Vodafone Marketing Strategy free essay sample

Vodafone UK gives NECTAR reward points for every ? 1 spent on calls, text messages, picture messages and ring tones. Promotion: Advertising on TV, on billboards, in magazines and in other media outlets reaches large audiences and spreads the brand image and the message very effectively. This is known as above the line promotion. Stores have special offers, promotions and point of sale posters to attract those inside the stores to buy. Vodafone’s stores, its products and its staff all project the brand image. Vodafone Products: Brand Image: David Beckham is more than a footballer. He is also regarded as a fashion icon, a caring family man and a nice guy: an overall image that attracted Vodafone to him. Beckham’s popularity with football fans comes largely from his England team captaincy. As a footballer, he is well regarded around the world. Other young men who might aspire to his success and style also tend to identify with him. We will write a custom essay sample on Vodafone Marketing Strategy or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page He appeals to many females because of his reputation as a fashion and lifestyle icon. He is also married to a female icon in her own right. Vodafone’s sponsorship of the Manchester United team appeals to a broad section of the global football/sports audience, whereas aspects of Beckham’s broader image have grown to appeal to a much wider section of society. That suits Vodafone, who needs to appeal to different segments of the market. Beckham’s healthy lifestyle allied to his talent suggests an energy and a controlled passion for life; an image that Vodafone would also like to project for itself. On a football field, Beckham is innovative, creative, exciting; characteristics that Vodafone aspires to. Beckham the family man comes across as caring and empathetic; Vodafone wants people to appreciate that it too understands and cares about what people want and need. Beckham is generally seen as dependable; Vodafone wants to communicate a similar image. The synergy is clear. The campaign Beckham is supporting the campaign to promote Vodafone live! in the UK and in other markets. The UK campaign shows Beckham doing everyday things: a happy, relaxed, competent shopper sending pictures and accepting a message to remember to buy eggs. At the same time he is also clearly demonstrating what Vodafone live! can do. The TV campaign has been a huge success. Many people have seen it and can recall the adverts. The campaign captured the imagination of the press, and many newspapers covered stories about Beckham’s sponsorship deal. Slogans such as ‘Send it like Beckham’ help to further promote the Vodafone message. Beckham’s image is also used on a variety of other customer communications including in-store posters, billboards, in the company’s magazines and catalogues and in leaflets mailed to customers. Market Research High profile campaigns are a gamble. The campaign’s impact has to justify the time, money and effort spent on it. The marketing team must evaluate the campaign’s success. Vodafone UK has asked people across different sectors of society about the campaign, and has analysed their responses. Individuals were asked what they could remember about the campaigns. This is known in the marketing industry as recall. Recall % Another exercise assessed the effectiveness of the poster depicting Beckham being reminded to buy some eggs. People in the survey are shown different Vodafone posters and asked to say which of them they recall in relation to Vodafone live! Clearly, the Beckham poster is far and away the one that is best recalled. Other data has been used to assess the success of the Beckham promotion. Findings from UK Brand Tracking data reveal that the TV campaign has increased awareness of Vodafone with above average efficiency as measured by the Awareness Index, primarily because of the Beckham scenes. People are able to recall and describe the advertisements without prompting. The Beckham campaign has also helped to support Vodafone’s drive for brand migration. Vodafone can help to fulfil its aim to grow successfully by acquiring local companies in markets that Vodafone would like to enter. A good example of this is Vodafone’s purchase of J-Phone in Japan. The initial strategy was to use a dual J-Phone Vodafone logo alongside the powerful image of Beckham to emphasise the relationship between the two companies. The final transition removed the J-Phone logo altogether to a sole focus on Vodafone (Vodafone KK). This strategy warmed J-Phone’s customers to the idea of a global brand replacing a local brand. David Beckham is a popular figure in Japan and helped to smooth the way for the substitution of the global brand in place of the local one. Vodafone India: Conclusion In a highly competitive market, David Beckham is the latest in a number of high profile celebrities and sports personalities that Vodafone has used to promote Vodafone live! Market research and increased sales indicate that using Beckham’s image has been highly effective. Sponsorship using stars involves a partnership between the star and the company, and success depends on both remaining high profile and in the public eye The Beckham campaign is seen in many countries worldwide and reinforces his own image as well as communicating Vodafone’s brand values. Beckham is something of a phenomenon whose star status shows no sign of waning. Vodafone believes that it has gained an important advantage in a highly competitive market place as a result of having such a high profile, admired star attached to its name and its product.

