Womens Leisure Essay Example | Topics and Well Written Essays - 750 words

Womens Leisure - Essay Example However, derby is popularly known as a type of rough contact sport. This is the reason why both male and female players may allow themselves some form of violence, roughness or even cheating strategies if they believe they have to win. At my school, athletes are perceived as physically healthy individuals and ones who are popular among members of the opposite sex. Moreover, the girls’ and boys’ athletic programs are usually perceived as a way for athletes to improve not only their physical health but also their self-esteem and popularity. Personally, I watch men’s professional sports more than women’s, especially basketball as NBA is more exciting to me than WNBA. It is hard to say why but as a man who is into basketball myself, I would usually prefer watching men play sports than women do it. Perhaps, it is the relatively higher level of aggression in men’s professional sports that makes them widely watched compared to women’s. Nevertheless, occasionally I am amazed whenever I see a woman doing a man’s job, especially when professional female athletes get to be really rough on the field, in the court or in the ring. Sports are basically all about competition, and so people are expecting a good and rough fight. This is akin to something that will maintain the adrenalin rush. This is also the same feeling the Romans perhaps once had while watching gladiators kill each other at the bottom of the Colosseum more than two thousand years ago. When it comes to the movie Whip It, Bliss seems to show doubt about trying out for the derby team because she herself is an indecisive woman with no idea about what the future would bring her. Moreover, she does not believe her mother Brooke would be proud of her if she played derby. Brooke wants her to be a beauty contestant like her in the past. Moreover, Bliss may be hesitant at attempting to do derby. This is because she knew that in order to be a professional derby player, on e has to be extra tough although one is female. She may have observed this since the first time she saw the Hurl Scouts and Holly Rollers played. Bliss and Brooke are very different from each other when it comes to leisure and recreation. Brooke likes shopping and is into beauty contests and other activities that bring out the refined woman in her. She views herself as usually right in her decisions, opinions and perspectives. Nevertheless, she shows her humility and love as a mother. This is evident when she tells Bliss that the latter need not join the Blue Bonnet pageant if she is only doing it for her mother. On the other hand, Bliss is into professional derby, men like Oliver, and something that will bring out the best in her. Bliss’ desire to bring out the best in her somehow translates as a desire to join the hurl Scouts and defeat the Holy Rollers. Little does she know that it is one way for her to prove her own worth to herself. In the process, she also indirectly pr oves to her mother that she can make decisions of her own. Nevertheless, at several instances in the movie, Bliss does not feel that she is making the right decisions. Thus, compared to her mother, who is righteous most of the time, Bliss shows some indecisiveness. Furthermore, perhaps this is the same way Bliss views herself at the beginning of the movie – a young woman with no direction. This view, however, changes in the end as her mother accepts her decision to be on the professional derby team. This is also the point where Bliss realizes her calling (Barrymore). The way I see it, Brooke is just the strict, conservative mother that mothers are supposed to be. On the other hand, Bliss is the picture of an ordinary girl who defies her own parents’

Juvenile Recidivism Essay Example | Topics and Well Written Essays - 1250 words - 1

Juvenile Recidivism - Essay Example The designing of effective assessment strategies leading to interventions with the young offenders are no doubt, predominantly dependant on the identification of causal factors that constitute the basis of a realistic prediction (Savitz et al., 1962). Adolescence is a phase of life when the young people are more prone to engaging in antisocial behavior. Practice of antisocial behavior during adolescence is the single most important factor in the prediction of criminal behavior in the adulthood. A plethora of research carried on till now has suggested that almost a half or more of juvenile offenders continue with the criminal behavior beyond their teens. Juvenile recidivism is a serious problem in the United States. On an annual basis, roughly 2.4 million juveniles are charged with criminal offences every year (Wilson, 2011: Online). As per some conservative estimates, roughly 55 percent or more of juvenile offenders who are released get rearrested within a year (Wilson, 2011: Online). In case of urban areas, the rate of juvenile recidivism is estimated to be as high as 76 percent (Wilson, 2011: Online). At a national level, it is utterly difficult to acquire the accurate recidivism rates. This is because in the US, the recidivism rates in case of juvenile offenders are assessed at a state or county level (Wilson, 2011: Online). Hence, the statistics specific to the particular states is in a way a realistic indicator of the levels of juvenile recidivism. In the year 2005, the rate of juvenile recidivism in the State of Washington, in case of boys stood at 77 percent and in the case of girls it stood at 72 percent (Wilson, 2011: Onli ne). In the State of California, the percentage of juvenile delinquents who got rearrested within a year was 74 (Wilson, 2011: Online). In Manhattan, the rate of juvenile recidivism rested at roughly 80 percent (Wilson, 2011: Online). Many

Merger Management of Kraft and Cadbury

Merger Management of Kraft and Cadbury Nowadays, organisations must actively develop and adapt appropriate changes that fit the dynamic environment. This highlights the importance of change management in judging how flexible and responsive an organisation is. As more companies expand and diversify, it is inevitable that smaller organisations are now looking into merger with bigger organisations to boost their share of their market. Bigger organisations are also attracted by the opportunities of entering untapped emerging markets. Therefore, the nature of change that we will be investigating in this report is merger and acquisition (MA). We will relate this change to USs Kraft Foods Incorporated, as the company recently acquired UKs Cadbury. The issues we have selected to focus on are in the following order: leadership and communication, cultural change, and staff resistance. Leadership is chosen because leadership is critical to determine the success of an acquisition. Firstly, due to the huge number of employees involved, the choice of an appropriate leadership style is vital to ensure integration in both companies is a smooth process. Next, an effective leadership will improve the productivity of both companies through the efficient allocation of resources to achieve the organizational strategic goals. Communication is chosen because the acquisition will raise doubts among Cadburys employees on their roles in the new company and its vision and goals. The feeling of uncertainty about their future could lead to a dip in their morale and low productivity at work. Thus, it is essential for Kraft to use an appropriate communication strategy to increase the awareness of the employees of any impending change initiatives. Culture is chosen because of the diverse cultures between Kraft and Cadbury. Cadbury was founded on Quaker ethics to build a socially benign business. This philosophy helped Cadbury to build a world-class brand that is close to the hearts of the British people. In contrast, Kraft is more of a performance driven company with decades of profit driven mergers and demergers. Thus, it is essential for Kraft to identify and value the cultural differences as sources of synergy and efficiency in order to manage the acquisition process effectively. Staff resistance is chosen because management and employees made up the core structure of the organisation. The policies undertaken by these personnel and behaviour exhibited can have an immediate and vital impact on determining how organisation functions. In the horizontal acquisition of Cadbury, Kraft must also be able to seek equilibrium for integrating the staff from both sets of organisation. Conflicts at management levels, diverse working styles and even motivational approaches are some situations that may hinder and pose challenges. Before implementing any change initiatives, we recommend Kraft to conduct diagnosis of the three issues mentioned above. 2.0 Leadership and Communication 2.1 Definition of Leadership Koontz and ODonnell (1955, pg 17) the activity of persuading people to cooperate in the achievement of a common objective 2.1.1 Adairs Action Centred Leadership Duberly (2010) Adairs Action Centred Leadership model points out that a leader can only be successful when he is able to meet all three areas of needs task, individual and team maintenance needs- by helping his followers achieve the objectives of the common tasks, generate team dynamics and cater to the individuals needs. 2.1.2 Case-Study Rebecca Johnson (2008) The case study of Refresh Yourself illustrates Britvics use of Adairs action centred leadership to revive their company. For meeting task needs, the use of the balanced scorecard system with job descriptions provided their employees with a clearer understanding of their duties and responsibilities. With that, it helped to set the direction for employees to achieve their objectives. For meeting individual needs, the use of a performance management system with a core set of behaviours allows the employees to know how effectively they are performing relative to expectations. With a performance-based rewards system, Britvic motivated their employees by rewarding them accordingly as a form of recognition. For meeting team maintenance needs, a workshop session was conducted for the leaders of Britvic to reflect on their leadership. The insights gained proved to be very beneficial to them in their role as a leader as survey results showed that the employee engagement increased from 55% to 70% and employees confidence in leadership leapt to 72%. 