The Ins And Outs Of Cancer Essays - Medicine, Cancer,

The Ins And Outs Of Cancer The Ins and Outs of Cancer Cancer has affected the lives of each and every one of us alive today. Many people have know someone with cancer, yet even those who havent have been bombarded with constant reminders of its terrible threat. Although cancer is often referred to as a single condition, it actually consists of more than 100 different diseases, all characterized by the uncontrolled growth, reproduction, and spread of abnormal body cells. All of these diseases are individually unique, yet the basic processes that produce cancers are very similar (Ruddon, 1995). The human body consists of over 30 trillion cells, living in a complex, interdependent harmony. They regulate each others proliferation; normal cells reproduce only when instructed to do so by other cells in their vicinity. This constant collaboration ensures that each tissue maintains a certain size and function that is exactly what the body needs. Cancer cells, on the other hand, violate the entire process. Not only do they ignore the bodys contr ols on proliferation, they possess the ability to invade nearby tissues, and may even metastasize migrate and form tumors in distant sites of the body. How do cancer cells achieve this? For decades, this question plagued scientists everywhere. But over the last 20 years, scientists have uncovered a set of basic principles that govern the development of cancer ( Brock, 1993). Within each cell lies a structure called a nucleus which contains strips of material known as DNA (dioxyribonucleic acid.) Each of these strips is divided into hundreds of genes, which are the codes and templates for all the functions of the human body. Each gene specifies a sequence of amino acids that must be linked together to make a particular protein; the protein then carries our the work of the gene. Two types of genes, which are only a small fraction of the genetic material, play particularly important roles in triggering cancer. Proto-oncogenes induce cell growth and reproduction, while tumor suppressor genes inhibit it. Together, they carefully control the proliferation of cells. However, if a proto-oncogene is mutated, it can become a carcinogenic oncogene, driving excessive multiplication. Tumor suppressor genes, on the other hand, contribute to cancer when they are inactivated by mutation (Ruddon, 1995). Luckily, cancerous tumors are not caused by one little mutation in one cell they are caused by multiple mutations in a number of the cells growth-controlling genes. The number of mutations necessary can be as low as two or quite high, depending on the specific type of cancer. Generally, these mutations occur either from mistakes during cell reproduction, or due to DNA damage caused by carcinogens such as tobacco, certain poisons, and UV rays. So, why dont we all get cancer from these things right away? Consider that one of your cells is damaged by poison and becomes mutated. In order for this cell to turn into a cancer cell, the rest of the necessary mutations must also occur in this very same cell. This in itself, is fairly unlikely. It normally takes decades for an incipient tumor to collect all the mutations required for its malignant growth, which explains why the average age for cancer diagnosis is 67 (Ruddon, 1995). Why, then, do some individuals contract cancer before the typ ical age of onset? In many cases, this is explained by the inheritance of a mutation in a critical growth controlling gene. Typically, this mutation would be a very rare event. However, in this individual, the mutation is present in ALL the cells of the body, instead of in some randomly stricken cell. So, the process of tumor formation skips its first, slow step. No one can actually inherit cancer; rather, they inherit a predisposition to develop a cancer, which is why cancers do tend to run in families, but not all family members are stricken (Brock, 1993). The outlook for people with cancer has improved steadily since the beginning of the 20th century, when few cancer victims survived for very long. Today, 51% of cancer patients survive for 5 years or more, and the American Cancer Society estimates that an additional 25% of cancer deaths could be prevented with earlier diagnosis and treatment (ACS

Pay for Performance in the NFL Essay Example

