0. 65508 Oppnt rebound per game 1 0. 32300 Unusual Observations 3-point Winning Obs per game percentage Fit SE Fit Residual St Resid 8 4. 13 0. 83333 0. 66281 0. 02375 0. 17053 2. 24R 13 6. 79 0. 55172 0. 72095 0. 02073 -0. 16923 -2. 20R 27 6. 60 0. 76667 0. 60253 0. 01331 0. 16414 2. 09R 30 6. 21 0. 50000 0. 66321 0. 01474 -0. 16321 -2. 08R 45 4. 75 0. 25000 0. 41575 0. 02187 -0. 16575 -2. 16R. R denotes an observation with a large standardized residual. APPENDIX VII (Continued): Descriptive Statistics for five Variables Descriptive Statistics Variable N N* Mean SE Mean StDev Variance Minimum Winning percentage 68 0 0. 5946 0. 0197 0. 1625 0. 0264 0. 2333 3-point per game 68 0 6. 478 0. 161 1. 326 1. 757 3. 645 Turn-over, pg 68 0 14. 086 0. 164 1. 355 1. 835 10. 974 Opponent Turn-over,pg 68 0 14. 755 0. 192 1. 583 2. 506 11. 438 Home rebound per game 68 0 35. 380 0. 389 3. 209 10. 297 27. 323 Oppnt rebound per game 68 0 33. 841 0. 258 2. 128 4. 528 28. 970 Variable Q1 Median Q3 Maximum Range IQR Winning percentage 0. 4707 0. 5938 0. 7403 0. 9487 0. 7154 0. 2696 3-point per game 5. 782 6. 433 7. 413 9. 471 5. 825 1. 631 Turn-over, pg 13. 116 14. 000 14. 875 17. 656 6. 682 1. 759 Opponent Turn-over,pg 13. 574 14. 769 15. 514 18. 406 6. 969 1. 939 Home rebound per game 33. 304 35. 383 37. 063 45. 548 18. 226 3. 758 Oppnt rebound per game 32. 611 33. 754 35. 047 39.938 10. 968 2. 436 8. 8. APPENDIX VIII: Residual Plots for 5 variables 8. 9 APPENDIX IX: Regression Excluding Residual Outliers Regression Analysis: The regression equation is Winning percentage = 0. 487 + 0. 0184 Free throws per game + 0. 0240 Opponent Turn-over,pg + 0. 0188 Home rebound per game – 0. 0303 Oppnt rebound per game – 0. 0243 Opp 3-point per game Predictor Coef SE Coef T P Constant 0. 4873 0. 2956 1. 65 0. 105 Free throws per game 0. 018444 0. 009412 1. 96 0. 055 Opponent Turn-over,pg 0. 024021 0. 009784 2. 46 0. 017 Home rebound per game 0. 018835 0. 006555 2. 87 0. 006 Oppnt rebound per game -0. 030258 0. 007625 -3. 97 0. 000 Opp 3-point per game -0. 02428 0. 02129 -1. 14 0. 259 S = 0. 118905 R-Sq = 49. 8% R-Sq(adj) = 45. 7% Analysis of Variance Source DF SS MS F P Regression 5 0. 84309 0. 16862 11. 93 0. 000 Residual Error 60 0. 84831 0. 01414 Total 65 1. 69140 Source DF Seq SS Free throws per game 1 0. 47458 Opponent Turn-over,pg 1 0. 03295 Home rebound per game 1 0. 04175 Oppnt rebound per game 1 0. 27543 Opp 3-point per game 1 0. 01839 Unusual Observations Free throws Winning Obs per game percentage Fit SE Fit Residual St Resid 12 12. 2 0. 3333 0. 5854 0. 0270 -0. 2521 -2. 18R 34 12. 2 0. 9487 0. 6218 0. 0297 0. 3269 2. 84R 42 14. 5 0. 2333 0. 5227 0. 0400 -0. 2893 -2. 58R 43 12. 5 0. 2500 0. 4925 0. 0367 -0. 2425 -2. 14R R denotes an observation with a large standardized residual. 8. 10 APPENDIX X: Regression with 6 Variables Regression Analysis: Winning perc versus 3-point per , Free throws , †¦ The regression equation is Winning percentage = 0. 565 + 0. 0239 3-point per game + 0. 