2.1.3 Recommendations For meeting task needs, Kraft needs to provide a detailed road map which includes specific and clear goals, a detailed approach for achieving these goals, and a list of resources and time required to reach these goals. With that, Kraft and Cadbury can identify common goals so that the employees from both companies can work together on common projects in order for a smooth integration to occur. In the case of Kraft, the closure of Cadburys Somerdale plant led to a loss of 400 jobs. This caused the Cadburys employees morale to decline as they were fearful of further jobs losses. More importantly, this caused a loss of trust in the leadership of the organisation. Thus, Kraft should make a pledge to them that there will be no further job losses and factory closures to assure them of their job security. This form of assurance will restore their morale and bring about greater productivity within the team to meet the team maintenance needs. For meeting individual needs, Kraft should acknowledge employees achievements by offering rewards to keep them motivated. The rewards can be offered in the form of the cafeteria rewards system which offers employees the ability to choose a combination of benefits that best suit their needs. 2.1.4 Transformational Leadership Bass, B. M. (1990) There are four factors which make up a transformational leader, namely, charisma, inspiration, intellectual stimulation and individualized attention. Firstly, a transformational leader exudes charisma and instils pride in employees within the organisation. He creates vision to guide his followers and as engines of change within the organisation. They are deemed as role models. Secondly, a transformational leader communicates optimism about future goals to inspire his followers. Thirdly, he uses intellectual stimulation which encourages innovation and creativity in approaches towards identifying and solving problems. Lastly, he shows individualized attention by addressing his followers concerns and providing them with training for self-development. 2.1.5 Case-Study Catherine Monthienvichienchai (2007) The case study of Korea Ladder illustrates how Jonathan Cormack took on the role of a transformational leader. He united the two organisations behind a shared vision to inspire his employees. They were placed in groups for a discussion to help them realise the shared vision. He also fostered workplace creativity by conducting workshop sessions which were not a common practice in a Korean workforce. It was used as a form of intellectual stimulation to instil a sense of belonging in the employees. The positive feedback received from the employees proved that it was a success. He showed individualized consideration for the staff when he convinced the union that the foreign executives should not be removed as their experience and strengths possessed were essential to bring the bank to the next level. 2.1.6 Recommendations As the acquirer, Kraft needs to unite the two companies under one vision by offering communication programs that support the shared vision. The programs can be in the form of workshop sessions which creates a participative environment to foster a sense of belonging and involvement among employees. This environment would enable Kraft and the employees to work out a mutually satisfying outcome (i.e. a win-win situation). Next, Kraft must work at gaining the trust and respect of Cadburys employees to prevent the defection of talented people. Kraft can do so by bringing talents from both organisations together to work jointly on projects. This will encourage employees to focus on their similarities rather than their differences.The organisations various departments need to be restructured and processes must be redesigned to align with Krafts vision. 2.2. Communication 2.2.1 Definition of Communication Newman Summer (1977, pg 12) Communication is an exchange of facts, ideas, opinions or emotions by two or more persons. Communication Process Duberly (2010) Communication begins when the transmitter encodes a message to convey an idea, the receiver then decodes the message to achieve understanding. The context of a message refers to the non verbal cues conveyed by the transmitter to the receiver. The perceptual filter actually influences the way the message is transmitted. It ends with the receiver providing feedback to the transmitter to evaluate the effectiveness of the message. For effective communication on change, we will focus on three communication strategies. Firstly, spray and pray is used when employees are showered with a wide variety of information, and managers pray that staff will pick up what is needed to be done. Secondly, tell and sell is used when management attempts to both inform employees on changes and sell them on why they are required to the passive employees. Lastly, underscore and explore is used when management engages employees in a dialogue about the change process and seeks to identify obstacles and the misunderstandings that need to be addressed. Lastly, withhold and uphold is used when information is withheld until it is absolutely necessary to release it. 2.2.2 Case-Study Sarah Butcher (2005) The case city of Recovery plan illustrated the use of tell and sell as a communication strategy to persuade the bankers at Citigroup to be receptive to the new performance appraisal and compensation system. With regards to the revised compensation system, Citigroup need to sell to their bankers the idea that bonus payout will not be solely based on reaching financial targets but also dependent upon comprehensible understanding of the shared responsibilities. Another communication strategy adopted by Citigroup was spray and pray in educating their employees on the ethics and the banks code of conduct. It is a top down and one way communicating approach as the responsibility of communicating the acquired knowledge to the rest of the employees falls on the shoulder of 3000 senior employees at Citigroup. 2.2.3 Recommendations For effective communication, Kraft can adopt the underscore and explore method whereby they conduct a transparent and truthful dialogue with the employees to address the differences in culture and to identify any constraints for the change to be implemented. Kraft can also go beyond reassuring the Cadbury employees of their motive regarding the closure of the Somerdale plant. Since this is a two-way communicating process, the feedback generated from the active employees will be useful in resolving misunderstandings and build consensus on key issues. Kraft has to be cautious especially when dealing with redundancies. The communications, compensations, and benefits for redundant workers need to be clear and direct so as to build trust and commitment. In addition, they should also take a closer look at the effects of survivors (remaining workers) who suffered from low morale and decreasing commitment, trust and loyalty towards the organisation after the merger. Kraft can being by educating and persuading survivors of their good intentions by accounting personally for their action. On the other hand, the survivors need to be informed of their specific job roles and entitlements. Unless companies try to deal with this survivor syndrome by demonstrating to the remaining employees that the process for determining redundancies was transparent and professional, and those made redundant were treated fairly and lawfully, the general productivity and morale are less likely to be affected adversely. 3.0 Cultural change 3.1 Definition of Culture Drennan (1992) defined organisational culture as, Culture is how things are done around here. It is what is typical of the organisation, the habits, the prevailing attitudes, the grown-up pattern of accepted and expected behaviour. And as aptly phrased, culture is the key factor for making or breaking a merger and acquisition dealÂÂ ­. (Accenture, 2000) 3.1.1 Understanding Organisational Culture To better understand organisational culture, different methods are used, one being the Onion skin (Duberley 2010, modified from Schein 1992, 2004) Schein suggests three levels to organisational ÂÂ ­ structure: artefacts (e.g. language, environment, rituals), espoused values (organisations strategies, goals, philosophies) and basic underlying assumptions level (subconscious perceptions, thoughts and feelings that are the ultimate source of values and actions). Onion Skin (modified from Schein) 3.1.2 Sources of Organisational Culture The founder, organisational history, industry and size of the organisation impact its culture. (Duberley, 2010) Organisational culture is also reflected by national cultures and professional subcultures. (Hofstede, 1981) 3.1.3 Influence of National Culture The difference in various elements of a country such as languages, laws, values and attitudes will lead to one nations culture diverging significantly from another (Hofstede, 1980; Tayeb, 1989; Wilson, 1992) As an example of national culture, Adler (1997) describes Americans strong individualistic tendencies evidenced in their language such as trounced the opposition, and their human resource management based on individual knowledge and skills. (Calori and De Woot, 1994; Hemel Hempstead: A European Management Model Beyond Diversity), we understand that though organisational culture in the United Kingdom has similarities with the United States, certain significant differences include the adversarial relationships with labour, the tradition of the manager as a gifted amateur (as opposed to the professionalism of US managers) and the influence of class differences in the firm. 3.1.4 Cadburys and Krafts Culture Cadburys culture stems from its founder