...------------------------------------3, 4 ------------------------------------4, 5 ------------------------------------5, 6 -----------------------------------6, 7 CONCLUSION ------------------------------------- 8, 9 REFERENCE LIST --------------------------------------- 10 PURPOSE OF PAPER Discussing the positive and negative impacts on the growth of events industry, analysising the different explanations might have in the implications of this phenomenon. INTRODUCTION Over the last 20 years, the events industry has grown significantly. This phenomenon appeared might because events industry could bring a great deal of positive impacts on society, environment and economy. Events sustainability has played an important role in the increasing of events industry. However, it is predicted that there will be a growth of 5.5% per year through 2011 which used to be 6.2% since 2003, this slightly decline of growth rate might causes by the social, environmental, economical negative implications, the global political unrest and natural disaster can also create a negative impact to event industry. Brief definition of sustainability Events sustainability is consists of three parts which are social, environmental and economic, because nature's resources are not never-ending, sustainability can find an enduring, balanced approach to economic activity, environmental responsibility and social progress. (BSI,2007,P.7) Sustainability is playing a very important...
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...Sport Events Industry Individual Report Words: 2822 Special events have always been part of human history. Anthropologists have traced human civilization for tens of thousands of years, and at the heart of their observations are the ‘special events’ that typify and explain tribal behavior of that time and place. At this point, there is no question that special events have taken on all aspects of an “industry” in that their organization and management are the underlying support for marking the local and domestic details of our lives. Events are not restricted to festive celebrations but can include a variety of gatherings, serious or happy, and religious or cultural, including meetings and conferences, expositions and trade shows, private and public special events, art entertainment and sport events, media or corporate events, and events of various sizes. Events are given a lot of definitions but the most general one that characterizes events is “Temporary occurrence with a predetermined beginning and end. It is unique stemming from the blend of management, programme setting and people.” (Getz, 2005). On the following report there is a wide overview of the sport events sector, that will examine the history and development, the factors that support this sector, the impacts of these events, career opportunities that will arise through this sector and future trends. History and Development Sport reflects the country in which it is played, so inevitably the history of sport...
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... Rawski University of Pittsburgh September 1999 As the People's Republic of China celebrates its fiftieth anniversary, economists look back on a remarkable kaleidoscope of events and policy shifts that, despite episodes of vast suffering and waste, have brought enormous material benefits to China's teeming masses. The economy inherited by China's new Communist leaders in 1949 was overwhelmingly agrarian, ravaged by twelve years of warfare, and wracked by hyperinflation. Despite the strains imposed by China's participation in the Korean War, the new government quickly resolved difficult short-term economic obstacles and embarked upon a long-term process of socialization and development. China's experience of socialist planning, which roughly coincides with the period from 1949 until the death of Mao Zedong in 1976, left a mixed economic legacy. Like other socialist regimes, China's new leaders poured resources into activities and industries linked to the expansion of national power. Production of steel, machinery, and building materials multiplied prodigiously. China succeeded in fabricating nuclear and thermonuclear weapons. Although these advances relied initially on technical and financial support from the Soviet Union and its East European allies, China's success in penetrating new industries and mastering new technologies following the withdrawal of Soviet aid demonstrated that a succession of Five-Year Plans had propelled China to a new level of development. In addition...
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...Discuss the Importance of Teams, Their Characteristics and Their Development to the Successful Delivery of Events The purpose of this essay is to explain the importance of teams within the event industry. It will go into depth explaining the different theories behind the importance of teams within events. Team work is a collaboration between individuals with different skills. It is key element in decentralized organization. Teams exist for efficiency and also because humans need continual motivation and emotional support which sustains work-flow and adds creativity to work. There are two types of team work groups: Formal groups, which are structured to pursue a specific task, and Informal groups, which occur naturally in response to interests. While it is possible to learn a lot from informal groups in terms of leadership and motivation, this essay will concentrate mostly on formal groups (Janis, I. L. 1982). A successful event is one that achieves a set of pre-determined goals. These goals can be to make a certain amount of financial profit, to reach a certain number of people in attendance or to raise awareness. (Brown, N. 2013). A good example of this would be Glastonbury festival 2013. After selling all 135,000 tickets in just under an hour and a half, the event was a success grossing profits of 32.2 million. The majority of the profit was donated to multiple charities (NME magazine 2013). Another example of this would be Notting Hill carnival 2012. As opposed to...
