The Rise and Fall of Enron - 2970 Words

Learning Outcomes and Assessment Feedback Name of the Assessor Learning Outcomes Assessment Criteria (AC) Assessor Feedback LO 1 Understand the relationship between strategic management and leadership 1.1 Explain the link between strategic management and leadership 1.2 Analyze the impact of management and leadership styles on strategic decisions 1.3 Evaluate how leadership styles can be adapted to different situations LO 2 Be able to apply management and leadership theory to support organizational direction 2.1 review the impact that selected theories of management and leadership have on organizational strategy 2.2 create a leadership strategy that†¦show more content†¦Enron took advantage of this and ventured into the power business, this resulted it being the bread and butter for the company, both as an energy company and a Wall Street type trading firm. Lay then employed Jeffery Skilling to further develop and redefine Enron’s business strategy. He proposed a new business model which was buying gas from suppliers and selling it to consumers. (Peter Ross 2002) this resulted in profits due to guaranteed supply and sales price and eventually a fixed commission involved in the transactions (Thomas, 2002) It is widely known that the influence along with the clearly exhibited leadership styles by Skilling who first served as the COO (Chief Operating Officer) and later went on to become the CEO, played a definitive role in leading the company to bankruptcy. According to Seabury, 2011, Skilling had a way of concealing financial loses of the company by adopting the marl to market accounting practice. Skillings also hired Andrew Fastow who created (SPE) Special Purpose Entities. SPEs were used to conceal low performing assets and to keep these off Enron’s financial books. SPEs were then compensated through shares in Enron. 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Guy De Maupassant s `` The Necklace `` And The Story ``...

â€Å"A Good Man Is Hard To Find† â€Å"The Necklace† In today s society we tend to see people live above their means. The reason why I may be picked or even chose. In the short story by author Guy De Maupassant, â€Å"The Necklace† and the story â€Å"Good Man Is Hard to Find† by Flannery O Connor is because both stories have many similarities that we can say that they are combined but different tragedies. The ladies show that they see themselves as important in these stories. These ladies are more concerned about their appearance and impressions on other people. These ladies refuse to admit that they are wrong in their decisions. These ladies also suffer from pride in both stories. First, â€Å"Good Man Is Hard To Find† to find that the grandmothers pride was manifested. The evidence shows that the grandmother believes herself to be better than others. The grandmother wants everyone to realize she’s a lady at all means. In contrast, the grandmother try to manipulate her grandchildren. 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Relation Between Crime, Poverty and Education in Usa Free Essays

————————————————- Statistical analysis of the relation between Crime Rate, Education and Poverty: USA, 2009 Sonarika Mahajan 100076 Research Question In this research paper, analysis is done to conclude whether the level of education and poverty influence the total crime rate in the United States of America. Using descriptive statistics such a mean, standard deviation, variance, histograms, scatter diagrams and simple linear regression analysis performed upon both independent variables separately, it can be analysed till what extent do these two independent variables, i. . We will write a custom essay sample on Relation Between Crime, Poverty and Education in Usa or any similar topic only for you Order Now education and poverty cause fluctuations upon the dependent variable, in what proportion (direct or inverse) and of the two independent variables, which is a better predictor for determining crime rate in USA. Data description [The states selected for this study are highlighted with yellow