Pay for Performance in the NFL Essay Example Pay for Performance in the NFL Paper Pay for Performance in the NFL Paper Statistics Project Pay for Performance in the NFL Introduction Pay for performance is a common theme throughout almost all organizations. Merit increases, performance bonuses for executives, and commissions for real estate salespeople are common examples of this concept. Even teachers’ pay in some states is linked to performance of their students. According to the Washington Post, the state of Florida instituted a policy that individual teacher’s raises and performance starting in 2007 will be tied directly to student’s scores on standardized tests. This pay for performance concept has generally been accepted by the new Obama administration and may make its way into more common usage across the United States. In corporate America, examples of pay for performance are quite common, especially for top executives. Most year end bonuses are based on individuals meeting certain criteria established by the board of directors. These bonuses can be quite substantial. According to the Proxy Statement for Meredith Corporation, the total executive bonuses for the year 2007 exceeded $2. 5 million dollars. While pay for performance seems a reasonable concept in general, it is not without its critics. In education, there are a number of critics that question the fairness of the standardized test score results as a measure of teacher performance. They worry about teaching towards the exam at the expense of the overall education of the student. The criticism from Congress and much of the population of the United States over the bonuses paid to AIG executives questions how performance is actually measured. This paper will attempt to partially address the issue of pay for performance in professional sport, specifically in the National Football League. Many different positions in football are difficult to obtain good performance measures. Offensive lineman, special teams players especially do not have good measures of individual performance that are tracked. This analysis will focuses on two groups of NFL players, quarterbacks and running backs where individual performance measures are readily available. Analytical Technique A correlation study will be done on a variety of performance measures and the salaries of both NFL quarterbacks and running backs to see which of the individual performance measures are most closely related to the individuals salaries. The assumption will be that the current salary is based on last year’s performance. In addition to the correlation study, a multiple regression model with the best performance measures will be used to explain the relationship between the measures and salaries. This could be potentially used as a basis of predicting next year’s salary for those players that are in contract discussions or are entering the market as free agents. The data for the study will be obtained from two primary sources, ESPN. com which tracks player performance measures for a number of years, and USATODAY. com for player salaries. Professional football players are compensated in a number of ways, base salary, signing bonus, and other bonuses. This study will be using base salary as the pay in the pay for performance analysis. Performance measures for quarterbacks will include: completion percentage, total passing yardage, touchdown completions, interceptions, and finally QB rating. Performance measures for running backs will include: total yards, yards per game, touchdowns, and fumbles lost. While other measures are collected it is felt that these are the most appropriate performance measures to use for both categories of NFL players. A sample of 22 NFL quarterbacks from the 2007 season was selected while a sample of 13 NFL running backs from 2007 was used. RESULTS NFL quarterbacks: Pearson’s correlation coefficients for all variables in the study were run and are presented in the table below: |   |PCT |YDS |TD |INT |RAT |Salary | |PCT |1 | | | | | | |YDS |0. 43677 |1 | | | | | |TD |0. 230412 |0. 843951 |1 | | | | |INT |-0. 31751 |0. 475031 |0. 247018 |1 | | | |RAT |0. 639073 |0. 45897 |0. 703364 |-0. 41675 |1 | | |2008 Salary |0. 211532 |0. 562896 |0. 428047 |0. 276031 |0. 265671 |1 | As can be seen in the above table the strongest correlation exists between salary and total yards passing (0. 562896) and the number of touchdowns (0. 428047). The other variables have very weak relationships between themselves and salary and will be excluded from further analysis. It seems that only total passing yards is an important variable in understanding the relationship between quarterback’s salary and on field performance. A second part of the study is to use a regression model to predict the next periods salary for free agents and other players whose contracts are up for negotiation. It could be a valuable tool in beginning negotiations between the player and team owner. Since only two variables had anything more than a very weak relationship with salary, two regressions will be run. The first is a simple linear regression with yards passing as the independent variable and the second is a multiple regression with number of touchdowns included. The regression analysis is presented below: Simple linear regression using yards: |Regression Statistics | | | | |Multiple R |0. 