Norms and Values Essay Essays

Norms and Values Essay Essays Norms and Values Essay Essay Norms and Values Essay Essay Beginning of Life Culture is an acquired style of living. At birth children do not have any knowledge of their cultures nor do they have any influence on which culture they can emulate. The process through which a person acquires certain values and norms is called socialization. At the beginning of ones life, the major influences on the behaviors, conceptions, and values of infants are their immediate social circles. Family and friends often make for the primary influences of individuals. Another important influence is the cultural context in which socialization occurs. The norms and values that are in India may not be the same as how people are socialized in Canada. Cultural diversity often influences the socialization (Kallivayalil 535-559). Looking at the differences in the socialization process in India and Canada can give important insight into the norms and values of both societies. Socialization pushes people towards an ideology or philosophy that governs social and cultural life (Kallivayalil 535-559). In India, there are many different ethnicities. This phenomenon, therefore, complicates the socialization process of the country. For instance, Yanomamo Indian boys are brought up to be aggressive and thought. They are taught to embrace violence and overwhelming emotions. For the most part, socialization is often culturally motivated in India, compared to European and Western Cultures. The family unit is an important social and political unit. In India, a lot of families practice joint living with the extended family (Kallivayalil 535-559). The mother is the nurturer while the father represents the bread winner of the family. In Indian culture, males are permissive, and females are meant to be submissive. The respect for elders also makes important competency for these students. The gender lines in India are given priority in nurturing children. For instance, theyre taught how they ought to behave and even have arranged marriage. In fact, the filing of divorce is not taken well by the society. Western cultures bring up their children in a different method. For one, the value for culture is limited given the diverse mix of people. It is difficult to find parents and peers whose cultural and social belief coincide. For this reason, one of the approaches through which values and norms are imparted to children (Pike and Zureik 1). Education and political socialization, therefore, present as the acceptable and most widely used measures for socialization. It is important to note that these measures only improve the outcome of how youth grow. Through learning about stratification methods and incorporating the ideologies deemed universal in civic education, is the primary approach towards socialization in western cultures (Pike and Zureik 1). Political socialization is a method through which people are socialized in Canada. Through civic education, the value of having and practicing certain moral values and coin conceptualizations is instilled in the lives of young people. However, it is important to note that there are different approaches towards civic education. The intent for Canada, however, is to stir political participation in the people (Claes, Hooghe and Stolle 613). Still, through civic education, small children can grow up having value for social service. Social service inspires more participation in community and therefore inspires more responsibility and character in peoples. It is, however, important to note that in its diversity, social stratification and socialization is one of the priorities of political socialization. The country is ultimately focused on protecting the ethnicities and the values that they hold. They country, therefore, is better positioned regarding liberal and culturally motivated socia lization(Claes, Hooghe and Stolle 613). Work Cited Claes, Ellen, Marc Hooghe, and Dietlind Stolle. The Political Socialization Of Adolescents In Canada: Differential Effects Of Civic Education On Visible Minorities. Canadian Journal of Political Science 42.03 (2009): 613. Web. 20 Mar. 2017. Kallivayalil, D. Gender And Cultural Socialization In Indian Immigrant Families In The United States. Feminism Psychology 14.4 (2004): 535-559. Web. 20 Mar. 2017. Pike, Robert M., and Elia Zureik. Socialization And Values In Canadian Society: Socialization, Social Stratification And Ethnicity On JSTOR. Jstor.org. N.p., 1978. Web. 20 Mar. 2017.