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...Unit 2 DB Subjective Probability “ A probability derived from an individual's personal judgment about whether a specific outcome is likely to occur. Subjective probabilities contain no formal calculations and only reflect the subject's opinions and past experience.” (investopedia.com, 2013) There are three elements of a probability which combine to equal a result. There is the experiment ,the sample space and the event (Editorial board, 2012). In this case the class is the experiment because the process of attempting it will result in a grade which could vary from an A to F. The different grades that can be achieved in the class are the sample space. The event or outcome is the grade that will be received at the end of the experiment. I would like to achieve an “A” in this class but due to my lack of experience in statistical analysis, my hesitation towards advanced mathematics, and the length of time it takes for me to complete my course work a C in this class may be my best result. I have a 1/9 chance or probability to receive an “A” in the data range presented to me which is (A,A-,B,B-,C,C-,D,D- AND F). By the grades that have been posted I would say that the other students have a much better chance of receiving a better grade than mine. I have personally use subjective probability in my security guard business in bidding on contracts based on the clients involved , the rates that I charge versus the rates other companies charge and the amount of work involved...
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... Probability – the chance that an uncertain event will occur (always between 0 and 1) Impossible Event – an event that has no chance of occurring (probability = 0) Certain Event – an event that is sure to occur (probability = 1) Assessing Probability probability of occurrence= probability of occurrence based on a combination of an individual’s past experience, personal opinion, and analysis of a particular situation Events Simple event An event described by a single characteristic Joint event An event described by two or more characteristics Complement of an event A , All events that are not part of event A The Sample Space is the collection of all possible events Simple Probability refers to the probability of a simple event. Joint Probability refers to the probability of an occurrence of two or more events. ex. P(Jan. and Wed.) Mutually exclusive events is the Events that cannot occur simultaneously Example: Randomly choosing a day from 2010 A = day in January; B = day in February Events A and B are mutually exclusive Collectively exhaustive events One of the events must occur the set of events covers the entire sample space Computing Joint and Marginal Probabilities The probability of a joint event, A and B: Computing a marginal (or simple) probability: Probability is the numerical measure of the likelihood that an event will occur The probability of any event must be between 0 and 1, inclusively The sum of the...
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...= {-20, -19, …, -1, 0, 1, …, 19, 20} Number of people arriving at a bank in a day: S = {0, 1, 2, …} Inspection of parts till one defective part is found: S = {d, gd, ggd, gggd, …} Temperature of a place with a knowledge that it ranges between 10 degrees and 50 degrees: S = {any value between 10 to 50} Speed of a train at a given time, with no other additional information: S = {any value between 0 to infinity} 4 Sample Space (cont…) Discrete sample space: One that contains either finite or countable infinite set of outcomes • Out of the previous examples, which ones are discrete sample spaces??? Continuous sample space: One that contains an interval of real numbers. The interval can be either finite or infinite 5 Events A collection of certain sample points A subset of the sample space Denoted by ‘E’ Examples: • Getting an odd number in dice throwing experiment S = {1, 2, 3, 4,...
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...the stage where one can begin to use probabilistic ideas in statistical inference and modelling, and the study of stochastic processes. Probability axioms. Conditional probability and independence. Discrete random variables and their distributions. Continuous distributions. Joint distributions. Independence. Expectations. Mean, variance, covariance, correlation. Limiting distributions. The syllabus is as follows: 1. Basic notions of probability. Sample spaces, events, relative frequency, probability axioms. 2. Finite sample spaces. Methods of enumeration. Combinatorial probability. 3. Conditional probability. Theorem of total probability. Bayes theorem. 4. Independence of two events. Mutual independence of n events. Sampling with and without replacement. 5. Random variables. Univariate distributions - discrete, continuous, mixed. Standard distributions - hypergeometric, binomial, geometric, Poisson, uniform, normal, exponential. Probability mass function, density function, distribution function. Probabilities of events in terms of random variables. 6. Transformations of a single random variable. Mean, variance, median, quantiles. 7. Joint distribution of two random variables. Marginal and conditional distributions. Independence. iii iv 8. Covariance, correlation. Means and variances of linear functions of random variables. 9. Limiting distributions in the Binomial case. These course notes explain the naterial in the syllabus. They have been “fieldtested” on the class of 2000...