0163 Free throws per game – 0. 0630 Turn-over, pg + 0. 0436 Opponent Turn-over,pg + 0. 0265 Home rebound per game – 0. 0310 Oppnt rebound per game Predictor Coef SE Coef T P Constant 0. 5654 0. 2100 2. 69 0. 009 3-point per game 0. 023949 0. 007224 3. 32 0. 002 Free throws per game 0. 016290 0. 005717 2. 85 0. 006 Turn-over, pg -0. 062984 0. 007443 -8. 46 0. 000 Opponent Turn-over,pg 0. 043571 0. 006305 6. 91 0. 000 Home rebound per game 0. 026482 0. 004218 6. 28 0. 000 Oppnt rebound per game -0. 031028 0. 004552 -6. 82 0. 000 S = 0. 0755690 R-Sq = 80. 3% R-Sq(adj) = 78. 4% Analysis of Variance Source DF SS MS F P Regression 6 1. 42184 0. 23697 41. 50 0. 000 Residual Error 61 0. 34835 0. 00571 Total 67 1. 77019 Source DF Seq SS 3-point per game 1 0. 10906 Free throws per game 1 0. 53614 Turn-over, pg 1 0. 24618 Opponent Turn-over,pg 1 0. 13117 Home rebound per game 1 0. 13403. Oppnt rebound per game 1 0. 26527 Unusual Observations 3-point Winning Obs per game percentage Fit SE Fit Residual St Resid 27 6. 60 0. 76667 0. 60084 0. 01262 0. 16582 2. 23R 44 6. 03 0. 23333 0. 38536 0. 02559 -0. 15202 -2. 14R 45 4. 75 0. 25000 0. 41158 0. 02076 -0. 16158 -2. 22R R denotes an observation with a large standardized residual. 8. 11 APPENDIX XI: Residual Plots for the 6-variable Model 8. 12 APPENDIX XII (a): The Final Regression Model. Regression Analysis: Winning perc versus 3-point per , Free throws , †¦ The regression equation is Winning percentage = 0. 604 + 0. 0226 3-point per game + 0. 0167 Free throws per game – 0. 0660 Turn-over, pg + 0. 0420 Opponent Turn-over,pg + 0. 0256 Home rebound per game – 0. 0292 Oppnt rebound per game Predictor Coef SE Coef T P Constant 0. 6038 0. 2065 2. 92 0. 005 3-point per game 0. 022564 0. 007108 3. 17 0. 002 Free throws per game 0. 016706 0. 005600 2. 98 0. 004 Turn-over, pg -0. 066016 0. 007456 -8. 85 0. 000 Opponent Turn-over,pg 0. 041969 0. 006229 6. 74 0. 000 Home rebound per game 0. 025649 0. 004152 6. 18 0. 000 Oppnt rebound per game -0. 029173 0. 004561 -6. 40 0. 000 S = 0. 0739739 R-Sq = 80. 8% R-Sq(adj) = 78. 8% Analysis of Variance Source DF SS MS F P Regression 6 1. 37853 0. 22976 41. 99 0. 000 Residual Error 60 0. 32833 0. 00547 Total 66 1. 70686 Source DF Seq SS 3-point per game 1 0. 10202 Free throws per game 1 0. 50620 Turn-over, pg 1 0. 30758 Opponent Turn-over,pg 1 0. 11512 Home rebound per game 1 0. 12372. Oppnt rebound per game 1 0. 22390 Unusual Observations 3-point Winning Obs per game percentage Fit SE Fit Residual St Resid 26 6. 60 0. 76667 0. 60237 0. 01238 0. 16429 2. 25R 29 6. 21 0. 50000 0. 64694 0. 01477 -0. 14694 -2. 03R 43 6. 03 0. 23333 0. 38546 0. 02505 -0. 15213 -2. 19R 44 4. 75 0. 25000 0. 41580 0. 02045 -0. 16580 -2. 33R R denotes an observation with a large standardized residual. APPENDIX XII (b): Residual Plots for the final regression model. APPENDIXXII (b): Continued REFERENCES Please state the source of data here.

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