The interaction of individual agency Essay Example | Topics and Well Written Essays - 750 words

The interaction of individual agency - Essay Example The structure of tennis (tournaments) consists of the four Grand Slam tournaments — the Australian Open, the French Open, the US open And the Wimbledon, and the Davis Cup (for men) and the Fed Cup (women). (University of Texas: Introduction to Tennis - website) The Agencies in the game of tennis are the governing bodies, the clubs and educational institutions where tennis is encouraged and played and also advertising sponsors and media, which have a marked influence on the fortunes of the game. The individuals involved in the game are the players, the coaches, the members of administrative bodies that govern the game, its patrons, the persons in decision-making positions in educational institutions, companies that sponsor the game and in the media; and most important of all — the individuals that comprise the audience. How is the role of a tennis coach affected by the interactions among its various agencies, and those between the agencies and the overall structure of th e game? I propose to look at the question mainly from my own experience as a tennis coach for the past fourteen years, and shall also refer to the findings of a research study done for the Scottish Sports Council (Lyle, Allison & Taylor 1997) Individuals are attracted to coaching to prolong an involvement with the game and to help others —mostly younger persons — and to a limited extent, the top performers in the game (those who coach top performers are less in number.) Wanting to put something back into the sport motivates many to become coaches. Although in most sports financial reward is not a motivating factor for taking up coaching, in tennis this reason seems to be of greater importance, with 38% of the respondents stating this.

Final Examination Assignment Example | Topics and Well Written Essays - 1250 words - 1

Final Examination - Assignment Example Employee X and employee Y. It say that employee X are bad employee that are not motivated by the job they are doing and need coercion to work productively. Most managers will not have incentives for employees falling in category X instead they use coercion. On the other side employee Y have natural liking of their job and little motivation can double their productivity. Other theories such as Maslow hierarchy of need indicate the progressive need of motivation. As one motivation is satisfied it means a lot to productivity and reenergize for another achievement. All motivation theories address one outcome in there explanation aspect of motivation. On the same note, every theory has a role and every theory has one way or the other that can be used by a manager to increase the productivity in the company. A good example is the comparative picture that the theory of Maslow and that of McGregor when viewed in unison they draw. On commonality, they all share one fabric: the fabric of motivation. However there approach is different. On one hand the of X and Y which is a school of thought of McGregor explains that there exist two types of employee one who is self-initiated, self-motivated and work productively under supervision and coercion. Such employee is grouped as an employee Y. In real sense such an employee could be following the ladder of hierarchy postulated by Maslow being motivated at every stage. After achieving on physiological need the self-motivation of moving to safety need arises and on and on till the self-actualization is achiev ed. Consequently, the two theory fit together. On the other side, an employee who is fixated on one step of Maslow theory tend to be frustrated or retrogress to the lower hierarchy for the purposes of feeling satisfied. These employees tend to fit in category X according to McGregor. Many other theories have a commonality point of view. Public employee determine

Domestic Violence Essay Example | Topics and Well Written Essays - 500 words - 4

Domestic Violence - Essay Example From a personal perspective, the fire seemed to be intentional as the Zephyrhills man refused to rescue his live-in girlfriend even though he had the opportunity to do so (Dutton, 2010). A family constitutes of all the requirements in life, as there is togetherness, love and care for each other. As a family man, Christopher Henry lived with his girlfriend and several children including a 1- year old baby who were at their relatives during the time of the incidence (Dutton, 2010). Wife battery and possession of marijuana made him serve a sentence in jail. Children in most cases learn from the behaviors of their parents and this affects them either positively or negatively (Kinsler, 2014). Henrys’ children may suffer emotional depression after learning the death of their mother. A person dealing with drugs is incapable of looking after his children as their mother could. Even though the children may need at least one of their parents, it is better for Christopher to go back to jail. Christopher Henry, the boyfriend, tells the investigators that he had tried to wake Lorraine up after the smell of smoke but she did not respond. Lorraine was jus close to the entrance lying in bed (Dutton, 2010). The duplex apartment consumed with flames on the arrival of firefighters. The fire was intentional because Christopher had the ability to rescue the girlfriend since she was two feet from the entrance but he instead called her to come out. Lorraine maybe suffered from suffocation and was unable to rescue herself. I disagree with Christopher’s statement of calling his girlfriend to escape from fire (Kinsler, 2014). A neighbor witnessed the whole incident and that Henry did not intend to go back to the duplex to rescue his girlfriend (Johnson, 2010). This made the neighbor beat Henry up and his face filled with bruises. The neighbor said that they had resolved a domestic disturbance