in the above map] The Data that is used to define our dependent variable include both, violent crime (murder and non- negligent manslaughter, forcible rape, robbery, and aggravated assault) as well as property crime (burglary, larceny-theft, motor vehicle theft, and arson). Crime statistics used in this study are published by FBI (Federal Bureau of Intelligence) serving as a governmental agency to the United States Department of Justice. The independent variable that comments upon the education levels in the United States of America is carried out by analysing the total number of public high school graduates per state. This data includes students of all the ethnicities for the school year 2008-2009. The education universe in this study is equivalent to the total population of the state. This data has been collected by National Centre for Education Statistics (NCES), which is the primary federal entity that collects education related data in the U. S. and other countries and analyses it. The poverty status for an individual is measured by comparing his/her income to a preset amount of dollars known as the threshold value. The poverty universe excludes children below the age of 15, people living in military barracks, institutional group quarters and college dormitories. This data is collected by the U. S. Census Bureau, serving as the most reliable source about America’s people and economy. All the data collected is cross-sectional, since it was taken during the same time period (year 2009) across different parameters. Also, the scale of measurement for these variables is the ratio scale, since the ratio between two values is meaningful and the observations are comparable to a zero value. Analysis Mean: It is the representative of a central value for a given data set, i. e. average. The mean value for crime variable suggests that in the year 2009, the percentage of crimes being reported in any state of USA was 3. 26%. The mean value for education variable suggests that the percentage of public high school graduates being reported in any state of USA was 1% for the same time period. Similarly, the mean value for the poverty variable suggests that the percentage of individuals living below the poverty line being reported in any state of USA was 13. 54%. Standard deviation Variance: The higher the value of the standard deviation, greater is the dispersion of the data set. Out of the three variables, poverty has the highest standard deviation value of 2. 98. Therefore, the percentage of individuals below poverty level is more widely dispersed over the states as compared to the other two variables. Variance is the average of the sum of squared deviation scores. It is used to compute the standard variation since it’s a better means for determining the dispersion of data. It is measured as the square of standard deviation for any data set. Skewness: The symmetry of the variable distribution is measured by the help of this statistic. Crime rate has a skewness of 0. 083, making it a symmetrical distributed variable since the value is closer to zero. The education variable is skewed negatively at -. 67 since the variable has lower values, indicating a left skewed histogram. Whereas, poverty shows a positive skewness value of . 670 since its variables have numerous high values, which justifies the right skewness of the histogram. Simple linear regression model: a. Crime and Education – Y = Dependent variable, Crime X = Independent variable, Education. The regression model is the equation that describes how y is related to x. This regression equation is: From Table 2. 4 in appendix, the regression equation is, Crime = 6. 17 – 2. 9 (Education) This regression equation can be graphed as follows assuming ? 0 as the intercept and ? as the slope: Here the slope ? 1 is negative. Interpretation of the slope: For every 1% increase in the number of students being graduated from high school, there is a decrease of 2. 9% in crime activities in the USA. Interpretation of the intercept: Even if there is no variation in the education level, the estimated crime rate would be 6. 