of Quaker origins: a paternalistic and philanthropic culture, which also focuses on the well being of groups rather than the individual. This translates to the idea that the leader knowing what is best for the organisation and its followers: (i.e. leaders as expert father figures). Cadburys culture of principled capitalism is what makes Cadbury great and it has successfully built a socially benign business. Cadbury has a loyal workforce that consists of staff and managers who have been in the organisation for a long period. Cadbury has a long tradition of high quality production and most of its factories make use of its local community, thereby ensuring that the brand itself is close to the hearts of locals. What this translates to is a family orientated and communal working atmosphere where staff takes strong pride in their work. Krafts culture is more performance oriented and is more focused on the meeting of sales and performance goals. Kraft is the quintessential traditional multinational business firm. There is a mercenary culture present; most staff and managers perform in relation to the rewards they get. Most rewards they expect are material and on an individual basis. Furthermore, Kraft is very much interested in destroying competition and seeks many alternatives and ways to ensure they are always ahead of competitors. Krafts culture is less communal and the working atmosphere is unlike a family atmosphere, in that people view each other as merely working colleagues and do not share deep friendships with one another. Staff and managers in Kraft are productive and focused in their work and most of them have a passion for business. 3.1.5 Potential of Post-Acquisition Culture Clash The acquisition will damage Cadburys current culture as the striking differences between Cadbury and Krafts culture may damage Cadbury existing successful culture of principled capitalism. This would bring about lower morale and performance and de-motivated staff. Krafts competitive and goal orientated culture might cause staff burn out amongst the Cadbury employees as they may feel insecure and uncertain. Krafts mercenary culture may create a lack of trust, which will weaken Cadbury staffs strong loyalty. By joining an American company, Cadbury risks losing UK benefits schemes to American procedures. Also, consumers may feel betrayed by the loss of its original organisational goals, resulting in a weakened brand. However, one advantage is that Cadburys shareholders will profit through the acquisition with the worlds second largest food manufacturing company. 3.1.6 Case-Study (Quote your Reference)SCB (an America-originated company) acquired and merged with KFB. SCB was successful with the merging of cultures due to effective communication, the understanding of the differences in cultures and the understanding that If you dont stretch things at all then nothing will change, but if you stretch things too far or too fast youll leave people behind. The approaches SCB used were the Conciliative, Educative and Corrosive approach, evidenced in the heavy use of consultation, training and use of networking. With this, they successfully overcame problems of national culture and cultural change such as: over-enthusiastic labour unions and differences in management styles, to name a few. What Kraft and Cadbury can learn from this is the importance of communication and flexibility in the use of approaches. 3.1.7 Recommendations Because culture is an essential element in an organisation, culture analysis should play a major role in an acquisition. Both qualitative and quantitative analysis exists in corporate culture: visual artefacts or manifestations of the organisation, the espoused values and basic assumptions (The Onion skin model) of the organisation have to be properly evaluated. This is essential to better understand the Krafts inner-workings and most importantly, its employees and their feelings toward the acquistion. The difference in national cultures of a UK in comparison to US organisation also impacts organisations greatly. Thus, Kraft should exercise patience and understanding to ensure that culture change is implemented and not resisted. (Whittle et al (1991:3) We understand that culture change is not a single event but an ongoing sequence of changes. Flexibility is important, as different stages of the change program may require appropriate approaches. 4.0 Resistance and Resentment Felt by Cadbury Employees 4.1 Definition of Individual and Change We will be illustrating the definition using case-study of the acquisition of Cadbury by Kraft. The process involves organisation undergoing a transformative change. This is so as the nature of change is large-scale and fundamental. At the same time, the effect is permanent and can be observed in the long-run. This issue raises the question of uncertainty which in turn, brought about greater anxiety. According to Schein E. et al. elaboration on Kurt Lewins(1951) Three-Step Model, anxiety can be broken down to two major forms survival and learned anxiety. The former pushes for change while the latter obstructs change. In order to integrate the change, Scheins theories suggest an inclination towards minimizing the learning anxieties. Generally, there are three areas we will be investigating with regards to the case-study. Firstly, the consideration of parochial self-interest which will need us to look into how the various groups in organisation will react upon their immediate interests in the company. With a likely shift in management, Cadbury staff will be reassigned to new job roles and positions in Kraft Inc. this may affect the individuals and probably also management. A possible reshuffling of manpower by HR will see the Cadbury staff go through a series of re-employment tests to allocate them to suitable tasks. This means that potential loss of authority and power especially for the executives and leaders of Cadbury relevant to their positions resulting in a conflict between their self-interests and the organizations interests. Therefore, the move to quit by the staff may be explained by this clash of interests. Next, the misunderstanding may be another consideration since the top management could be inconsistent in conveying their messages while middle-level managers could have failed to communicate clearly and on a constant basis to employees. They may put off the delivery of what they deemed as negative news. At the same time, employees are interpreted to be in denial and avoid the truth. They are sceptical of success and are unwilling to move out of their comfort zone to embrace change. Lastly, the tolerance for change is low as the former Cadbury staff may take huge pride in how they go about doing their work. Kraft personnel think otherwise and what they thought to be desirable for the organisation could be in contradiction with the new staff. The former Cadbury staff may reject changes while Kraft personnel likewise will stick to their ways of carrying out tasks. We will look into the Model of Stages of Psychological Reaction by Hayes and Hyde (1996) adapted from Elizabeth Kubler-Ross(1969) and see how it affects the individual change of Cadbury worker. Model of Stages of Psychological Reaction When Kraft Inc. based in the America announces the acquisition of UKs Cadbury on February 2010, there was first, feelings of shock among the stakeholders and also the employees of both companies as the ongoing speculation has been realized. Upon the acquisition of Cadbury, subsequently Kraft drew up plans to smooth the process of integration of the UK-based competitor into the confectioner family. However, at this stage the general sentiments among the people involved are denial. The employees may find it difficult to come out of this particular stage. Implementation of the changes to the different levels of organisation as Kraft begins to incorporate and find a fit for the new employees, new teams and new managers. At this stage, employees may experience depression and letting go but the main idea is that most personnel will not be able to come out of the previous stage so they will have regress or stay put in the previous phase. Thus, not all will complete this cycle. Some may progress beyond and enter the acceptance and testing stage which we believe to consist of majority to be the lower level of staff and employees while the senior management figures most probably regress or stay in the denial stage or even move on to another company highly likely for the case according to the article 120 of 165 Cadbury Staff Leave Since Krafts Takeover on webpage http://www.foodanddrinkdigital.com/sectors/food-manufacturing/120-165-cadbury-staff-leave-kraft-takeover. Case Study (Quote your reference) In January 2005, Gillette was acquired by PG. The merger resulted in around 6000 job cuts which was equal to 4% of the combined workforce of the two companies. The process of post-merger integration of these two companies faced an inevitable resistance. A number of people were told that they had joined what they thought was a long-term employer with sufficient size and famous brand. They felt that Gillette was a leader in the industry on many fronts. Not only were some Gillette staff personally unprepared for the takeover, because they often felt that their company was the better one, they also did not believe in the tremendous opportunities that were promised by top management from PG. Gillette employees had to face the unpleasant dilemma of whether moving to PG (and coming to terms with the changes) or leaving the company. PG and Gillette essentially had two different corporate cultures. Employees of Gillette hoped that Gillette, because they had been so large and successful, would be able to influence PG post-acquisition. But hoping for that was a complete utopia, as one former employee said. Gillette was absorbed into PG and very little from its culture was left, another said. Rather than change, employees voluntarily but reluctantly left the jobs they thought they would have until retirement. (Quote your Reference) In a joint press release at the time of their merger in 1997, the president of Daimler-Benz, JÃ ¼rgen Schrempp, and the president of Chrysler, Robert Eaton, declared that there will be no plant closures or layoffs as a result of the merger. However in 2000, the company announced there would be between 20000 and 40000 job cuts in the North American Chrysler division. Schrempp, by then the DaimlerChrysler president, claimed that the company was overstaffed by at least 6%. Announcing redundancies two or three years after the initial deal can cause even greater stress to the employees who thought that they were survivors. Employees are better prepared to handle the bad news straight after the transaction than two years later, after the newly merged culture has started to take shape when they thought they could rely on managements assurances regarding the merger, often accompanied, as in the Daimler/Chrysler example, with promises of no future redundancies. 