Advantages And Disadvantages Of Smart Antenna

Advantages And Disadvantages Of Smart Antenna The Direction of Arrival (DOA) estimation algorithm which may take various forms generally follows from the homogeneous solution of the wave equation. The models of interest in this dissertation may equally apply to an EM wave as well as to an acoustic wave. Assuming that the propagation model is fundamentally the same, we will, for analytical expediency, show that it can follow from the solution of Maxwells equations, which clearly are only valid for EM waves. In empty space the equation can be written as: =0 (3.1) =0 (3.2) (3.3) (3.4) where . and ÃÆ'-, respectively, denote the divergence and curl. Furthermore, B is the magnetic induction. E denotes the electric field, whereas and are the magnetic and dielectric constants respectively. Invoking 3.1 the following curl property results as: (3.5) (3.6) (3.7) The constant c is generally referred to as the speed of propagation. For EM waves in free space, it follows from the derivation c = 1 / = 3 x m / s. The homogeneous wave equation (3.7) constitutes the physical motivation for our assumed data model, regardless of the type of wave or medium. In some applications, the underlying physics are irrelevant, and it is merely the mathematical structure of the data model that counts. 3.2 Plane wave In the physics of wave propagation, a plane wave is a constant-frequency wave whose wave fronts are infinite parallel planes of constant peak-to-peak amplitude normal to the phase velocity vector[]. Actually, it is impossible to have a rare plane wave in practice, and only a plane wave of infinite extent can propagate as a plane wave. Actually, many waves are approximately regarded as plane waves in a localized region of space, e.g., a localized source such as an antenna produces a field which is approximately a plane wave far enough from the antenna in its far-field region. Likely, we can treat the waves as light rays which correspond locally to plane waves, when the length scales are much longer than the waves wavelength, as is often appearing of light in the field of optics. 3.2.1 Mathematical definition Two functions which meet the criteria of having a constant frequency and constant amplitude are defined as the sine or cosine functions. One of the simplest ways to use such a sinusoid involves defining it along the direction of the x axis. As the equation shown below, it uses the cosine function to express a plane wave travelling in the positive x direction. (3.8) Where A(x,t) is the magnitude of the shown wave at a given point in space and time. is the amplitude of the wave which is the peak magnitude of the oscillation. k is the waves wave number or more specifically the angular wave number and equals 2à Ã¢â€š ¬/ÃŽÂ », where ÃŽÂ » is the wavelength of the wave. k has the units of radians per unit distance and is a standard of how rapidly the disturbance changes over a given distance at a particular point in time. x is a point along the x axis. y and z are not considered in the equation because the waves magnitude and phase are the same at every point on any given y-z plane. This equation defines what that magnitude and phase are. is the waves angular frequency which equals 2à Ã¢â€š ¬/T, and T is the period of the wave. In detail, omega, has the units of radians per unit time and is also a standard of how rapid the disturbance changing in a given length of time at a particular point in space. is a given particular point in time, and varphi , is the wave phase shift with the units of radians. It must make clear that a positive phase shift will shifts the wave along the negative x axis direction at a given point of time. A phase shift of 2à Ã¢â€š ¬ radians means shifting it one wavelength exactly. Other formulations which directly use the waves wavelength, period T, frequency f and velocity c, are shown as follows: A=A_o cos[2pi(x/lambda- t/T) + varphi], (3.9) A=A_o cos[2pi(x/lambda- ft) + varphi], (3.10) A=A_o cos[(2pi/lambda)(x- ct) + varphi], (3.11) To appreciate the equivalence of the above set of equations denote that f=1/T,! and c=lambda/T=omega/k,! 