17%. The coefficient of determination or r2: It determines the proportion of variation in the dependent variable by the independent variable. From Table 2. 2, r2 = . 181 This states that 18. 1% of the variation in crime rate is explained by regression of education on crime. Since this value is not close to 1, it doesn’t seem to be a appropriate predictor to determine the crime rate in USA. Hypothesis testing: Ho: ? 1 = 0 (education is not a useful predictor of crime) Ha: ? 1 ? 0 (education is a useful predictor of crime) Significance level: ? = 0. 05 According to the rejection rule, the null hypothesis will be rejected if p-value ? ?. From table 2. 4, p-value = 0. 019 Since 0. 019 ? 0. 05, we reject the null hypothesis. At 95% confidence level, there is enough evidence to conclude that education is a useful predictor for crime in USA since the slope of the regression line is not zero. b. Crime and Poverty: Y = Dependent variable, Crime X = Independent variable, Poverty. The regression equation is as follows: Plugging in the values to from Table 3. 4, get: Crime = 1. 819 + 0. 107 (Poverty) This regression equation can be graphed as follows assuming ? 0 as the intercept and ? 1 as the slope: Here the slope ? 1 is positive. Interpretation of the slope: For every 1% increase in the individuals below poverty line, there is an increase of . 11% in crime activities in the USA. Interpretation of the intercept: With the poverty level remaining constant, the estimated crime rate would be 1. 82%. The coefficient of determination or r2 From Table 3. 2, r2 = . 191 This states that 19. 1% of the variation in crime rate is explained by regression of poverty on crime. Hypothesis testing: Ho: ? 1 = 0 (poverty is not a useful predictor of crime) Ha: ? 1 ? 0 (poverty is a useful predictor of crime) Significance level: ? = 0. 05 According to the rejection rule, the null hypothesis will be rejected if p-value ? ?. From table 3. 4, p-value = 0. 016 Since 0. 016 ? 0. 05, we reject the null hypothesis. At 95% confidence level, there is enough evidence to conclude that poverty is a useful predictor for crime in USA since the slope of the regression line is not zero. Conclusion and recommendations From this study conducted, it is assured that the crime rate in USA is directly proportionate to the people below the poverty line and inversely proportionate to the number of high school students graduating in the year 2009. When simple linear regression was performed to both the independent variables separately, the coefficient of determination (r2) and the p-value aided our study to select the variable that was a better predictor for determining the crime rate in America. Poverty, with the significance level of 19. 1% is known to be a better predictor in this case as compared to the 18. % significance level shown by the independent variable, education. This fact was further proved when the p-value for poverty stood at a lower amount as compared to its counterpart. Even though it can be concluded that poverty is a better predictor for crime rate in USA, the level of significance still stands at a diminutive 19. 1%. Much stronger predictors could be us ed for the above study. GDP, income level, provision of federal aid or employment rate could be a few options to choose amongst. Appendix Table 1. 1 Statistics for crimes reported in 30 states of USA. State| Population| Violent Crime| Property Crime| Total Crime| Percentage of Total Crime | Alabama| 47,08,708| 21,179| 1,77,629| 1,98,808| 4. 22| Alaska| 6,98,473| 4,421| 20,577| 24,998| 3. 58| Arizona| 65,95,778| 26,929| 2,34,582| 2,61,511| 3. 96| California| 3,69,61,664| 1,74,459| 10,09,614| 11,84,073| 3. 20| Colorado| 50,24,748| 16,976| 1,33,968| 1,50,944| 3. 00| Connecticut| 35,18,288| 10,508| 82,181| 92,689| 2. 63| Florida| 1,85,37,969| 1,13,541| 7,12,010| 8,25,551| 4. 45| Hawaii| 12,95,178| 3,559| 47,419| 50,978| 3. 94| Iowa| 30,07,856| 8,397| 69,441| 77,838| 2. 59| Kansas| 28,18,747| 11,278| 90,420| 1,01,698| 3. 61| Michigan| 99,69,727| 49,547| 2,82,918| 3,32,465| 3. 33| Minnesota| 52,66,214| 12,842| 1,39,083| 1,51,925| 2. 