62896387 | | | | |R Square |0. 316852343 | | | | |Observations |22 | | | | | | | | | | |   |Coefficients |Standard Error |t Stat |P-value | |Intercept |-1267325. 07 |1976273. 783 |-0. 64127 |0. 528628 | |YDS |1839. 467659 |603. 9569583 |3. 045693 |0. 006383 | Multiple regression using yards and touchdowns: |Regression Statistics | | | | |Multiple R |0. 569677436 | | | | |R Square |0. 24532381 | | | | |Observations |22 | | | | | | | | | | |   |Coefficients |Standard Error |t Stat |P-value | |In tercept |-1596606. 7 |2137031. 816 |-0. 747114146 |0. 464141 | |YDS |2290. 32518 |1148. 639878 |1. 993690591 |0. 060741 | |TD |-50963. 9896 |109649. 6343 |0. 464789417 |0. 647365 | The multiple regression will be excluded from use because the sign of the coefficient is negative, implying that the more touchdowns thrown the lower the salary. This is not logical. The most likely cause is that relationship between total yards passing and touchdowns is stronger than the correlation between touchdowns and salary. This could cause the regression coefficient for touchdowns to be unreliable. The regression equation provides only marginal explanatory power, based on the R square this equation using total yards only explains 31. 68% of salary for an NFL quarterback leaving over 68% of salary unexplained. It usefulness as a tool in negotiation would seem to be very limited. NFL running backs: Pearson’s correlation coefficients for all variables in the study were run and are presented in the table below    |YDS |AVG |TD |FUM |Salary | |YDS |1 | | | | | |AVG |0. 196119 |1 | | | | |TD |0. 382323 |0. 466749 |1 | | | |FUM |0. 017765 |0. 069592 |-0. 31995 |1 | | |Salary |0. 571773 |0. 260196 |0. 38083 |-0. 05109 |1 | Only the total yards gained seem to have anything but a weak relationship with salary. The number of touchdowns being somewhat explanatory of salary and will be used in the multiple regression. Since only two variables had anything more than a very weak relationship with salary, two regressions will be run. The first is a simple linear regression with yards rushing as the independent variable and the second is a multiple regression with number of touchdowns included as well. The regression analysis is presented below: Simple linear regression using yards: Regression Statistics | | | | |Multiple R |0. 57177269 | | | | |R Square |0. 326924009 | | | | | | | | | | |   |Coefficients |Standard Error |t Stat |P-value | |Intercept |-1273523. 69 |1812128. 448 |-0. 702777759 |0. 496798 | |YDS |3659. 184626 |1583. 057254 |2. 311467016 |0. 041192 | Multiple regression using yards and touchdowns: |Regression Statistics | | | | |Multiple R |0. 598119739 | | | | |R Square |0. 57747222 | | | | |Ob servations |13 | | | | | | | | | | |   |Coefficients |Standard Error |t Stat |P-value | |Intercept |-1191870. 48 |1860286. 025 |-0. 64069 |0. 536128 | |YDS |3194. 299878 |1755. 207634 |1. 819899 |0. 098793 | |TD |64585. 6109 |93229. 10033 |0. 692765 |0. 504227 | The multiple regression will be used since it is marginally better in explanatory power than the simple regression model The regression equation provides only marginal explanatory power, based on the R square this equation using total yards only explains 35. 77% of salary for an NFL quarterback leaving over 64% of salary unexplained. It usefulness as a tool in negotiation would seem to be very limited. Conclusion While there seems to be a relationship between player salaries and total yardage for both quarterbacks and running backs, the relationship is not very strong. The use of individual statistics does not seem to explain the greatest proportion of player salaries. It does not seem as if trying to use individual performance measures provides much important information on the value of the player to the team as measured by salary. This could be due to a number of issues. Possibly base salary is not the appropriate measure for player compensation. Maybe the owners look at improvement in individual performance measures over time or the average of the performance measures over time. We also need to consider that qualitative factors play a role in player salaries. It could be the so called â€Å"star power† of the player as an entertainment value. Or maybe the owners do not look at the individual statistics but rather the ability of the player to improve overall team performance. Is the owner actually looking at numbers put up by the player or is the owner estimating how many more games can we win by having this player? Sample Data Quarteracks |NAME |PCT |YDS |TD |INT |RAT |salary | |Tom Brady QB, NWE |68. 