3.2.2 Application Plane waves are solutions for a scalar wave equation in the homogeneous medium. As for vector wave equations, e.g., waves in an elastic solid or the ones describing electromagnetic radiation, the solution for the homogeneous medium is similar. In vector wave equations, the scalar amplitude is replaced by a constant vector. e.g., in electromagnetism is the vector of the electric field, magnetic field, or vector potential. The transverse wave is a kind of wave in which the amplitude vector is perpendicular to k, which is the case for electromagnetic waves in an isotropic space. On the contrast, the longitudinal wave is a kind of wave in which the amplitude vector is parallel to k, typically, such as for acoustic waves in a gas or fluid. The plane wave equation is true for arbitrary combinations of à Ã¢â‚¬ ° and k. However, all real physical mediums will only allow such waves to propagate for these combinations of à Ã¢â‚¬ ° and k that satisfy the dispersion relation of the mediums. The dispersion relation is often demonstrated as a function, à Ã¢â‚¬ °(k), where ratio à Ã¢â‚¬ °/|k| gives the magnitude of the phase velocity and dà Ã¢â‚¬ °/dk denotes the group velocity. As for electromagnetism in an isotropic case with index of refraction coefficient n, the phase velocity is c/n, which equals the group velocity on condition that the index is frequency independent. In linear uniform case, a wave equation solution can be demonstrated as a superposition of plane waves. This method is known as the Angular Spectrum method. Actually, the solution form of the plane wave is the general consequence of translational symmetry. And in the more general case, for periodic structures with discrete translational symmetry, the solution takes the form of Bloch waves, which is most famous in crystalline atomic materials, in the photonic crystals and other periodic wave equations. 3.3 Propagation Many physical phenomena are either a result of waves propagating through a medium or exhibit a wave like physical manifestation. Though 3.7 is a vector equation, we only consider one of its components, say E(r,t) where r is the radius vector. It will later be assumed that the measured sensor outputs are proportional to E(r,t). Interestingly enough, any field of the form E(r,t) = , which satisfies 3.7, provided with T denoting transposition. Through its dependence on only, the solution can be interpreted as a wave traveling in the direction, with the speed of propagation. For the latter reason, ÃŽÂ ± is referred to as the slowness vector. The chief interest herein is in narrowband forcing functions. The details of generating such a forcing function can be found in the classic book by Jordan [59]. In complex notation [63] and taking the origin as a reference, a narrowband transmitted waveform can be expressed as: (3.12) where s(t) is slowly time varying compared to the carrier . For, where B is the bandwidth of s(t), we can write: (3.13) In the last equation 3.13, the so-called wave vector was introduced, and its magnitude is the wavenumber. One can also write, where is the wavelength. Make sure that k also points in the direction of propagation, e.g., in the x-y plane we can get: (3.14) where is the direction of propagation, defined counter clockwise relative the x axis. It should be noted that 3.12 implicitly assumed far-field conditions, since an isotropic, which refers to uniform propagation/transmission in all directions, point source gives rise to a spherical traveling wave whose amplitude is inversely proportional to the distance to the source. All points lying on the surface of a sphere of radius R will then share a common phase and are referred to as a wave front. This indicates that the distance between the emitters and the receiving antenna array determines whether the spherical degree of the wave should be taken into account. The reader is referred to e.g., [10, 24] for treatments of near field reception. Far field receiving conditions imply that the radius of propagation is so large that a flat plane of constant phase can be considered, thus resulting in a plane wave as indicated in Eq. 8. Though not necessary, the latter will be our assumed working mode l for convenience of exposition. Note that a linear medium implies the validity of the superposition principle, and thus allows for more than one traveling wave. Equation 8 carries both spatial and temporal information and represents an adequate model for distinguishing signals with distinct spatial-temporal parameters. These may come in various forms, such as DOA, in general azimuth and elevation, signal polarization, transmitted waveforms, temporal frequency etc. Each emitter is generally associated with a set of such characteristics. The interest in unfolding the signal parameters forms the essence of sensor array signal processing as presented herein, and continues to be an important and active topic of research. 