88| Mississippi| 29,51,996| 8,304| 87,181| 95,485| 3. 23| Missouri| 59,87,580| 29,444| 2,02,698| 2,32,142| 3. 88| Montana| 9,74,989| 2,473| 24,024| 26,497| 2. 72| Nebraska| 17,96,619| 5,059| 49,614| 54,673| 3. 04| Nevada| 26,43,085| 18,559| 80,763| 99,322| 3. 76| New Jersey| 87,07,739| 27,121| 1,81,097| 2,08,218| 2. 39| New Mexico| 20,09,671| 12,440| 75,078| 87,518| 4. 35| New York | 1,95,41,453| 75,176| 3,78,315| 4,53,491| 2. 2| North Carolina| 93,80,884| 37,929| 3,44,098| 3,82,027| 4. 07| North Dakota| 6,46,844| 1,298| 12,502| 13,800| 2. 13| Oregon| 38,25,657| 9,744| 1,13,511| 1,23,255| 3. 22| Pennsylvania| 1,26,04,767| 47,965| 2,77,512| 3,25,477| 2. 58| South Dakota| 8,12,383| 1,508| 13,968| 15,476| 1. 91| Texas | 2,47,82,302| 1,21,668| 9,95,145| 11,16,813| 4. 51| Virginia| 78,82,590| 17,879| 1,91,453| 2,09,332| 2. 66| Was hington| 66,64,195| 22,056| 2,44,368| 2,66,424| 4. 00| Wisconsin| 56,54,774| 14,533| 1,47,486| 1,62,019| 2. 87| Wyoming| 5,44,270| 1,242| 14,354| 15,596| 2. 87| Source: http://www. fbi. ov/about-us/cjis/ucr/crime-in-the-u. s/2011/crime-in-the-u. s. -2011/tables/table-5 Table 1. 2 Statistics for public high school graduates in 30 states of USA. State| Population| Total Public High School Graduates| Percentage of High School Graduates| Alabama| 47,08,708| 42,082| 0. 89| Alaska| 6,98,473| 8,008| 1. 15| Arizona| 65,95,778| 62,374| 0. 95| California| 3,69,61,664| 3,72,310| 1. 01| Colorado| 50,24,748| 47,459| 0. 94| Connecticut| 35,18,288| 34,968| 0. 99| Florida| 1,85,37,969| 1,53,461| 0. 83| Hawaii| 12,95,178| 11,508| 0. 89| Iowa| 30,07,856| 33,926| 1. 13| Kansas| 28,18,747| 30,368| 1. 8| Michigan| 99,69,727| 1,12,742| 1. 13| Minnesota| 52,66,214| 59,729| 1. 13| Mississippi| 29,51,996| 24,505| 0. 83| Missouri| 59,87,580| 62,969| 1. 05| Montana| 9,74,989| 10,077| 1. 03| Nebraska| 17,96,61 9| 19,501| 1. 09| Nevada| 26,43,085| 19,904| 0. 75| New Jersey| 87,07,739| 95,085| 1. 09| New Mexico| 20,09,671| 17,931| 0. 89| New York | 1,95,41,453| 1,80,917| 0. 93| North Carolina| 93,80,884| 86,712| 0. 92| North Dakota| 6,46,844| 7,232| 1. 12| Oregon| 38,25,657| 35,138| 0. 92| Pennsylvania| 1,26,04,767| 1,30,658| 1. 04| South Dakota| 8,12,383| 8,123| 1. 00| Texas | 2,47,82,302| 2,64,275| 1. 7| Virginia| 78,82,590| 79,651| 1. 01| Washington| 66,64,195| 62,764| 0. 94| Wisconsin| 56,54,774| 65,410| 1. 16| Wyoming| 5,44,270| 5,493| 1. 01| Source: http://nces. ed. gov/CCD/tables/ESSIN_Task5_f2. asp Table 1. 3 Statistics for individuals below Poverty line in 30 states of USA. State| Population for whom poverty status is determined| Individuals in poverty| Percent below poverty| Alabama| 45,88,899| 8,04,683| 17. 54| Alaska| 6,82,412| 61,653| 9. 03| Arizona| 64,75,485| 10,69,897| 16. 52| California| 3,62,02,780| 51,28,708| 14. 17| Colorado| 49,17,061| 6,34,387| 12. 90| Connecticut| 34,09,901| 3,20,554| 9. 40| Florida| 1,81,24,789| 27,07,925| 14. 94| Hawaii| 12,64,202| 1,31,007| 10. 36| Iowa| 29,05,436| 3,42,934| 11. 80| Kansas| 27,32,685| 3,65,033| 13. 36| Michigan| 97,35,741| 15,76,704| 16. 20| Minnesota| 51,33,038| 5,63,006| 10. 97| Mississippi| 28,48,335| 6,24,360| 21. 92| Missouri| 58,18,541| 8,49,009| 14. 59| Montana| 9,46,333| 1,43,028| 15. 11| Nebraska| 17,39,311| 2,14,765| 12. 35| Nevada| 26,06,479| 3,21,940| 12. 35| New Jersey| 85,31,160| 7,99,099| 9. 37| New Mexico| 19,68,078| 3,53,594| 17. 97| New York | 1,90,14,215| 26,91,757| 14. 16| North Carolina| 90,95,948| 14,78,214| 16. 25| North Dakota| 6,20,821| 72,342| 11. 65| Oregon| 37,48,545| 5,34,594| 14. 26| Pennsylvania| 1,21,65,877| 15,16,705| 12. 47| South Dakota| 7,82,725| 1,11,305| 14. 22| Texas | 2,41,76,222| 41,50,242| 17. 17| Virginia| 76,23,736| 8,02,578| 10. 53| Washington| 65,30,664| 8,04,237| 12. 31| Wisconsin| 54,95,845| 6,83,408| 12. 43| Wyoming| 5,29,982| 52,144| 9. 84| Source: http://www. census. gov/compendia/statab/cats/income_expenditures_poverty_wealth/income_and_poverty–state_and_local_data. html Regression (Independent variable: Education) Table 2. 1 Variables Entered/Removedb| Model| Variables Entered| Variables Removed| Method| 1| Educationa| . | Enter| a. All requested variables entered. | | b. Dependent Variable: Crime| | Table 2. 