9 |4806 |50 |8 |117. | |Tomlinson RB, SDG |1474 |4. 7 |15 |0 |$5,750,000 | | Peterson RB, MIN |1341 |5. 6 |12 |4 |$2,821,320 | |Willie Parker RB, PIT |1316 |4. 1 |2 |4 |$2,900,000 | |Jamal Lewis RB, CLE |1304 |4. 4 |9 |4 |$1,400,000 | |E. James RB, ARI |1222 |3. 8 |7 |4 |$5,000,000 | |Fred Taylor RB, JAC |1202 |5. 4 |5 |2 |$4,000,000 | |Thomas Jones RB, NYJ |1119 |3. |1 |2 |$2,000,000 | |M. Lynch RB, BUF |1115 |4 |7 |1 |$2,635,770 | |Frank Gore RB, SFO |1102 |4. 2 |5 |3 |$2,562,000 | |E. Graham RB, TAM |898 |4 |10 |0 |$1,500,000 | |D. Foster RB, CAR |876 |3. 5 |3 |5 |$1,903,120 | |C. Taylor RB, MIN |844 |5. 4 |7 |5 |$3,000,000 | |L. Maroney RB, NWE |835 |4. 5 |6 |0 |$1,571,720 |

Leading Organizations and Competing in a Global Flat World Essay

Leading Organizations and Competing in a Global Flat World - Essay Example Adaptation to changing requirements while maintaining standards is a challenge of the present. Organizations cannot stay stagnant, they have to perform in a dynamic environment and are forced to continually learn in order to survive. A central concept taken into consideration in the present analysis is the 'learning organization'. The concept stresses that organizations have the ability to improve their bottom-line results and embrace new requirements by developing a culture of learning and adaptation. This can be done with best results, as will be argued further on, at a cultural level, the level of mental models (or views on organizational realities). But it can also be implemented at the individual level, with strategic implication for the career. The concept of learning organization is strictly connected to the concept of quality, referring both to the end products of an organization as well as to its overall activity and performance. Different organizations have different perspectives on quality (different models), each allowing a degree of learning capacity. The ideal, is the total quality mental model diffused in the culture of learning organizations, which states that quality represents a transformation that acts on a deep level within the organization, changing the way people think and work together, what management values and rewards, the way success is measured: "all of us collaborate to design and operate a seamless value-adding system which incorporates quality control, customer service, process involvement, supplier relationship, and good relations with the communities in which we operate" (these being inferior or partial levels of understanding of the concept of quality) (Albert 49). In today's ever changing business and social environments, organizational design is a serious challenge for all managers, irrespective or organization size. Managers recognize that organizational design is critical to performance and must accommodate change and new market or social requirements. This is why career planning has become so difficult. The overall learning behavior of an organization depends on its entire structure, not merely on the sum of its parts. It is important to focus on the whole rather than on specific events that can be misleading. The broader scheme of things is always determined by a complex set of factors. What lacks from the picture is the relationship between parts, that can determine complete different outcomes. Organizations are open systems that relate to their environments and learn from them. There is a circular relationship between the overall system and its parts. There are some patterns that repeat over and over again in a given system. Learning fr om the past receives whole new meanings from this perspective. A careful analysis is capable of determining trends of development and predicting specific directions of evolution. This can be achieved only by taking a look at the whole, at the entire system. But in order to transform the vision shared by an organization, all the mental models of its members have to be slowly directed to a coherent common view on key functional areas of the organization. And a change of this size can only be done at a cultural level. The corporate culture is a vital part of the corporate identity, along with the corporate overall goal, the objectives, the organizational structure