Air Safety essays

Air Safety essays Now there is roughly one major accident per 1 million flights with increased air traffic by 2015 there would be one fatal crash a week. (CNN.com) So in the near future if stricter regulations are not placed on the people building, inspecting and flying these planes. Future airplane crashes will be responsible for killing hundreds of people a week. Air safety is also important because it effects Americas economy. American airlines alone logged 9.5 million scheduled flights in 1997and had 625 passengers. (Gaffney There are four areas where air safety needs to be improved. The most important area is human error which has caused over 70 percent of airline accidents since 1950. Other important areas of air safety is finding better and more accurate ways of predicting the weather. Security needs to be increased in the ways of detecting weapons and explosives. The final area of air safety that needs to be improved is the Since 1950 over 70 percent of airline accidents have been caused by Human error. The reason is simple while all other aspects of air travel have been improved, human behavior still has its age-old imperfections. Which is why the Gore Commission spot lighted the need for more work on the human side of aviation safety research. Government and industry aviation research should emphasize human factors and training. The Gore Commission said to Bill Clinton in its Human error was responsible for the Deaths of 583 people on March 27 which was the worst commercial air disasters in history. The disaster happened when KLM and Pan Am pilots did not see each others airplanes, because of thick fog, and collided. (Two 747 jumbo jets collide at Tenerife) Most fatal accidents are caused when perfectly working aircraft are flown in to the ground which is called controlled-flight-into-terrain (CFIT). (Gaffney 45) This ...

Law Essay Example | Topics and Well Written Essays - 2000 words - 7

Law - Essay Example the UK government was in violation of the European Convention on Human Rights; the Grand Chamber of the European Court dismissed the appeal of the British government in October 2005. But as of June 2006 there has been no revision in UK law on the issue.3 Once the European Court of Human Rights ultimately rejected the British government’s inexcusable appeal in the John Hirst’s case they granted the New Labour Government with a rare and genuine opportunity to implement their much proclaimed policy of political and social inclusion.4 Until Hirst’s case, whenever any person in the United Kingdom is sentenced to imprisonment they sacrificed more than their rights or freedom. They also sacrificed their right to vote and along with it their position as citizens. Convicts remain the last primary group to be prohibited from the electorate.5 Consequently their welfare is mostly overlooked and politicians have little motivation to pay comprehensive and knowledgeable attention in penal policy.6 The electoral disentitlement of inmates is a remnant of the nineteenth century which plays no contemporary role and which is in conflict with the declared commitment of the government to social and political inclusion.7 Sentenced inmates in the UK have been stripped of their right to vote ever since the Forfeiture Act of 1870, immediately after the vote was bestowed upon multitudes of working class men in urban areas and after transportation was closed down as a court ruling.8 The forfeiture was brought in by a privileged class resolute to shun impoverished offenders away from the ballot box. In the past, the moving of lawbreakers and a quite controlled authorisation had implied that right to vote for inmates had never been a problem.9 The beginnings of the electoral banning of inmates can be traced back from the ancient concept of ‘civic death’, a sentence involving the forfeiture of citizenship rights.10 The prohibition reveals outdated and negative concepts of social

Letter of introduction Essay Example | Topics and Well Written Essays - 500 words

Letter of introduction - Essay Example One of my lifetime goals is to start my own marketing firm. Marketing is my favorite branch of business administration. I like marketing because this field of study is responsible for ensuring companies achieve their desired sales targets. The marketing efforts of an organization help your product or service get recognized in the open market. I believe that starting a marketing business is a good idea because every business enterprise needs marketing in order to achieve their goals. Performing marketing consulting services is a business model that can generate a lot of money. Another of the reasons why I want to start a business is to help out the U.S. economy. Entrepreneurs help the economy by creating jobs and paying taxes to the local and federal government. I consider myself a very giving and honest person. During my young life I have always done everything I can to help out others. I believe a lot in the value of volunteer work. In the past I have volunteered my time at a homele ss shelter in my community. This experience helped grow as a human being. Volunteer work is extremely important because there a lot of people in need in the United States. It is estimated that nearly 37 million Americans are living under the federal poverty line.