3.4 Smart antenna Smart antennas are devices which adapt their radiation pattern to achieve improved performance either range or capacity or some combination of these [1]. The rapid growth in demand for mobile communications services has encouraged research into the design of wireless systems to improve spectrum efficiency, and increase link quality [7]. Using existing methods more effective, the smart antenna technology has the potential to significantly increase the wireless. With intelligent control of signal transmission and reception, capacity and coverage of the mobile wireless network, communications applications can be significantly improved [2]. In the communication system, the ability to distinguish different users is essential. The smart antenna can be used to add increased spatial diversity, which is referred to as Space Division Multiple Access (SDMA). Conventionally, employment of the most common multiple access scheme is a frequency division multiple access (FDMA), Time Division Multiple Access (TDMA), and Code Division Multiple Access (CDMA). These independent users of the program, frequency, time and code domain were given three different levels of diversity. Potential benefits of the smart antenna show in many ways, such as anti-multipath fading, reducing the delay extended to support smart antenna holding high data rate, interference suppression, reducing the distance effect, reducing the outage probability, to improve the BER (Bit Error Rate)performance, increasing system capacity, to improve spectral efficiency, supporting flexible and efficient handoff to expand cell coverage, flexible management of the district, to extend the battery life of mobile station, as well as lower maintenance and operating costs. 3.4.1 Types of Smart Antennas The environment and the systems requirements decide the type of Smart Antennas. There are two main types of Smart Antennas. They are as follows: Phased Array Antenna In this type of smart antenna, there will be a number of fixed beams between which the beam will be turned on or steered to the target signal. This can be done, only in the first stage of adjustment to help. In other words, as wanted by the moving target, the beam will be the Steering [2]. Adaptive Array Antenna Integrated with adaptive digital signal processing technology, the smart antenna uses digital signal processing algorithm to measure the signal strength of the beam, so that the antenna can dynamically change the beam which transmit power concentrated, as figure 3.2 shows. The application of spatial processing can enhance the signal capacity, so that multiple users share a channel. Adaptive antenna array is a closed-loop feedback control system consisting of an antenna array and real-time adaptive signal receiver processor, which uses the feedback control method for automatic alignment of the antenna array pattern. It formed nulling interference signal offset in the direction of the interference, and can strengthen a useful signal, so as to achieve the purpose of anti-jamming [3]. Figure 2 click for text version Figure 3.2 3.4.2 Advantages and disadvantages of smart antenna Advantages First of all, a high level of efficiency and power are provided by the smart antenna for the target signal. Smart antennas generate narrow pencil beams, when a big number of antenna elements are used in a high frequency condition. Thus, in the direction of the target signal, the efficiency is significantly high. With the help of adaptive array antennas, the same amount times the power gain will be produce, on condition that a fixed number of antenna elements are used. Another improvement is in the amount of interference which is suppressed. Phased array antennas suppress the interference with the narrow beam and adaptive array antennas suppress by adjusting the beam pattern [2]. Disadvantages The main disadvantage is the cost. Actually, the cost of such devices will be more than before, not only in the electronics section, but in the energy. That is to say the device is too expensive, and will also decrease the life of other devices. The receiver chains which are used must be decreased in order to reduce the cost. Also, because of the use of the RF electronics and A/D converter for each antenna, the costs are increasing. Moreover, the size of the antenna is another problem. Large base stations are needed to make this method to be efficient and it will increase the size, apart from this multiple external antennas needed on each terminal. Then, when the diversity is concerned, disadvantages are occurred. When mitigation is needed, diversity becomes a serious problem. The terminals and base stations must equip with multiple antennas. 3.5 White noise White noise is a random signal with a flat power spectral density []. In another word, the signal contains the equal power within a particular bandwidth at the centre frequency. White noise draws its name from white light where the power spectral density of the light is distributed in the visible band. In this way, the eyes three colour receptors are approximately equally stimulated []. In statistical case, a time series can be characterized as having weak white noise on condition that {} is a sequence of serially uncorrelated random vibrations with zero mean and finite variance. Especially, strong white noise has the quality to be independent and identically distributed, which means no autocorrelation. In particular, the series is called the Gaussian white noise [1], if is normally distributed and it has zero mean and standard deviation. Actually, an infinite bandwidth white noise signal is just a theoretical construction which cannot be reached. In practice, the bandwidth of white noise is restricted by the transmission medium, the mechanism of noise generation, and finite observation capabilities. If a random signal is observed with a flat spectrum in a mediums widest possible bandwidth, we will refer it as white noise. 