4.1.2 Recommendations The negotiation and agreement approach would be much recommended. The resistance stems from Krafts inability to walk the talk of retaining the Somerdale plant in operation. On top of walking the talk, they are expected to keep behaviour consistent with messages, keep commitments and promises, and demonstrate some energy and enthusiasm regarding the change. Only then will team members feel they can take the next step of commitment. Kraft has to deploy the soft power to facilitate integration between the two cultures. The soft power provides an effective mean for Kraft to reach out to employees at a personal level. This promotes the establishment of a long-term positive relationship as empathy and communicating at employees level builds trust and strengthens their commitment to the organisation. 5.0 Conclusion In view of the acquisition of Cadbury, it is critical for Kraft to establish an effective leadership team to pave the way for a change programme. Kraft can adopt Adairs action leadership to have an overview of the three areas of need and ensure that they are met adequately. The success of the acquisition is also influenced by how well Kraft communicates to their employees on the change. Thus, Kraft should use the underscore and explore method to address the possible challenges and problems caused by culture differences. Due to the diverse cultures of Kraft and Cadbury, onion skin method can help Kraft identify the differences to prevent culture clash. With this in mind, Kraft can set directions for the management and employees to realign their styles of working to fit the companys vision. This will pave the way for Kraft to achieve their objectives and develop a strong standing corporate culture at the same time. Effective HR management may be a viable solution but in the long-run, in order to foster a harmonious relationship and spirit of unity among the management and employees. It is necessary to inculcate positive employee attitudes and promote willingness to learn with the acceptance of changes. Eventually, the organization can help staff to align their interests with those of the company so as to keep motivation high level across all departments. 6.0 Appendices 6.1 List of References References for Leadership Communication Type of Source Reference List Reference Books Bernard M. Bass, Ruth Bass (2008), Concepts of Leadership, The Bass Handbook of Leadership: Theory, Research, and Managerial Applications (4thedn), pg 17, Simon Schuster M V Rodriques(2000), The Meaning and Process of Communications, Perspectives of Communication and Communicative Competence, (1st edn), pg 12, Concept Publishing Company Bass, B. M. (1990), Organizational Dynamics, From Transactional to Transformational Leadership: Learning to Share the Vision, pg 22, Winter Case Studies Rebecca Johnson (2008), Refresh yourself, People Management Magazine, pg 32 Catherine Monthienvichienchai (2007), Climbing the Korea ladder, People Management Magazine, pg 30 Sarah Butcher (2005), Recovery plan, People Management Magazine, pg 34 Lecture Notes Duberly (2010), Change Management Lecture Notes References for Staff Resistance Type of Source Reference List Reference Books Esther Cameron Mike Green (2004,2009), Making Sense Of Change Management: A Complete Guide to the Models, Tools and Techniques of Organizational Change (2ndEdition) various Chapters Moeller S., (2009), Surviving MA: Make the Most of Your Company Being Acquired, John Wiley Sons Ltd Adolph G., Pettit J. and Sisk M., (2009), Merge Ahead: Mastering the Five Enduring Trends of Artful MA), Booz Company Inc Maginn M. D., (2007), Managing in time of change, McGraw-Hill Case Studies Gillette and Daimler Online Articles 120 of 165 Cadbury Staff Leave Since Kraft Takeover by Chris Farnell on July 29,2010 accessed on Saturday August 28, 2010 Kraft Gets Boost from Cadbury Thursday August 5, 2010 taken from The Wall Street Journal Earnings accessed on Saturday August 28, 2010 Kraft Criticised over Cadbury Factory Pledge May 26,2010 taken from BBC News Business accessed on Saturday August 28, 2010 Lecture Notes Duberly (2010), Change Management Lecture Notes Session 5 The Individual and Change Quotes Esther Cameron Mike Green (2004,2009), Making Sense Of Change Management: A Complete Guide to the Models, Tools and Techniques of Organizational Change (2ndEdition) What gets in the way of change: resistance to changeSchein E. Page 57, Kogan Page Limited, London UK

Applications of Prisms and Math :: Mathematics

Missing Figures Prisms and their Applications Introduction A prism is one or several blocks of glass, through which light passes and refracts and reflects off its straight surfaces. Prisms are used in two fundamentally different ways. One is changing the orientation, location, etc. of an image or its parts, and another is dispersing light as in a refractometers and spectrographic equipment. This project will only deal with the first use. Consider an image projected onto a screen with parallel rays of light, as opposed to an image formed by the same rays that are passed through a cubic prism (assume that the amount of light that is reflected is negligible). The rays that pass through the prism will not be refracted since the angle of refraction = sin-1(sin(0)/n) = 0, or reflected, so the images will be exactly the same. More generally, if the rays enter and leave a prism at right angles (Assuming the rays only travels through one medium while passing through the prism), the only effect on the image will be the reflection of the rays off of its surfaces. Since the law of reflection I= -I’ (Angle of incidence equals the negative of the angle of reflection) is not effected by the medium, the effect of the prism will be same as that of reflective surfaces or mirrors placed in the same location as the reflective surfaces of the prism. It follows that to understand prisms it is important to understand how mirrors can be used to change the direction of rays. Mirror Location Problem 1: Consider the following example: A horizontal ray is required to undergo a 45Â º-angle change and this has to be achieved using a mirror. We need to find how the mirror should be oriented to achieve the desired change of angle. Solution: Recall the Snell’s law which deals with refraction: sinI0 /n0 = sinI1/n1 if we define the incoming and outgoing rays ray and the normal of the refractive surface as vectors and using a property of the cross-product we can say the following Q0xM1 = |Q0||M1| sinI0 = sinI0 and also Q1xM1 = |Q1||M1| sinI1 = sinI1 thus N0 (Q0xM1)= n1 (Q1xM1) If we introduce two new vectors S0 and S1 and let them equal n0 Q0 and n1Q1 respectively we will get S0x M1 = S1xM1 or (S1-S0)xM1 = 0 this implies that (S1-S0) are parallel or anti-parallel, which means that we can define a new variable Γ which is called the astigmatic constant with S1 – S0 = ΓM1 How is useful for solving our problem? Applications of Prisms and Math :: Mathematics Missing Figures Prisms and their Applications Introduction A prism is one or several blocks of glass, through which light passes and refracts and reflects off its straight surfaces. Prisms are used in two fundamentally different ways. One is changing the orientation, location, etc. of an image or its parts, and another is dispersing light as in a refractometers and spectrographic equipment. This project will only deal with the first use. Consider an image projected onto a screen with parallel rays of light, as opposed to an image formed by the same rays that are passed through a cubic prism (assume that the amount of light that is reflected is negligible). The rays that pass through the prism will not be refracted since the angle of refraction = sin-1(sin(0)/n) = 0, or reflected, so the images will be exactly the same. More generally, if the rays enter and leave a prism at right angles (Assuming the rays only travels through one medium while passing through the prism), the only effect on the image will be the reflection of the rays off of its surfaces. Since the law of reflection I= -I’ (Angle of incidence equals the negative of the angle of reflection) is not effected by the medium, the effect of the prism will be same as that of reflective surfaces or mirrors placed in the same location as the reflective surfaces of the prism. It follows that to understand prisms it is important to understand how mirrors can be used to change the direction of rays. Mirror Location Problem 1: Consider the following example: A horizontal ray is required to undergo a 45Â º-angle change and this has to be achieved using a mirror. We need to find how the mirror should be oriented to achieve the desired change of angle. Solution: Recall the Snell’s law which deals with refraction: sinI0 /n0 = sinI1/n1 if we define the incoming and outgoing rays ray and the normal of the refractive surface as vectors and using a property of the cross-product we can say the following Q0xM1 = |Q0||M1| sinI0 = sinI0 and also Q1xM1 = |Q1||M1| sinI1 = sinI1 thus N0 (Q0xM1)= n1 (Q1xM1) If we introduce two new vectors S0 and S1 and let them equal n0 Q0 and n1Q1 respectively we will get S0x M1 = S1xM1 or (S1-S0)xM1 = 0 this implies that (S1-S0) are parallel or anti-parallel, which means that we can define a new variable Γ which is called the astigmatic constant with S1 – S0 = ΓM1 How is useful for solving our problem?