In your OWN words, prepare a Microsoft Word report to provide evidence Essay

In your OWN words, prepare a Microsoft Word report to provide evidence of advanced knowledge of a concept taught in one of week - Essay Example But what is the definition of electronic commerce? One definition of electronic commerce states that it is the use of computer networks in business, with the aim of increasing profitability and increasing market share and brand reach (Watson et al. 2008). Another definition of electronic commerce states that it is any form of business transaction done in the internet (Department of Trade and Economic Development – Government of South Australia n.d.). There may be a lot of varying definitions, but most of these definitions boil down to one common theme: the use of the internet in conducting business transactions, as opposed to physical contact in doing business. One of the main reasons why electronic commerce has already been one of the most popular choices for many businesses is because of the fact that electronic commerce has been able to improve organizational efficiency and other logistical problems in conducting business. ... on logistical expenses, especially on marketing their respective products, due to the fact that maintaining a website and investing on search engine optimization services to reach out on prospective customers online is more cost-effective, as compared to physically distributing marketing tools to customers or hiring advertising firms to advertise on tri-media. In this case, lessening such costs will surely increase the profitability of any business. In addition, by using electronic commerce, clients can now have much more convenience in giving feedbacks to the goods and services that they receive, especially through online surveys that online businesses actually uses. This mechanism will surely help businesses in evaluating their own performances and giving much better services to clients. In terms of gaining a larger market share, the best thing about electronic commerce is that it has the ability to reach out to millions of customers worldwide in just a matter of seconds. This woul d be virtually impossible for other businesses who are not engaged in electronic commerce. In addition, it is important to take note that consumers nowadays are more comfortable in looking for goods and services through the internet, given its speed, reliability, and the convenience that it offers to consumers. Also, because electronic commerce relies much on the internet, it would be able to deliver services to clients at a much faster pace than usual. Take note that consumers nowadays can now do their shopping in the comfort of their own homes, and that companies can now provide the specific needs of customers in just a matter of clicks, without bothering them to go physically into the merchandising stores of companies. This will surely make businesses deliver their goods and services

Is Money Happiness Essay Example for Free

Is Money Happiness Essay Benjamin Franklin, a well known Founding Father of the United States among other avenues of pursuit once said, â€Å"Money has never made man happy, nor will it, there is nothing in its nature to produce happiness. The more of it one has the more one wants. † This excerpt founds the question of whether or not money can buy happiness; and can it really? In no way can monetary value equate to true serenity. To closely examine the question in subject, the definition of money and its origin must be examined in coherence with what happiness really is. To compare the two contrary parties, the investigation of state facts of Swaziland, a â€Å"poor† country, and The United States, a â€Å"wealthy† country will be explored. A final analysis and comparison will close the article. A monetary value simply cannot purchase a state of being; that is money cannot buy happiness. II. Money and Happiness A. What is money? 1. Money is simply a unit of exchange in which the transfer of goods and services is exchanged for. Money is synonymous with currency and cash. (Wikipedia: Money) 2. Money allows for the creation of set values of goods and services, and facilitates those trades between producer and worker and consumer and recipient. 3. Money can be recognized as any form of currency, or a medium of exchange a. Shells b. Bones and fossils c. Tokens d. Special rocks and minerals B. What is happiness? 1. Happiness is defined by the Merriam Webster dictionary as â€Å"a state of well-being and contentment or a pleasurable or satisfying experience. (Merriam Webster Dictionary) C. A Time Before Money. Generally, historians agree that money was created at approximately 100,000 B.C. (Wikipedia: History of Money) 2. Before that time, a system of bartering was the only way goods or services could be exchanged. a. â€Å"Barter is a type of trade that doesnt use any medium of exchange, in which goods or services are exchanged for other goods and/or services. † (Wikipedia: Barter) b. For example, if a farmer needed an iron plow for his field, he would have to find a blacksmith that needed apples who then in turn could fabricate a plow for the farmer. In other words, they had to have a coincidence of wants. The transition period between mainstream bartering and a monetary system seems to have emerged from Swaziland at approximately 100,000 B. C. a. This emergence of money was in the simple form of red ochre i. Red ochre are pigments made from naturally tinted clay. Chemically, it is hydrated iron oxide. (Answers: Red Ochre) C. The Symbolic Meaning of Money 1. There are many variants of the true symbolism of money a. One theory directly refers to coin money i. The shape of coin money is generally round. This shape represents the eternal continuation of currency. This round shape also represents the world; again, in it’s ever continuing and developing cycle iii. Together, these ideals represent the ongoing continuation of money throughout the world. b. Another theory applies to paper money i. The square shape that paper money ideally holds is representative of a solid foundation, trust, and solidness. ii. Often times, faces of strong leaders or portraits of influential people will be printed in the currency. These leaders often created the foundation (pioneering or renewed) of any given state, and thus are represented by and represent the country.