2 Model Summary| Model| R| R Square| Adjusted R Square| Std. Error of the Estimate| 1| . 425a| . 181| . 152| . 67068| a. Predictors: (Constant), Education| | Table 2. 3 ANOVAb| Model| Sum of Squares| df| Mean Square| F| Sig. | 1| Regression| 2. 784| 1| 2. 784| 6. 189| . 019a| | Residual| 12. 595| 28| . 450| | | | Total| 15. 379| 29| | | | a. Predictors: (Constant), Education| | | | . Dependent Variable: Crime| | | | | Table 2. 4 Coefficientsa| Model| Unstandardized Coefficients| Standardized Coefficients| t| Sig. | | B| Std. Error| Beta| | | 1| (Constant)| 6. 165| 1. 173| | 5. 257| . 000| | Education| -2. 904| 1. 167| -. 425| -2. 488| . 019| | | | | | Regression (Independent variable: Poverty) Table 3. 1 Variables Entered/Removedb| Model| Variables Entered| Variables Removed| Method| 1| P overtya| . | Enter| a. All requested variables entered. | | b. Dependent Variable: Crime| | Table 3. 2 Model Summary| Model| R| R Square| Adjusted R Square| Std. Error of the Estimate| 1| . 437a| . 191| . 162| . 66665| a. Predictors: (Constant), Poverty| | Table 3. 3 ANOVAb| Model| Sum of Squares| df| Mean Square| F| Sig. | 1| Regression| 2. 935| 1| 2. 935| 6. 604| . 016a| | Residual| 12. 444| 28| . 444| | | | Total| 15. 379| 29| | | | a. Predictors: (Constant), Poverty| | | | | b. Dependent Variable: Crime| | | | | Table 3. 4 Coefficientsa| Model| Unstandardized Coefficients| Standardized Coefficients| t| Sig. | | B| Std. Error| Beta| | | 1| (Constant)| 1. 819| . 575| | 3. 162| . 004| | Poverty| . 107| . 042| . 437| 2. 570| . 016| How to cite Relation Between Crime, Poverty and Education in Usa, Essay examples

Health Promotion in New Zealand Health Promotion Agency

Question: Discuss about the Health Promotion in New Zealand for Health Promotion Agency. Answer: 1. Health promotion helps to reduce mortality and morbidity rates and address risk factors. Through health promotion programs, a strong, sustainable health system can be placed in the community. Here, the selected health promotion campaign for tobacco control is Stop Before You Start. This new campaign has been launched by Health promotion agency, New Zealand. The program is addressing one of the major issues in the world, tobacco smoking, which is known to be the cause behind fatal health issues including cancer (Hpa.org.nz, 2016). The campaign has targeted the audiences within 17-24 years old, who are at the greater risk f tobacco smoking because of social influence. It has been seen that, in New Zealand, the smoking prevalence is higher in Maori and pacific people along with people from a low socioeconomic background. 5% and 8% gap in mortality has been revealed within Maori and non-Maori men and women respectively (Durkin, Brennan Wakefield, 2012). A significant gap between low and high socioeconomic people has also been identified regarding mortality rate. Thus, this campaign has a special focus on these groups of young adults. In previous studies mass media campaign has been seen to be effective for smoking reduction. Thus, the current campaign has focused on communicating with young adults to know their thinking about tobacco smoking and implementing strategies for changing their views. Considering the health determinants is very important for establishing interventions in the community. Within the individual factors, the lifestyle and diet are considered. The young age people usually lead a harsh lifestyle, as they started to be involved in the wider community through college or working places. Different unsocial behaviors are seen to be increased during this age. Pregnant women are prone to smoke for stress relief, but it is very much injurious to babys health. However, shifting to a healthy life style can help to reduce smoking habit. Water is a natural detoxifier; it helps to reduce the toxic addiction for nicotine. Giger and grape juice are other natural processes for healthy living by quitting smoking (Zhu et al., 2012). In addition, there are some NRT therapies useful for quitting smoking including nicotine patches, gums or inhalers. However, these treatments usually include some side effects including nausea, irritation, behavioral changes. Analyzing all the se factors, the campaign targeted audiences to stop smoking from the root; it aims the cessation of smoking before start. 