How michael porter five model affects Costco Wholesale Corporation Essay

According to Michael Porter, an industry is affected by certain forces, which enable them to attain different levels of profitability. These five forces help managers analyze the industry to gain a better understanding and develop a more effective business strategy. In the discount retailing industry, it is important to consider the following when considering entry: Threat of New Entrants: Four major competitors, WalMart, Kmart, Target and Costco Wholesale dominate the discount retail industry. The threat of new entrants is low, as this small number of large firms has spent decades establishing their position in the market. While online retailers, such as Amazon.com, and smaller department stores do create a semi-competitive environment, there is no major threat as the entry barriers are high (there is major risk and expensive start-up costs) and small start-ups are discouraged from trying to penetrate the market. The lack of patents and government regulation allow the existence of small department stores in the industry, but their expansion is limited. Rivalry Within the Industry: In the discount retail industry, there is fierce competition among the major brands, as products sold are usually relatively price elastic; most of the shoppers are looking for the â€Å"best value for price† and the goods are not significantly differentiated from one another. This leads to efficient management and competitive costs. While dollar stores and other small retailers have established a niche market, they do not pose a significant threat to the market leaders. Supplier Power: The existence of a large number of suppliers and limited shelf space has lead to low supplier power; retailers like Kmart are free to switch to alternate, cheaper brands. Threat of Substitutes: In terms of brand identity, the main players attempt to differentiate themselves from each other by emphasizing on their strengths; while WalMart is known as the price leader, Target

Based Data Mining Approach for Quality Control

Classification-Based Data Mining Approach For Quality Control In Wine Production GUIDED BY: | | SUBMITTED BY:| Jayshri Patel| | Hardik Barfiwala| INDEX Sr No| Title| Page No. | 1| Introduction Wine Production| | 2| Objectives| | 3| Introduction To Dataset| | 4| Pre-Processing| | 5| Statistics Used In Algorithms| | 6| Algorithms Applied On Dataset| | 7| Comparison Of Applied Algorithm | | 8| Applying Testing Dataset| | 9| Achievements| | 1.INTRODUCTION TO WINE PRODUCTION * Wine industry is currently growing well in the market since the last decade. However, the quality factor in wine has become the main issue in wine making and selling. * To meet the increasing demand, assessing the quality of wine is necessary for the wine industry to prevent tampering of wine quality as well as maintaining it. * To remain competitive, wine industry is investing in new technologies like data mining for analyzing taste and other properties in wine. Data mining techniques provide more than summary, but valuable information such as patterns and relationships between wine properties and human taste, all of which can be used to improve decision making and optimize chances of success in both marketing and selling. * Two key elements in wine industry are wine certification and quality assessment, which are usually conducted via physicochemical and sensory tests. * Physicochemical tests are lab-based and are used to characterize physicochemical properties in wine such as its density, alcohol or pH values. * Meanwhile, sensory tests such as taste preference are performed by human experts.Taste is a particular property that indicates quality in wine, the success of wine industry will be greatly determined by consumer satisfaction in taste requirements. * Physicochemical data are also found useful in predicting human wine taste preference and classifying wine based on aroma chromatograms. 2. OBJECTIVE * Modeling the complex human taste is an important focus in wine industries. * The main purpose of this study was to predict wine quality based on physicochemical data. * This study was also conducted to identify outlier or anomaly in sample wine set in order to detect ruining of wine. 3. INTRODUCTION TO DATASETTo evaluate the performance of data mining dataset is taken into consideration. The present content describes the source of data. * Source Of Data Prior to the experimental part of the research, the data is gathered. It is gathered from the UCI Data Repository. The UCI Repository of Machine Learning Databases and Domain Theories is a free Internet repository of analytical datasets from several areas. All datasets are in text files format provided with a short description. These datasets received recognition from many scientists and are claimed to be a valuable source of data. * Overview Of Dataset INFORMATION OF DATASET|Title:| Wine Quality| Data Set Characteristics:| Multivariate| Number Of Instances:| WHITE-WINE : 4898 RED-WINE : 1599 | Area:| Business| Attrib ute Characteristic:| Real| Number Of Attribute:| 11 + Output Attribute| Missing Value:| N/A| * Attribute Information * Input variables (based on physicochemical tests) * Fixed Acidity: Amount of Tartaric Acid present in wine. (In mg per liter) Used for taste, feel and color of wine. * Volatile Acidity: Amount of Acetic Acid present in wine. (In mg per liter) Its presence in wine is mainly due to yeast and bacterial metabolism. * Citric Acid: Amount of Citric Acid present in wine. In mg per liter) Used to acidify wine that are too basic and as a flavor additive. * Residual Sugar: The concentration of sugar remaining after fermentation. (In grams per liter) * Chlorides: Level of Chlorides added in wine. (In mg per liter) Used to correct mineral deficiencies in the brewing water. * Free Sulfur Dioxide: Amount of Free Sulfur Dioxide present in wine. (In mg per liter) * Total Sulfur Dioxide: Amount of free and combined sulfur dioxide present in wine. (In mg per liter) Used mainly as pres ervative in wine process. * Density: The density of wine is close to that of water, dry wine is less and sweet wine is higher. In kg per liter) * PH: Measures the quantity of acids present, the strength of the acids, and the effects of minerals and other ingredients in the wine. (In values) * Sulphates: Amount of sodium metabisulphite or potassium metabisulphite present in wine. (In mg per liter) * Alcohol: Amount of Alcohol present in wine. (In percentage) * Output variable (based on sensory data) * Quality (score between 0 and 10) : White Wine : 3 to 9 Red Wine : 3 to 8 4. PRE-PROCESSING * Pre-processing Of Data Preprocessing of the dataset is carried out before mining the data to remove the different lacks of the information in the data source.Following different process are carried out in the preprocessing reasons to make the dataset ready to perform classification process. * Data in the real world is dirty because of the following reason. * Incomplete: Lacking attribute values, lacking certain attributes of interest, or containing only aggregate data. * E. g. Occupation=â€Å"† * Noisy : Containing errors or outliers. * E. g. Salary=â€Å"-10† * Inconsistent : Containing discrepancies in codes or names. * E. g. Age=â€Å"42† Birthday=â€Å"03/07/1997† * E. g. Was rating â€Å"1,2,3†, Now rating â€Å"A, B, C† * E. g. Discrepancy between duplicate records * No quality data, no quality mining results! Quality decisions must be based on quality data. * Data warehouse needs consistent integration of quality data. * Major Tasks in done in the Data Preprocessing are, * Data Cleaning * Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies. * Data integration * Integration of multiple databases, data cubes, or files. * The dataset provided from given data source is only in one single file. So there is no need for integrating the dataset. * Data transformation * Normalization a nd aggregation * The dataset is in Normalized form because it is in single data file. * Data reduction Obtains reduced representation in volume but produces the same or similar analytical results. * The data volume in the given dataset is not very huge, the procedure of performing different algorithm is easily done on dataset so the reduction of dataset is not needed on the data set * Data discretization * Part of data reduction but with particular importance, especially for numerical data. * Need for Data Preprocessing in wine quality, * For this dataset Data Cleaning is only required in data pre-processing. * Here, NumericToNominal, InterquartileRange and RemoveWithValues filters are used for data pre-processing. * NumericToNominal Filter weka. filters. unsupervised. attribute. NumericToNominal) * A filter for turning numeric attribute into nominal once. * In our dataset, Class attribute â€Å"Quality† in both dataset (Red-wine Quality, White-wine Quality) have a type †Å"Numeric†. So after applying this filter, class attribute â€Å"Quality† convert into type â€Å"Nominal†. * And Red-wine Quality dataset have class names 3, 4, 5 †¦ 8 and White-wine Quality dataset have class names 3, 4, 5 †¦ 9. * Because of classification does not apply on numeric type class field, there is a need for this filter. * InterquartileRange Filter (weka. filters. unsupervised. attribute. InterquartileRange) A filter for detecting outliers and extreme values based on interquartile ranges. The filter skips the class attribute. * Apply this filter for all attribute indices with all default options. * After applying, filter adds two more fields which names are â€Å"Outliers† and â€Å"ExtremeValue†. And this fields has two types of label â€Å"No† and â€Å"Yes†. Here â€Å"Yes† label indicates, there are outliers and extreme values in dataset. * In our dataset, there are 83 extreme values and 125 outliers i n White-wine Quality dataset and 69 extreme values and 94 outliers in Red-wine Quality. * RemoveWithValues Filter (weka. filters. unsupervised. instance.RemoveWithValues) * Filters instances according to the value of an attribute. * This filter has two options which are â€Å"AttributeIndex† and â€Å"NominalIndices†. * AttributeIndex choose attribute to be use for selection and NominalIndices choose range of label indices to be use for selection on nominal attribute. * In our dataset, AttributeIndex is â€Å"last† and NominalIndex is also â€Å"last†, so It will remove first 83 extreme values and then 125 outliers in White-wine Quality dataset and 69 extreme values and 94 outliers in Red-wine Quality. * After applying this filter on dataset remove both fields from dataset. * Attribute SelectionRanking Attributes Using Attribute Selection Algorithm| RED-WINE| RANKED| WHITE-WINE| Volatile_Acidity(2)| 0. 