Employee Performance Analysis

Employee Performance Analysis Project Outline: This research is about the Employee performance in an organization. Data related to several factors such as Employee Productivity, Customer Satisfactions Scores, Accuracy Scores, Experience and Age of Employees is taken into consideration. Statistical methods are used to identify if there is any impact of Age and Experience of Employees on factors such as Productivity, Customer Satisfaction and Accuracy. Theoretical Framework: XYZ Corporation operating out of Illinois, US want to find out if the age and experience of employees have an impact on his/her performance. They have hired an external consultant to study the impact of these two factors (age and experience) on the performance metrics of the employees. According to the results of the research conducted by this external consultant, XYZ Corporate will design a strategy of recruiting the right talent which will have maximum performance. Design and Methodology: Design and Methodology used by the external consultant include identifying the various performance factors common across different businesses within XYZ Corporation. The performance measures common for all businesses included: Customer Satisfaction Scores Accuracy Scores Productivity The consultants decided to study the impact of age of employees and their experience on the above factors by using statistical methods. Details on participants and sampling methods: Sampling Methods: Sampling is the process of selecting a small number of elements from a larger defined target group of elements. Population is the total group of elements we want to study. Sample is the subgroup of the population we actually study. Sample would mean a group of ‘n’ employees chosen randomly from organization of population ‘N’. Sampling is done in situations like: We sample when the process involves destructive testing, e.g. taste tests, car crash tests, etc. We sample when there are constraints of time and costs We sample when the populations cannot be easily captured Sampling is NOT done in situations like: We cannot sample when the events and products are unique and cannot be replicable Sampling can be done by using several methods including: Simple random sampling, Stratified random sampling, Systematic sampling and Cluster sampling. These are Probability Sampling Methods. Sampling can also be done using methods such as Convenience sampling, Judgment sampling, Quota sampling and Snowball sampling. These are non-probability methods of sampling. Simple random sampling is a method of sampling in which every unit has equal chance of being selected. Stratified random sampling is a method of sampling in which stratum/groups are created and then units are picked randomly. Systematic sampling is a method of sampling in which every nth unit is selected from the population. Cluster sampling is a method of sampling in which clusters are sampled every tth time. For the non-probability methods, Convenience sampling relies upon convenience and access. Judgment sampling relies upon belief that participants fit characteristics. Quota sampling emphasizes representation of specific characteristics. Snowball sampling relies upon respondent referrals of others with like characteristics. In our research, the consultant organization used a Simple Random Sampling method to conduct the study where they chose about 75 random employees and gathered data of age, experience, their Customer Satisfaction scores, their Accuracy Scores and their Productivity scores. The employees were bifurcated into 3 age groups, namely, 20 – 30 years, 30 – 40 years and 40 – 50 years. Similarly, they were also bifurcated into 3 experience groups, namely, 0 – 10 years, 10 – 20 years and 20 – 30 years. Data Analysis: Below are the different data analysis options used by the consultant: Impact of Age on Accuracy Impact of Experience on Accuracy Impact of Age on Customer Satisfaction Impact of Experience on Customer Satisfaction Impact of Age on Productivity Impact of Experience on Productivity For each of the above statistical analysis, we will need to use Hypothesis testing methods. Hypothesis testing tells us whether there exists statistically significant difference between the data sets for us to consider to represent different distribution. The difference that can be detected using hypothesis testing is: Continuous Data Difference in Average Difference in Variation Discrete Data Difference in Proportion Defective We follow the below steps for Hypothesis testing: Step 1 : Determine appropriate Hypothesis test Step 2 : State the Null Hypothesis Ho and Alternate Hypothesis Ha Step 3 : Calculate Test Statistics / P-value against table value of test statistic Step 4 : Interpret results – Accept or reject Ho The mechanism of Hypothesis testing involves the following: Ho = Null Hypothesis – There is No statistically significant difference between the two groups Ha = Alternate Hypothesis – There is statistically