2. A number of studies highlighted that the Maori and Pacific population has a high rate of smoking habit when compared to non-Maori/non-pacific population. It is the main reason behind the health inequality in the community. The lung mortality rate has been estimated to be higher in Maori population than non-Maori are. This evidence shows a direct link between tobacco smoking and health inequalities in the particular population. While analyzing the contribution of smoking to the health inequalities, it has been found that lack of education and awareness is a major reason behind the health inequalities within Maori and non-Maori. With the increase in education rate among Maoris, a relative reduction of health inequalities was reported. It reduction rate in relative inequalities were 16-21% for men and 3-11% for women in between 1996-99 (Gould et al., 2012). The higher mortality rates for Maoris with poorer education were due to the result of smoking on cardiovascular, respiratory and cancer-related deaths. However, some initiatives have been undertaken by New Zealand government to reduce these health disparities and the situation has been improved; nevertheless much more improvement is needed to reduce the gap. 3. According to the data provided by (Hammond et al., 2012), non-Maori/non-Pacific people are getting the advantages in comparison to the Maori/Pacific population. The non-Maori/non-pacific population mostly lives in urban areas and gets adequate education to know about the impact of smoking on lung health. Thus, they are more concern about reducing smoking. This is why the campaign Stop before you start has more focus on Maori population. 4. A number of health determinants are contributing in the health inequalities caused by tobacco smoking. These include socioeconomic, educational as well as personal factors. The education level is much lower in Maoris compared to the non-Maoris/non-Pacific. Education is the key to health and well-being. Lack of education through the community is the main reason behind their unhealthy behavior (Durkin, S., Brennan Wakefield, 2012). Due to lack of financial stability, they are more prone to smoking, as it is addiction and helps people to be relaxed. In addition, the personal factors include cultural and racial influence. The campaign targets young adults, who also have a social influence, which is facilitating their unsocial behavior including smoking. On the other hand, culture is one of the major determinants. The rural Maoris have a cultural tradition of smoking at very young age, which influences the health inequalities. 5.The purpose of New Zealand public health and disability act 2000 is to provide disability support services, personal health services, public funding, and public health service as well as to establish new publicly owned health and disability organizations for improvement, promotion and protection of public health. To respect and recognize the principles of treaty of Waitangi, some mechanisms have been included, through which people can participate in the decision-making about the health and disability service delivery (Ministry of Health NZ, 2015). Treaty is an integral part of the act. Aligning with the Treaty of Waitangi principles, the key principles of the health and disability act for involving Maori are partnership, participation and protection. The health and disability act 2000 has involved four Treaty of Waitangi principles, which facilitates Mari self-determination and rights of Maori people in health care delivery (Ministry of Health NZ, 2015). Nurses work in partnership with Maoris to improve their health outcomes. The health and disability act has aimed to promote the rights of Maoris to equitable access and participation in health services at all levels for decreasing inequalities. 