1248| 0. 0406| Volatile_Acidity(2)| Total_sulfer_Diox ide(7)| 0. 0695| 0. 0600| Citric_Acidity(3)| Sulphates(10)| 0. 1464| 0. 0740| Chlorides(5)| Alcohal(11)| 0. 2395| 0. 0462| Free_Sulfer_Dioxide(6)| | | 0. 1146| Density(8)| | | 0. 2081| Alcohal(11)| * The selection of attributes is performed automatically by WEKA using Info Gain Attribute Eval method. * The method evaluates the worth of an attribute by measuring the information gain with respect to the class. 5. STATISTICS USED IN ALGORITHMS * Statistics MeasuresThere are Different algorithms that can be used while performing data mining on the different dataset using weka, some of them are describe below with the different statistics measures. * Statistics Used In Algorithms * Kappa statistic * The kappa statistic, also called the kappa coefficient, is a performance criterion or index which compares the agreement from the model with that which could occur merely by chance. * Kappa is a measure of agreement normalized for chance agreement. * Kappa statistic describe that our predicti on for class attribute for given dataset is how much near to actual values. * Values Range For Kappa Range| Result| lt;0| POOR| 0-0. 20| SLIGHT| 0. 21-0. 40| FAIR| 0. 41-0. 60| MODERATE| 0. 61-0. 80| SUBSTANTIAL| 0. 81-1. 0| ALMOST PERFECT| * As above range in weka algorithm evaluation if value of kappa is near to 1 then our predicted values are accurate to actual values so, applied algorithm is accurate. Kappa Statistic Values For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 0. 5365| 0. 5294| J48| 0. 3813| 0. 3881| Multilayer Perceptron| 0. 2946| 0. 3784| * Mean absolute error (MAE) * Mean absolute error (MAE)  is a quantity used to measure how close forecasts or predictions are to the eventual outcomes. The mean absolute error is given by, Mean absolute Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 0. 1297| 0. 1381| J48| 0. 1245| 0. 1401| Multilayer Perceptron| 0. 1581| 0. 1576| * Root Mean Squared Erro r * If you have some data and try to make a curve (a formula) fit them, you can graph and see how close the curve is to the points. Another measure of how well the curve fits the data is Root Mean Squared Error. * For each data point, CalGraph calculates the value of  Ã‚  y from the formula. It subtracts this from the data's y-value and squares the difference. All these squares are added up and the sum is divided by the number of data. * Finally CalGraph takes the square root. Written mathematically, Root Mean Square Error is Root Mean Squared Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 0. 2428| 0. 2592| J48| 0. 3194| 0. 3354| Multilayer Perceptron| 0. 2887| 0. 3023| * Root Relative Squared Error * The  root relative squared error  is relative to what it would have been if a simple predictor had been used. More specifically, this simple predictor is just the average of the actual values. Thus, the relative squared error takes the to tal squared error and normalizes it by dividing by the total squared error of the simple predictor. * By taking the square root of therelative squared error  one reduces the error to the same dimensions as the quantity being predicted. * Mathematically, the  root relative squared error  Ei  of an individual program  i  is evaluated by the equation: * where  P(ij)  is the value predicted by the individual program  i  for sample case  j  (out of  n  sample cases);  Tj  is the target value for sample case  j; andis given by the formula: * For a perfect fit, the numerator is equal to 0 and  Ei  = 0.So, the  Ei  index ranges from 0 to infinity, with 0 corresponding to the ideal. Root Relative Squared Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 78. 1984 %| 79. 309 %| J48| 102. 9013 %| 102. 602 %| Multilayer Perceptron| 93. 0018 %| 92. 4895 %| * Relative Absolute Error * The  relative absolute error  is very similar to the  relative squared error  in the sense that it is also relative to a simple predictor, which is just the average of the actual values. In this case, though, the error is just the total absolute error instead of the total squared error. Thus, the relative absolute error takes the total absolute error and normalizes it by dividing by the total absolute error of the simple predictor. Mathematically, the  relative absolute error  Ei  of an individual program  i  is evaluated by the equation: * where  P(ij)  is the value predicted by the individual program  i  for sample case  j  (out of  n  sample cases);  Tj  is the target value for sample case  j; andis given by the formula: * For a perfect fit, the numerator is equal to 0 and  Ei  = 0. So, the  Ei  index ranges from 0 to infinity, with 0 corresponding to the ideal.Relative Absolute Squared Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality | K-Star| 67. 2423 %| 64. 5286 %| J48| 64. 577 %| 65. 4857 %| Multilayer Perceptron| 81. 9951 %| 73. 6593 %| * Various Rates * There are four possible outcomes from a classifier. * If the outcome from a prediction is  p  and the actual value is also  p, then it is called a  true positive  (TP). * However if the actual value is  n  then it is said to be a  false positive  (FP). * Conversely, a  true negative  (TN) has occurred when both the prediction outcome and the actual value are  n. And  false negative  (FN) is when the prediction outcome is  n while the actual value is  p. * Absolute Value | P| N| TOTAL| p’| True positive| false positive| P’| n’| false negative| True negative| N’| Total| P| N| | * ROC Curves * While estimating the effectiveness and accuracy of data mining technique it is essential to measure the error rate of each method. * In the case of binary classification tasks the error rate takes and components under consideration. * The ROC analysis which stands for Receiver Operating Characteristics is applied. * The sample ROC curve is presented in the Figure below.The closer the ROC curve is to the top left corner of the ROC chart the better the performance of the classifier. * Sample ROC curve (squares with the usage of the model, triangles without). The line connecting the square with triage is the benefit from the usage of the model. * It plots the curve which consists of x-axis presenting false positive rate and y-axis which plots the true positive rate. This curve model selects the optimal model on the basis of assumed class distribution. * The ROC curves are applicable e. g. in decision tree models or rule sets. * Recall, Precision and F-Measure There are four possible results of classification. * Different combination of these four error and correct situations are presented in the scientific literature on topic. * Here three popular notions are presented. The introduction of the se classifiers is explained by the possibility of high accuracy by negative type of data. * To avoid such situation recall and precision of the classification are introduced. * The F measure is the harmonic mean of precision and recall. * The formal definitions of these measures are as follow : PRECSION = TPTP+FP RECALL = TPTP+FNF-Measure = 21PRECSION+1RECALL * These measures are introduced especially in information retrieval application. * Confusion Matrix * A matrix used to summarize the results of a supervised classification. * Entries along the main diagonal are correct classifications. * Entries other than those on the main diagonal are classification errors. 6. ALGORITHMS * K-Nearest Neighbor Classifiers * Nearest neighbor classifiers are based on learning by analogy. * The training samples are described by n-dimensional numeric attributes. Each sample represents a point in an n-dimensional space. In this way, all of the training samples are stored in an n-dimensional pattern space. When given an unknown sample, a k-nearest neighbor classifier searches the pattern space for the k training samples that are closest to the unknown sample. * These k training samples are the k-nearest neighbors of the unknown sample. â€Å"Closeness† is defined in terms of Euclidean distance, where the Euclidean distance between two points, , * The unknown sample is assigned the most common class among its k nearest neighbors. When k = 1, the unknown sample is assigned the class of the training sample that is closest to it in pattern space. Nearest neighbor classifiers are instance-based or lazy learners in that they store all of the training samples and do not build a classifier until a new (unlabeled) sample needs to be classified. * Lazy learners can incur expensive computational costs when the number of potential neighbors (i. e. , stored training samples) with which to compare a given unlabeled sample is great. * Therefore, they require efficient indexing techniqu es. As expected, lazy learning methods are faster at training than eager methods, but slower at classification since all computation is delayed to that time.Unlike decision tree induction and back propagation, nearest neighbor classifiers assign equal weight to each attribute. This may cause confusion when there are many irrelevant attributes in the data. * Nearest neighbor classifiers can also be used for prediction, i. e. to return a real-valued prediction for a given unknown sample. In this case, the classifier returns the average value of the real-valued labels associated with the k nearest neighbors of the unknown sample. * In weka the previously described algorithm nearest neighbor is given as Kstar algorithm in classifier -> lazy tab. The Result Generated After Applying K-Star On White-wine Quality Dataset Kstar Options : -B 70 -M a | Time Taken To Build Model: 0. 