significant difference between the two groups We also have different types of errors that can be caused if we are using hypothesis testing. The errors are as noted below: Type I Error – P (Reject Ho when Ho is true) = ÃŽ ± Type II Error P (Accept Ho when Ho is false) = ÃŽ ² P Value – Statistical Measure which indicates the probability of making an ÃŽ ± error. The value ranges between 0 and 1. We normally work with 5% alpha risk, a p value lower than 0.05 means that we reject the Null hypothesis and accept alternate hypothesis. Let’s talk a little about p-value. It is a Statistical Measure which indicates the probability of making an ÃŽ ± error. The value ranges between 0 and 1. We normally work with 5% alpha risk. ÃŽ ± should be specified before the hypothesis test is conducted. If the p-value is > 0.05, then Ho is true and there is no difference in the groups (Accept Ho). If the p-value is < 0.05, then Ho is false and there is a statistically significant difference in the groups (Reject Ho). We will also discuss about the types of hypothesis testing: 1-Sample t-test: It’s used when we have Normal Continuous Y and Discrete X. It is used for comparing a population mean against a given standard. For example: Is the mean Turn Around Time of thread à ¯Ã¢â‚¬Å¡Ã‚ £15 minutes. 2-Sample t-test: It’s used when we have Normal Continuous Y and Discrete X. It is used for comparing means of two different populations. For example: Is the mean performance of morning shift = mean performance of night shift. ANOVA: It’s used when we have Normal Continuous Y and Discrete X. It is used for comparing the means of more than two populations. For example: Is the mean performance of staff A = mean performance of staff B = mean performance of staff C. Homogeneity Of Variance: It’s used when we have Normal Continuous Y and Discrete X. It is used for comparing the variance of two or more than two populations. For example: Is the variation of staff A = variation of staff B = variation of staff C. Mood’s Median Test: It’s used when we have Non-normal Continuous Y and Discrete X. It is used for Comparing the medians of two or more than two populations. For example: Is the median of staff A = median of staff B = median of staff C. Simple Linear Regression: It’s used when we have Continuous Y and Continuous X. It is used to see how output (Y) changes as the input (X) changes. For example: If we need to find out how staff A’s accuracy is related to his number of years spent in the process. Chi-square Test of Independence: It’s used when we have Discrete Y and Discrete X. It is used to see how output counts (Y) from two or more sub-groups (X) differ. For example: If we want to find out whether defects from morning shift are significantly different from defects in the evening shift. Let’s look at each of the analysis for our research: Impact of Age on Accuracy Practical Problem Hypothesis Statistical Tool Used Conclusion Is Accuracy impacted by Age of Employees H0: Accuracy is independent of the Age of Employees H1: Accuracy is impacted by Age of Employees One-Way ANOVA p-value < 0.05 indicates that performance measure of accuracy is impacted by age factor One-way ANOVA: Accuracy versus Age Bucket Source DF SS MS F P Age Bucket 2 0.50616 0.25308 67.62 0.000 Error 72 0.26946 0.00374 Total 74 0.77562 S = 0.06118 R-Sq = 65.26% R-Sq(adj) = 64.29% Individual 95% CIs For Mean Based on Pooled StDev Level N Mean StDev ++++ 20 30 years 26 0.75448 0.06376 (*) 30 40 years 26 0.85078 0.07069 (*) 40 50 years 23 0.95813 0.04416 (*) ++++ 0.770 0.840 0.910 0.980 Pooled StDev = 0.06118 Boxplot of Accuracy by Age Bucket Conclusion: P-value of the above analysis < 0.05 which indicates that we reject the null hypothesis and thus, the performance measure of accuracy is impacted by age of employees. As the age increases, we observe that the accuracy of the employees also increases. Impact of Experience on Accuracy Practical Problem Hypothesis Statistical Tool Used Conclusion Is Accuracy impacted by Experience of Employees H0: Accuracy is independent of the Experience of Employees H1: Accuracy is impacted by Experience of Employees One-Way ANOVA p-value < 0.05 indicates that performance measure of accuracy is impacted by experience factor One-way ANOVA: Accuracy versus Experience Bucket Source DF SS MS F P Experience Bucke 2 0.53371 0.26685 79.42 0.000 Error 72 0.24191 0.00336 Total 74 0.77562 S = 0.05796 R-Sq = 68.81% R-Sq(adj) = 67.94% Individual 95% CIs For Mean Based on Pooled StDev Level N Mean StDev -++++ 0 10 years 24 0.74403 0.05069 (*) 10 20 years 23 0.84357 0.05354 (*) 20 30 years 28 0.94696 0.06660 (*) -++++ 0.770 0.840 0.910 0.980 Pooled StDev = 0.05796 Boxplot of Accuracy by Experience Bucket Conclusion: P-value of the above analysis < 0.05 which indicates that we reject the null hypothesis and thus, the performance measure of accuracy is impacted by experience of employees. As the experience increases, we observe that the accuracy of the employees also