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...[pic] [pic] Markov Chain [pic] Bonus Malus Model [pic] [pic] This table justifies the matrix above: | | | |Next state | | | |State |Premium |0 Claims |1 Claim |2 Claims |[pic]Claims | |1 | |1 |2 |3 |4 | |2 | |1 |3 |4 |4 | |3 | |2 |4 |4 |4 | |4 | |3 |4 |4 |4 | | | | | | | | |P11 |P12 |P13 |P14 | | | |P21 |P22 |P23 |P24 | | | |P31 |P32 |P33 |P34 | | | |P41 |P42 |P43 |P44 | | | | ...
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...Permutations The word ‘coincidence’ is defined as an event that might have been arranged though it was accidental in actuality. Most of us perceive life as a set of coincidences that lead us to pre-destined conclusions despite believing in a being who is free from the shackles of time and space. The question is that a being, for whom time and space would be nothing more than two more dimensions, wouldn’t it be rather disparaging to throw events out randomly and witness how the history unfolds (as a mere spectator)? Did He really arrange the events such that there is nothing accidental about their occurrence? Or are all the lives of all the living beings merely a result of a set of events that unfolded one after another without there being a chronological order? To arrive at satisfactory answers to above questions we must steer this discourse towards the concept of conditional probability. That is the chance of something to happen given that an event has already happened. Though, the prior event need not to be related to the succeeding one but must be essential for it occurrence. Our minds as I believe are evolved enough to analyze a story and identify the point in time where the story has originated or the set of events that must have happened to ensure the specific conclusion of the story. To simplify the conundrum let us assume a hypothetical scenario where a man just became a pioneer in the field of actuarial science. Imagine him telling us his story in reverse. “I became...
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...presence with probability 0.99. If it is not present, the radar falsely registers an aircraft presence with probability 0.10. We assume that an aircraft is present with probability 0.05. What is the probability of false alarm (a false indication of aircraft presence), and the probability of missed detection (nothing registers, even though an aircraft is present)? A sequential representation of the sample space is appropriate here, as shown in Fig. 1. Figure 1: Sequential description of the sample space for the radar detection problem Solution: Let A and B be the events A={an aircraft is present}, B={the radar registers an aircraft presence}, and consider also their complements Ac={an aircraft is not present}, Bc={the radar does not register an aircraft presence}. The given probabilities are recorded along the corresponding branches of the tree describing the sample space, as shown in Fig. 1. Each event of interest corresponds to a leaf of the tree and its probability is equal to the product of the probabilities associated with the branches in a path from the root to the corresponding leaf. The desired probabilities of false alarm and missed detection are P(false alarm)=P(Ac∩B)=P(Ac)P(B|Ac)=0.95∙0.10=0.095, P(missed detection)=P(A∩Bc)=P(A)P(Bc|A)=0.05∙0.01=0.0005. Application of Bayes` rule in this problem. We are given that P(A)=0.05, P(B|A)=0.99, P(B|Ac)=0.1. Applying Bayes’ rule, with A1=A and A2=Ac, we obtain P(aircraft present | radar registers) =...
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...1.M/G/ Queue a. Show that Let A(t) : Number of arrivals between time (0, t] “ n should be equal to or great than k” since if n is less than k (n<k), Pk(t)=0 Let’s think some customer C, Let’s find P{C arrived at time x and in service at time t | x=(0,t)] } P{C arrives in (x, x+dx] | C arrives in (0, t] }P{C is in service | C arrives at x, and x = (0,t] } Since theorem of Poisson Process, The theorem is that Given that N(t) =n, the n arrival times S1, S2, …Sn have the same distribution as the order statistics corresponding to n independent random variables uniformly distributed on the interval (0, t) Thus, P{C is in service | C arrives between time (0, t] } Since let y=t-x, x=0 → y=t, x=t →y=o, dy=-dx Therefore, In conclusion, ------ (1) 1-a Solution Since b. let 1-b Solution ------------------------------------------------- 2. notation Page 147 in “Fundamentals of Queuing Theory –Third Edition- , Donald Gross Carl M. Harris a. b. ------------------------------------------------- ------------------------------------------------- ------------------------------------------------- 3. a. let X=service time (Random variable) and XT=total service time (Random variable) X2=X+X, X3=X+X+X, ….. f2(x2)...