6.The campaign Stop before you start launched by health agency, has focused on the young adults, specifically the Maori people. The campaign is attempting to increase the awareness of audience for tobacco control. The young Maori adults are being communicated through a series of television commercials and radio advertisements and social media sites, where this age group people are mostly available. They are asked about their "relationship with tobacco." After understanding their relationship, the risk of tobacco smoking and addiction are discussed along with the ways by which they can avoid social influence. This intervention is helping Maori Young adults to think about their anti-smoking habits. These influenced young adults can then disperse their awareness within the community and help others to reduce their smoking habit (Ministry of Health NZ, 2015). Health promotion is one of the best ways to enhance Maori peoples awareness about the risk of tobacco smoking and its impact on th eir health. Young adults are the most common groups who are being engaged in different antisocial habits because of addiction. In the campaign, interaction with individual Maori adults would be more effective to promote health information within the community (Hpa.org.nz, 2016). It has been seen that lack of education is the most important factor contributing in health inequalities. Heath promotion is the best way to enhance knowledge and awareness. Through yearly survey, the social workers would know whether the inequalities are reduced or not. 7.As the campaign is targeted to 18-24 years old young adults, it can be assumed that this group of people would be most benefited from the campaign along with the entire Maori population. It is because, usually, people tend to respond, when someone from their cultural background promotes the activity (Hpa.org.nz, 2016). Thus, through the Maori young people, the entire population would be benefited. 8. Anti-smoking messages can employ shock tactics and may scare people into giving up smoking. It can also be related to physical health issues like reduced corrugators muscle responses, accelerated heart rate and worsened recognition memory. Fear and disgust can also include cardiac deceleration. Conclusion In conclusion, it has been revealed that a significant heath inequality is present in the New Zealand health care framework within Maori and non-Maori population. It has been seen that Maori population are less aware about their health concerns and possible way to reduce health inequalities. Smoking is a significant cause of these health inequalities. The health promotion agency has launched a new program, Stop before you start targeting the young people who are more prone to smoke. The direct and in-direct health promotion through this population can enhance anti-smoking habit within the Maori and Pacific population. Reference List Durkin, S., Brennan, E., Wakefield, M. (2012). Mass media campaigns to promote smoking cessation among adults: an integrative review.Tobacco control,21(2), 127-138. Gould, G. S., McEwen, A., Watters, T., Clough, A. R., van der Zwan, R. (2012). Should anti-tobacco media messages be culturally targeted for Indigenous populations? A systematic review and narrative synthesis.Tobacco Control, tobaccocontrol-2012. Hammond, D., Wakefield, M., Durkin, S., Brennan, E. (2012). Tobacco packaging and mass-media campaigns: research needs for Articles 11 and 12 of the WHO Framework Convention on Tobacco Control.Nicotine Tobacco Research, nts202. Hpa.org.nz,. (2016). Stop Before You Start | HPA - Health promotion agency. Hpa.org.nz. Retrieved 29 July 2016, from https://www.hpa.org.nz/stop-before-you-start Ministry of Health NZ,. (2015). Tobacco control. Ministry of Health NZ. Retrieved 29 July 2016, from https://www.health.govt.nz/our-work/preventative-health-wellness/tobacco-control Zhu, S. H., Lee, M., Zhuang, Y. L., Gamst, A., Wolfson, T. (2012). Interventions to increase smoking cessation at the population level: how much progress has been made in the last two decades?.Tobacco Control,21(2), 110-118.