02 Seconds| Stratified Cross-Validation (10-Fold)| * Summary | Correctly Classified Instances | 3307 | 70. 6624 % | Incorrectly Classified Instances| 1373 | 29. 3376 %| Kappa Statistic | 0. 5365| | Mean Absolute Error | 0. 1297| | Root Mean Squared Error| 0. 2428| | Relative Absolute Error | 67. 2423 %| | Root Relative Squared Error | 78. 1984 %| | Total Number Of Instances | 4680 | | * Detailed Accuracy By Class | TP Rate| FP Rate | Precision | Recall | F-Measure | ROC Area | PRC Area| Class| | 0 | 0 | 0 | 0 | 0 | 0. 583 | 0. 004 | 3| | 0. 211 | 0. 002 | 0. 769 | 0. 211 | 0. 331 | 0. 884 | 0. 405 | 4| | 0. 672 | 0. 079 | 0. 777 | 0. 672 | 0. 721 | 0. 904 | 0. 826 | 5| | 0. 864 | 0. 378 | 0. 652 | 0. 864 | 0. 743 | 0. 84 | 0. 818 | 6| | 0. 536 | 0. 031 | 0. 797 | 0. 536 | 0. 641 | 0. 911 | 0. 772 | 7| | 0. 398 | 0. 002 | 0. 883 | 0. 398 | 0. 548 | 0. 913 | 0. 572 | 8| | 0 | 0 | 0 | 0 | 0 | 0. 84 | 0. 014 | 9| Weighted Avg. | 0. 707 | 0. 2 | 0. 725 | 0. 707 | 0. 695 | 0. 876 | 0. 787| | * Confusion Matrix| A | B | C | D | E | F| G | | Class| 0 | 0 | 4 | 9 | 0| 0 | 0 | | | A=3| 0| 30| 49| 62| 1 | 0 | 0| | | B=4| 0 | 7 | 919| 437| 5 | 0 | 0 | | | C=5| 0 | 2 | 201| 1822| 81 | 2 | 0 | || D=6| 0 | 0 | 9 | 389 | 468 | 7 | 0| || E=7| 0 | 0 | 0 | 73 | 30 | 68 | 0 | || F=8| 0 | 0 | 0 | 3 | 2 | 0 | 0 | || G=9| * Performance Of The Kstar With Respect To A Testing Configuration For The White-wine Quality DatasetTesting Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 99. 6581 %| 100 %| 70. 6624 %| 63. 9221 %| Kappa statistic| 0. 9949| 1| 0. 5365| 0. 4252| Mean Absolute Error| 0. 0575| 0. 0788| 0. 1297| 0. 1379| Root Mean Squared Error| 0. 1089| 0. 145| 0. 2428| 0. 2568| Relative Absolute Error| 29. 8022 %| | 67. 2423 %| 71. 2445 %| * The Result Generated After Applying K-Star On Red-wine Quality Dataset Kstar Options : -B 70 -M a | Time Taken To Build Model: 0 Seconds| Stratified Cross-Validation (10-Fold)| * Summary | Correctly Classified Instances | 1013 | 71. 379 %| Incorrectly Classified Instances| 413 | 28. 9621 %| Kappa Stat istic | 0. 5294| | Mean Absolute Error | 0. 1381| | Root Mean Squared Error | 0. 2592| | Relative Absolute Error | 64. 5286 %| | Root Relative Squared Error | 79. 309 %| | Total Number Of Instances | 1426 | | * Detailed Accuracy By Class | | TP Rate | FP Rate | Precision | Recall | F-Measure | ROC Area | PRC Area| Class| | 0 | 0. 001 | 0 | 0 | 0 | 0. 574 | 0. 019 | 3| | 0 | 0. 003 | 0 | 0 | 0 | 0. 811 | 0. 114 | 4| | 0. 791| 0. 176 | 0. 67| 0. 791| 0. 779 | 0. 894 | 0. 867 | 5| | 0. 769 | 0. 26 | 0. 668 | 0. 769 | 0. 715 | 0. 834 | 0. 788 | 6| | 0. 511 | 0. 032 | 0. 692 | 0. 511 | 0. 588 | 0. 936 | 0. 722 | 7| | 0. 125 | 0. 001 | 0. 5 | 0. 125 | 0. 2 | 0. 896 | 0. 142 | 8| Weighted Avg. | 0. 71| 0. 184| 0. 685| 0. 71| 0. 693| 0. 871| 0. 78| | * Confusion Matrix | A | B | C | D | E | F| | Class| 0 | 1 | 4| 1 | 0 | 0 | | | A=3| 1 | 0 | 30| 17 | 0 | 0| | | B=4| 0 | 2| 477| 120 | 4 | 0| | | C=5| 0 | 1 | 103 | 444| 29 | 0| || D=6| 0 | 0 | 8 | 76 | 90 | 2 | || E=7| 0 | 0 | 0 | 7 | 7 | 2| || F=8| Performance Of The Kstar With Respect To A Testing Configuration For The Red-wine Quality Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 99. 7895 %| 100 % | 71. 0379 %| 70. 7216 %| Kappa statistic| 0. 9967| 1| 0. 5294| 0. 5154| Mean Absolute Error| 0. 0338| 0. 0436| 0. 1381| 0. 1439| Root Mean Squared Error| 0. 0675| 0. 0828 | 0. 2592| 0. 2646| Relative Absolute Error| 15. 8067 %| | 64. 5286 %| 67. 4903 %| * J48 Decision Tree * Class for generating a pruned or unpruned C4. 5 decision tree. A decision tree is a predictive machine-learning model that decides the target value (dependent variable) of a new sample based on various attribute values of the available data. * The internal nodes of a decision tree denote the different attribute; the branches between the nodes tell us the possible values that these attributes can have in the observed samples, while the terminal nodes tell us the final value (class ification) of the dependent variable. * The attribute that is to be predicted is known as the dependent variable, since its value depends upon, or is decided by, the values of all the other attributes.The other attributes, which help in predicting the value of the dependent variable, are known as the independent variables in the dataset. * The J48 Decision tree classifier follows the following simple algorithm: * In order to classify a new item, it first needs to create a decision tree based on the attribute values of the available training data. So, whenever it encounters a set of items (training set) it identifies the attribute that discriminates the various instances most clearly. * This feature that is able to tell us most about the data instances so that we can classify them the best is said to have the highest information gain. Now, among the possible values of this feature, if there is any value for which there is no ambiguity, that is, for which the data instances falling wi thin its category have the same value for the target variable, then we terminate that branch and assign to it the target value that we have obtained. * For the other cases, we then look for another attribute that gives us the highest information gain. Hence we continue in this manner until we either get a clear decision of what combination of attributes gives us a particular target value, or we run out of attributes.In the event that we run out of attributes, or if we cannot get an unambiguous result from the available information, we assign this branch a target value that the majority of the items under this branch possess. * Now that we have the decision tree, we follow the order of attribute selection as we have obtained for the tree. By checking all the respective attributes and their values with those seen in the decision tree model, we can assign or predict the target value of this new instance. * The Result Generated After Applying J48 On White-wine Quality Dataset Time Taken To Build Model: 1. 4 Seconds| Stratified Cross-Validation (10-Fold) | * Summary| | | Correctly Classified Instances| 2740 | 58. 547 %| Incorrectly Classified Instances | 1940 | 41. 453 %| Kappa Statistic | 0. 3813| | Mean Absolute Error | 0. 1245| | Root Mean Squared Error | 0. 3194| | Relative Absolute Error | 64. 5770 %| | Root Relative Squared Error| 102. 9013 %| | Total Number Of Instances | 4680| | * Detailed Accuracy By Class| | TP Rate| FP Rate| Precision| Recall| F-Measure| ROC Area| Class| | 0| 0. 002| 0| 0| 0| 0. 30| 3| | 0. 239| 0. 020| 0. 270| 0. 239| 0. 254| 0. 699| 4| | 0. 605| 0. 169| 0. 597| 0. 605| 0. 601| 0. 763| 5| | 0. 644| 0. 312| 0. 628| 0. 644| 0. 636| 0. 689| 6| | 0. 526| 0. 099| 0. 549| 0. 526| 0. 537| 0. 766| 7| | 0. 363| 0. 022| 0. 388| 0. 363| 0. 375| 0. 75| 8| | 0| 0| 0| 0| 0| 0. 496| 9| Weighted Avg. | 0. 585 | 0. 21 | 0. 582 | 0. 585 | 0. 584 | 0. 727| | * Confusion Matrix | A| B| C| D| E| F| G| || Class| 0| 2| 6| 5| 0| 0| 0| || A=3| 1| 34| 55| 44| 6| 2| 0| || B=4| 5| 50| 828| 418| 60| 7| 0| || C=5| 2| 32| 413| 1357| 261| 43| 0| || D=6| | 7| 76| 286| 459| 44| 0| || E=7| 1| 1| 10| 49| 48| 62| 0| || F=8| 0| 0| 0| 1| 2| 2| 0| || G=9| * Performance Of The J48 With Respect To A Testing Configuration For The White-wine Quality Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 90. 1923 %| 70 %| 58. 547 %| 54. 8083 %| Kappa statistic| 0. 854| 0. 6296| 0. 3813| 0. 33| Mean Absolute Error| 0. 0426| 0. 0961| 0. 1245| 0. 1347| Root Mean Squared Error| 0. 1429| 0. 2756| 0. 3194| 0. 3397| Relative Absolute Error| 22. 0695 %| | 64. 577 %| 69. 84 %| * The Result Generated After Applying J48 On Red-wine Quality Dataset Time Taken To Build Model: 0. 17 Seconds| Stratified Cross-Validation| * Summary| Correctly Classified Instances | 867 | 60. 7994 %| Incorrectly Classified Instances | 559 | 39. 2006 %| Kappa Statistic | 0. 3881| | Mean Absolute Error | 0. 1401| | Root Mean Squa red Error | 0. 3354| | Relative Absolute Error | 65. 4857 %| | Root Relative Squared Error | 102. 602 %| |Total Number Of Instances | 1426 | | * Detailed Accuracy By Class| | Tp Rate | Fp Rate | Precision | Recall | F-measure | Roc Area | Class| | 0 | 0. 004 | 0 | 0 | 0 | 0. 573 | 3| | 0. 063 | 0. 037 | 0. 056 | 0. 063 | 0. 059 | 0. 578 | 4| | 0. 721 | 0. 258 | 0. 672 | 0. 721 | 0. 696 | 0. 749 | 5| | 0. 57 | 0. 238 | 0. 62 | 0. 57 | 0. 594 | 0. 674 | 6| | 0. 563 | 0. 64 | 0. 553 | 0. 563 | 0. 558 | 0. 8 | 7| | 0. 063 | 0. 006 | 0. 1 | 0. 063 | 0. 077 | 0. 691 | 8| Weighted Avg. | 0. 608 | 0. 214 | 0. 606 | 0. 608 | 0. 606 | 0. 718 | | * Confusion Matrix | A | B | C | D | E | F | | Class| 0 | 2 | 1 | 2 | 1 | 0 | | | A=3| 2 | 3 | 25 | 15 | 3 | 0 | | | B=4| 1 | 26 | 435 | 122 | 17 | 2 | | | C=5| 2 | 21 | 167 | 329 | 53 | 5 | | | D=6| 0 | 2 | 16 | 57 | 99 | 2 | | | E=7| 0 | 0 | 3 | 6 | 6 | 1 | | | F=8| Performance Of The J48 With Respect To A Testing Configuration For The Red-wine Qual ity Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 91. 1641 %| 80 %| 60. 7994 %| 62. 4742 %| Kappa statistic| 0. 8616| 0. 6875| 0. 3881| 0. 3994| Mean Absolute Error| 0. 0461| 0. 0942| 0. 1401| 0. 1323| Root Mean Squared Error| 0. 1518| 0. 2618| 0. 3354| 0. 3262| Relative Absolute Error| 21. 5362 %| 39. 3598 %| 65. 4857 %| 62. 