increases. Impact of Age on Customer Satisfaction Practical Problem Hypothesis Statistical Tool Used Conclusion Is Customer Satisfaction Score impacted by Age of Employees H0: Customer Satisfaction Score is independent of the Age of Employees H1: Customer Satisfaction Score is impacted by Age of Employees One-Way ANOVA p-value < 0.05 indicates that performance measure of Customer Satisfaction score is impacted by age factor One-way ANOVA: Customer Satisfaction versus Age Bucket Source DF SS MS F P Age Bucket 2 49.51 24.75 18.92 0.000 Error 72 94.23 1.31 Total 74 143.74 S = 1.144 R-Sq = 34.44% R-Sq(adj) = 32.62% Individual 95% CIs For Mean Based on Pooled StDev Level N Mean StDev ++++ 20 30 years 26 6.906 1.164 (-*) 30 40 years 26 8.041 1.156 (*-) 40 50 years 23 8.907 1.107 (*) ++++ 7.20 8.00 8.80 9.60 Pooled StDev = 1.144 Boxplot of Customer Satisfaction by Age Bucket Conclusion: P-value of the above analysis < 0.05 which indicates that we reject the null hypothesis and thus, the performance measure of Customer Satisfaction Score is impacted by age of employees. As the age increases, we observe that the Customer Satisfaction Score of the employees also increases. Impact of Experience on Customer Satisfaction Practical Problem Hypothesis Statistical Tool Used Conclusion Is Customer Satisfaction Score impacted by Experience of Employees H0: Customer Satisfaction Score is independent of the Experience of Employees H1: Customer Satisfaction Score is impacted by Experience of Employees One-Way ANOVA p-value < 0.05 indicates that performance measure of Customer Satisfaction score is impacted by experience factor One-way ANOVA: Customer Satisfaction versus Experience Bucket Source DF SS MS F P Experience Bucke 2 51.20 25.60 19.92 0.000 Error 72 92.54 1.29 Total 74 143.74 S = 1.134 R-Sq = 35.62% R-Sq(adj) = 33.83% Individual 95% CIs For Mean Based on Pooled StDev Level N Mean StDev ++++- 0 10 years 24 7.035 1.277 (*) 10 20 years 23 7.570 0.922 (*) 20 30 years 28 8.948 1.160 (-*-) ++++- 7.20 8.00 8.80 9.60 Pooled StDev = 1.134 Boxplot of Customer Satisfaction by Experience Bucket Conclusion: P-value of the above analysis < 0.05 which indicates that we reject the null hypothesis and thus, the performance measure of Customer Satisfaction Score is impacted by experience of employees. As the experience increases, we observe that the Customer Satisfaction Score of the employees also increases. Impact of Age on Productivity Practical Problem Hypothesis Statistical Tool Used Conclusion Is Productivity impacted by Age of Employees H0: Productivity is independent of the Age of Employees H1: Productivity is impacted by Age of Employees One-Way ANOVA p-value < 0.05 indicates that performance measure of Productivity is impacted by experience factor One-way ANOVA: Productivity versus Age Bucket Source DF SS MS F P Age Bucket 2 0.74389 0.37194 194.56 0.000 Error 72 0.13765 0.00191 Total 74 0.88153 S = 0.04372 R-Sq = 84.39% R-Sq(adj) = 83.95% Individual 95% CIs For Mean Based on Pooled StDev Level N Mean StDev ++++ 20 30 years 26 0.93959 0.04287 (-*) 30 40 years 26 0.81511 0.05831 (-*-) 40 50 years 23 0.69291 0.01747 (*-) ++++ 0.720 0.800 0.880 0.960 Pooled StDev = 0.04372 Boxplot of Productivity by Age Bucket Conclusion: P-value of the above analysis < 0.05 which indicates that we reject the null hypothesis and thus, the performance measure of Productivity is impacted by age of employees. As the age increases, we observe that the Productivity of the employees decreases. Impact of Experience on Productivity Practical Problem Hypothesis Statistical Tool Used Conclusion Is Productivity impacted by Experience of Employees H0: Productivity is independent of the Experience of Employees H1: Productivity is impacted by Experience of Employees One-Way ANOVA p-value < 0.05 indicates that performance measure of Productivity is impacted by experience factor One-way ANOVA: Productivity versus Experience Bucket Source DF SS MS F P Experience Bucke 2 0.74024 0.37012 188.61 0.000 Error 72 0.14129 0.00196 Total 74 0.88153 S = 0.04430 R-Sq = 83.97% R-Sq(adj) = 83.53% Individual 95% CIs For Mean Based on Pooled StDev Level N Mean StDev ++++- 0 10 years 24 0.94474 0.03139 (*) 10 20 years 23 0.83120 0.05754 (*-) 20 30 years 28 0.70599 0.04118 (*-) ++++- 0.700 0.770 0.840 0.910 Pooled StDev = 0.04430 Boxplot of Productivity by Experience Bucket Conclusion: P-value of the above analysis < 0.05 which indicates that we reject the null hypothesis and thus, the performance measure of Productivity is impacted by experience of employees. As the experience increases, we observe that the Productivity of the employees decreases. Conclusion of the Analysis: As Age and Experience increases, the Accuracy and Customer Satisfaction Scores of Employees increases As Age and Experience increases, the Productivity of Employees decreases Bibliography: The data used in this analysis is self-created data using statistical software.      Research Schedule (Gantt Chart) of the Project:

Essay --

Schroeder 1 Hunter Schroeder Ms. Caturano Honors English 9 January 2013 Independent Reading: Connections Connection: Going to Extremes For Love The Hunger Games is set in world called Panem that was once America, before the Capitol was overcome in some unexplained, apocalyptic war. As punishment for that aggression, the remains were divided into 12 districts. Every year each district has to send one boy and one girl between 12 and 18 years old, chosen by lottery, to compete in a nationally televised event called â€Å"the Hunger Games.† The purpose of this is to create a mass killing spree with only one survivor. What really twists this storyline is when two tributes from the same district fall in love and fight to protect each other until they are the last ones left in the games. The two go to extreme measures to keep one another out of danger. â€Å"You're still trying to protect me. Real or not real," he whispers. "Real," I answer. "Because that's what you and I do, protect each other† (Suzanne Collins, The Hunger Games). Because the hunger games is such a gruesome event, the things Katniss and Peeta did for each other aren't typical things couples would give up for eachother. These two would share supplies and weapons along with going as far as killing off an attacking enemy. Seeing how far Katniss and Peeta were willing to go to be with each other gave me a better understanding of why Romeo and Juliet fought to never let anything get in between them. Even though Romeo is a Montague and Juliet is a Capulet and the two families have an ancient rivalry they did not let that stand in the way of being with each other. â€Å"O Romeo, Romeo! Wherefore art thou Romeo? Deny thy father and refuse thy name! Or, if thou wilt not, be but sworn my love, ... ...ena. â€Å"You love me. Real or not real?" I tell him, "Real.† (Suzanne Collins, The Hunger Games). Even though Katniss and Peeta are so young, they truly believe that they are in love. This changes my opinion of Romeo and Juliet and leads me to believe that they really could have been in love so young. Romeo and Juliet were a mere 13 and 14 when they claimed to fall madly in love and get married, but this was around the normal marrying age for that time period. â€Å"But, soft! what light through yonder window breaks? It is the east, and Juliet is the sun† (Shakespeare, Romeo and Juliet). In this time period, young people were very romantically mature and knew about love and did not just marry anyone because there was no such thing as divorce. This makes me think that Romeo and Juliet may have been in love and people were just very critical because the were of rival families.