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...Probability & Mathematical Statistics | “The frequency concept of Probability” | [Type the author name] | What is probability & Mathematical Statistics? It is the mathematical machinery necessary to answer questions about uncertain events. Where scientists, engineers and so forth need to make results and findings to these uncertain events precise... Random experiment “A random experiment is an experiment, trial, or observation that can be repeated numerous times under the same conditions... It must in no way be affected by any previous outcome and cannot be predicted with certainty.” i.e. it is uncertain (we don’t know ahead of time what the answer will be) and repeatable (ideally).The sample space is the set containing all possible outcomes from a random experiment. Often called S. (In set theory this is usually called U, but it’s the same thing) Discrete probability Finite Probability This is where there are only finitely many possible outcomes. Moreover, many of these outcomes will mostly be where all the outcomes are equally likely, that is, uniform finite probability. An example of such a thing is where a fair cubical die is tossed. It will come up with one of the six outcomes 1, 2, 3, 4, 5, or 6, and each with the same probability. Another example is where a fair coin is flipped. It will come up with one of the two outcomes H or T. Terminology and notation. We’ll call the tossing of a die a trial or an experiment. Where we...
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...Model Answers for Chapter 4: Evaluating Classification and Predictive Performance Answer to 4.3.a: Leftmost bar: If we take the 10% "most probable 1’s(frauds)” (as ranked by the model), it will yield 6.5 times as many 1’s (frauds), as would a random selection of 10% of the records. 2nd bar from left: If we take the second highest decile (10%) of records that are ranked by the model as “the most probable 1’s (frauds ” it will yield 2.7 times as many 1’s (frauds), as would a random selection of 10 % of the records. Answer to 4.3.b: Consider a tax authority that wants to allocate their resources for investigating firms that are most likely to submit fraudulent tax returns. Suppose that there are resources for auditing only 10% of firms. Rather than taking a random sample, they can select the top 10% of firms that are predicted to be most likely to report fraudulently (according to the decile chart). Or, to preserve the principle that anyone might be audited, they can establish differential probabilities for being sampled -- those in the top deciles being much more likely to be audited. . Answer to 4.3.c: Classification Confusion Matrix Predicted Class 1 (Fraudulent) Actual Class 1 (Fraudulent) 0 (Non-fraudulent) Error rate = 0 (Non-fraudulent) 30 58 32 920 n0,1 + n1,0 32 + 58 = = 0.0865 = 8.65% n 1040 Our classification confusion matrix becomes Classification Confusion Matrix Predicted Class 1 (Fraudulent) ...
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...Memorandum To: CC: From: Date: Re: The Cincinnati Enquirer Kristen DelGuzzi Ashley N. Ear; September 16, 2007 Data Analysis of Hamilton County Judges Probabilities used to assist with Ranking of Hamilton County Judges After the current statistics were gathered to produce data analysis regarding Hamilton County Judges, we can come to a conclusion and rank judges appropriately by their probability to be appealed, reversed and a combination of the both. With the provided data analysis, I have included statistics to all probabilities including: total cases disposed, appealed cases, reversed cases, probability of appeal, rank by probability of appeal, probability of reversal, rank by probability of reversal, conditional probability of reversal given appeal, rank by conditional probability of reversal given appeal and overall sum of ranks. The judges that rank the highest (i.e. 1st, 2nd, 3rd) have the lowest probability to have appealed cases, reversed cases and lowest conditional probability of reversed cases given appeal. In my opinion, by ranking the judges as such, we can see how often their ruling is upheld, which is ultimately desirable when concerning the credibility of a judge. I have provided rankings for all three different courts including: Common Pleas Court, Domestic Relations Court and Municipal Court. These overall rankings are gathered by summing up all of the rankings by the three probability variables. I have also provided data analysis which interprets who...
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