052 %| * Multilayer Perceptron * The back propagation algorithm performs learning on a multilayer feed-forward neural network. It iteratively learns a set of weights for prediction of the class label of tuples. * A multilayer feed-forward neural network consists of an input layer, one or more hidden layers, and an output layer. * Each layer is made up of units. The inputs to the network correspond to the attributes measured for each training tuple. The inputs are fed simultaneously into the units making up the input layer. These inputs pass through the input layer and are then weighted an d fed simultaneously to a second layer of â€Å"neuronlike† units, known as a hidden layer. The outputs of the hidden layer units can be input to another hidden layer, and so on. The number of hidden layers is arbitrary, although in practice, usually only one is used. The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the network’s prediction for given tuples. * The units in the input layer are called input units. The units in the hidden layers and output layer are sometimes referred to as neurodes, due to their symbolic biological basis, or as output units. * The network is feed-forward in that none of the weights cycles back to an input unit or to an output unit of a previous layer.It is fully connected in that each unit provides input to each unit in the next forward layer. * The Result Generated After Applying Multilayer Perceptron On White-wine Quality Dataset Time taken to build model: 36. 22 seconds| Stratifi ed cross-validation| * Summary| Correctly Classified Instances | 2598 | 55. 5128 %| Incorrectly Classified Instances | 2082 | 44. 4872 %| Kappa statistic | 0. 2946| | Mean absolute error | 0. 1581| | Root mean squared error | 0. 2887| |Relative absolute error | 81. 9951 %| | Root relative squared error | 93. 0018 %| | Total Number of Instances | 4680 | | * Detailed Accuracy By Class | | TP Rate | FP Rate | Precision | Recall | F-Measure | ROC Area | PRC Area | Class| | 0 | 0 | 0 | 0 | 0 | 0. 344 | 0. 002 | 3| | 0. 056 | 0. 004 | 0. 308 | 0. 056 | 0. 095 | 0. 732 | 0. 156 | 4| | 0. 594 | 0. 165 | 0. 597 | 0. 594 | 0. 595 | 0. 98 | 0. 584 | 5| | 0. 704 | 0. 482 | 0. 545 | 0. 704 | 0. 614 | 0. 647 | 0. 568 | 6| | 0. 326 | 0. 07 | 0. 517 | 0. 326 | 0. 4 | 0. 808 | 0. 474 | 7| | 0. 058 | 0. 002 | 0. 5 | 0. 058 | 0. 105 | 0. 8 | 0. 169 | 8| | 0 | 0 | 0| 0 | 0 | 0. 356 | 0. 001 | 9| Weighted Avg. | 0. 555 | 0. 279 | 0. 544 | 0. 555 | 0. 532 | 0. 728 | 0. 526| | * Confusion Matrix |A | B | C | D | E | F | G | | Class| 0 | 0 | 5 | 7 | 1 | 0 | 0 | | | A=3| 0 | 8 | 82 | 50 | 2 | 0 | 0 | | | B=4| 0 | 11 | 812 | 532 | 12 | 1 | 0 | | | C=5| 0 | 6 | 425 | 1483 | 188 | 6 | 0 | | | D=6| 0 | 1 | 33 | 551 | 285 | 3 | 0 | | | E=7| 0 | 0 | 3 | 98 | 60 | 10 | 0 | | | F=8| 0 | 0 | 0 | 2 | 3 | 0 | 0 | | | G=9| * Performance Of The Multilayer perceptron With Respect To A Testing Configuration For The White-wine Quality DatasetTesting Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 58. 1838 %| 50 %| 55. 5128 %| 51. 3514 %| Kappa statistic| 0. 3701| 0. 3671| 0. 2946| 0. 2454| Mean Absolute Error| 0. 1529| 0. 1746| 0. 1581| 0. 1628| Root Mean Squared Error| 0. 2808| 0. 3256| 0. 2887| 02972| Relative Absolute Error| 79. 2713 %| | 81. 9951 %| 84. 1402 %| * The Result Generated After Applying Multilayer Perceptron On Red-wine Quality Dataset Time taken to build model: 9. 14 seconds| Stratified cross-validation (10-Fold)| * Summary | Co rrectly Classified Instances | 880 | 61. 111 %| Incorrectly Classified Instances | 546 | 38. 2889 %| Kappa statistic | 0. 3784| | Mean absolute error | 0. 1576| | Root mean squared error | 0. 3023| | Relative absolute error | 73. 6593 %| | Root relative squared error | 92. 4895 %| | Total Number of Instances | 1426| | * Detailed Accuracy By Class | | TP Rate | FP Rate | Precision | Recall | F-Measure | ROC Area | Class| | 0 | 0 | 0 | 0 | 0 | 0. 47 | 3| | 0. 42 | 0. 005 | 0. 222 | 0. 042 | 0. 070 | 0. 735 | 4| | 0. 723 | 0. 249 | 0. 680 | 0. 723 | 0. 701 | 0. 801 | 5| | 0. 640 | 0. 322 | 0. 575 | 0. 640 | 0. 605 | 0. 692 | 6| | 0. 415 | 0. 049 | 0. 545 | 0. 415 | 0. 471 | 0. 831 | 7| | 0 | 0 | 0 | 0 | 0 | 0. 853 | 8| Weighted Avg. | 0. 617 | 0. 242 | 0. 595 | 0. 617 | 0. 602 | 0. 758| | * Confusion Matrix | A | B | C | D | E | F | | Class| | 0 | 5 | 1 | 0 | 0| || A=3| 0 | 2 | 34 | 11 | 1 | 0 | | | B=4| 0 | 2 | 436 | 160 | 5 | 0 | | | C=5| 0 | 5 | 156 | 369 | 47 | 0 | | | D=6| 0 | 0 | 10 | 93 | 73 | 0 | | | E=7| 0 | 0 | 0 | 8 | 8 | 0 | | | F=8| * Performance Of The Multilayer perceptron With Respect To A Testing Configuration For The Red-wine Quality Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 68. 7237 %| 70 %| 61. 7111 %| 58. 7629 %| Kappa statistic| 0. 4895| 0. 5588| 0. 3784| 0. 327| Mean Absolute Error| 0. 426| 0. 1232| 0. 1576| 0. 1647| Root Mean Squared Error| 0. 2715| 0. 2424| 0. 3023| 0. 3029| Relative Absolute Error| 66. 6774 %| 51. 4904 %| 73. 6593 %| 77. 2484 %| * Result * The classification experiment is measured by accuracy percentage of classifying the instances correctly into its class according to quality attributes ranges between 0 (very bad) and 10 (excellent). * From the experiments, we found that classification for red wine quality using  Kstar algorithm achieved 71. 0379 % accuracy while J48 classifier achieved about 60. 7994% and Multilayer Perceptron classifier ac hieved 61. 7111% accuracy. For the white wine, Kstar algorithm yielded 70. 6624 % accuracy while J48 classifier yielded 58. 547% accuracy and Multilayer Perceptron classifier achieved 55. 5128 % accuracy. * Results from the experiments lead us to conclude that Kstar performs better in classification task as compared against the J48 and Multilayer Perceptron classifier. The processing time for Kstar algorithm is also observed to be more efficient and less time consuming despite the large size of wine properties dataset. 7. COMPARISON OF DIFFERENT ALGORITHM * The Comparison Of All Three Algorithm On White-wine Quality Dataset (Using 10-Fold Cross Validation) Kstar| J48| Multilayer Perceptron| Time (Sec)| 0| 1. 08| 35. 14| Kappa Statistics| 0. 5365| 0. 3813| 0. 29| Correctly Classified Instances (%)| 70. 6624| 58. 547| 55. 128| True Positive Rate (Avg)| 0. 707| 0. 585| 0. 555| False Positive Rate (Avg)| 0. 2| 0. 21| 0. 279| * Chart Shows The Best Suited Algorithm For Our Dataset (Measu res Vs Algorithms) * In above chart, comparison of True Positive rate and kappa statistics is given against three algorithm Kstar, J48, Multilayer Perceptron * Chart describes algorithm which is best suits for our dataset. In above chart column of TP rate & Kappa statistics of Kstar algorithm is higher than other two algorithms. * In above chart you can see that the False Positive Rate and the Mean Absolute Error of the Multilayer Perceptron algorithm is high compare to other two algorithms. So it is not good for our dataset. * But for the Kstar algorithm these two values are less, so the algorithm having lowest values for FP Rate & Mean Absolute Error rate is best suited algorithm. * So the final we can make conclusion that the Kstar algorithm is best suited algorithm for White-wine Quality dataset. The Comparison Of All Three Algorithm On Red-wine Quality Dataset (Using 10-Fold Cross Validation) | Kstar| J48| Multilayer Perceptron| Time (Sec)| 0| 0. 24| 9. 3| Kappa Statistics| 0. 5294| 0. 3881| 0. 3784| Correctly Classified Instances (%)| 71. 0379| 60. 6994| 61. 7111| True Positive Rate (Avg)| 0. 71| 0. 608| 0. 617| False Positive Rate (Avg)| 0. 184| 0. 214| 0. 242| * For Red-wine Quality dataset have also Kstar is best suited algorithm , because of TP rate & Kappa statistics of Kstar algorithm is higher than other two algorithms and FP rate & Mean Absolute Error of Kstar algorithm is lower than other algorithms. . APPLYING TESTING DATASET Step1: Load pre-processed dataset. Step2: Go to classify tab. Click on choose button and select lazy folder from the hierarchy tab and then select kstar algorithm. After selecting the kstar algorithm keep the value of cross validation = 10, then build the model by clicking on start button. Step3: Now take any 10 or 15 records from your dataset, make their class value unknown(by putting ’? ’ in the cell of the corresponding raw ) as shown below. Step 4: Save this data set as . rff file. Step 5: From â€Å"tes t option† panel select â€Å"supplied test set†, click on to the set button and open the test dataset file which was lastly created by you from the disk. Step 6: From â€Å"Result list panel† panel select Kstar-algorithm (because it is better than any other for this dataset), right click it and click â€Å"Re-evaluate model on current test set† Step 7: Again right click on Kstar algorithm and select â€Å"visualize classifier error† Step 8:Click on save button and then save your test model.Step 9: After you had saved your test model, a separate file is created in which you will be having your predicted values for your testing dataset. Step 10: Now, this test model will have all the class value generated by model by re-evaluating model on the test data for all the instances that were set to unknown, as shown in the figure below. 9. ACHIEVEMENT * Classification models may be used as part of decision support system in different stages of wine productio n, hence giving the opportunity for manufacturer to make corrective and additive measure that will result in higher quality wine being produced. From the resulting classification accuracy, we found that accuracy rate for the white wine is influenced by a higher number of physicochemistry attribute, which are alcohol, density, free sulfur dioxide, chlorides, citric acid, and volatile acidity. * Red wine quality is highly correlated to only four attributes, which are alcohol, sulphates, total sulfur dioxide, and volatile acidity. * This shows white wine quality is affected by physicochemistry attributes that does not affect the red wine in general. Therefore, I suggest that white wine manufacturer should conduct wider range of test particularly towards density and chloride content since white wine quality is affected by such substances. * Attribute selection algorithm we conducted also ranked alcohol as the highest in both datasets, hence the alcohol level is the main attribute that d etermines the quality in both red and white wine. * My suggestion is that wine manufacturer to focus in maintaining a suitable alcohol content, may be by longer fermentation period or higher yield fermenting yeast.