3.5.1 Mathematical definition White random vector A random vector W is a white random vector only if its mean vector and autocorrelation matrix are corresponding to the follows: mu_w = mathbb{E}{ mathbf{w} } = 0 (3.15) R_{ww} = mathbb{E}{ mathbf{w} mathbf{w}^T} = sigma^2 mathbf{I} . (3.16) That is to say, it is a zero mean random vector, and its autocorrelation matrix is a multiple of the identity matrix. When the autocorrelation matrix is a multiple of the identity, we can regard it as spherical correlation. White random process A time continuous random process where is a white noise signal only if its mean function and autocorrelation function satisfy the following equation: mu_w(t) = mathbb{E}{ w(t)} = 0 (3.17) R_{ww}(t_1, t_2) = mathbb{E}{ w(t_1) w(t_2)} = (N_{0}/2)delta(t_1 t_2). (3.18) That is to say, it is zero mean for all time and has infinite power at zero time shift since its autocorrelation function is the Dirac delta function. The above autocorrelation function implies the following power spectral density. Since the Fourier transform of the delta function is equal to 1, we can imply: S_{ww}(omega) = N_{0}/2 ,! (3.19) Since this power spectral density is the same at all frequencies, we define it white as an analogy to the frequency spectrum of white light. A generalization to random elements on infinite dimensional spaces, e.g. random fields, is the white noise measure. 3.5.2 Statistical properties The white noise is uncorrelated in time and does not restrict the values a signal can take. Any distribution of values about the white noise is possible. Even a so-called binary signal that can only take the values of 1 or -1 will be white on condition that the sequence is statistically uncorrelated. Any noise with a continuous distribution, like a normal distribution, can be white noise certainly. It is often incorrectly assumed that Gaussian noise is necessarily white noise, yet neither property implies the other. Gaussianity refers to the probability distribution with respect to the value, in this context the probability of the signal reaching amplitude, while the term white refers to the way the signal power is distributed over time or among frequencies. Spectrogram of pink noise (left) and white noise (right), showed with linear frequency axis (vertical). We can therefore find Gaussian white noise, but also Poisson, Cauchy, etc. white noises. Thus, the two words Gaussian and white are often both specified in mathematical models of systems. Gaussian white noise is a good approximation of many real-world situations and generates mathematically tractable models. These models are used so frequently that the term additive white Gaussian noise has a standard abbreviation: AWGN. White noise is the generalized mean-square derivative of the Wiener process or Brownian motion. 3.6 Normal Distribution In probability theory, the normal (or Gaussian) distribution is a continuous probability distribution that has a bell-shaped probability density function, known as the Gaussian function or informally as the bell curve[1]. f(x;mu,sigma^2) = frac{1}{sigmasqrt{2pi}} e^{ -frac{1}{2}left(frac{x-mu}{sigma}right)^2 } The parameter ÃŽÂ ¼ is the mean or expectation (location of the peak) and à Ã†â€™Ãƒ ¢Ã¢â€š ¬Ã¢â‚¬ °2 is the variance. à Ã†â€™ is known as the standard deviation. The distribution with ÃŽÂ ¼ = 0 and à Ã†â€™Ãƒ ¢Ã¢â€š ¬Ã¢â‚¬ °2 = 1 is called the standard normal distribution or the unit normal distribution. A normal distribution is often used as a first approximation to describe real-valued random variables that cluster around a single mean value. http://upload.wikimedia.org/wikipedia/commons/thumb/8/8c/Standard_deviation_diagram.svg/325px-Standard_deviation_diagram.svg.png The normal distribution is considered the most prominent probability distribution in statistics. There are several reasons for this:[1] First, the normal distribution arises from the central limit theorem, which states that under mild conditions, the mean of a large number of random variables drawn from the same distribution is distributed approximately normally, irrespective of the form of the original distribution. This gives it exceptionally wide application in, for example, sampling. Secondly, the normal distribution is very tractable analytically, that is, a large number of results involving this distribution can be derived in explicit form. For these reasons, the normal distribution is commonly encountered in practice, and is used throughout statistics, natural sciences, and social sciences [2] as a simple model for complex phenomena. For example, the observational error in an experiment is usually assumed to follow a normal distribution, and the propagation of uncertainty is computed using this assumption. Note that a normally distributed variable has a symmetric distribution about its mean. Quantities that grow exponentially, such as prices, incomes or populations, are often skewed to the right, and hence may be better described by other distributions, such as the log-normal distribution or Pareto distribution. In addition, the probability of seeing a normally distributed value that is far (i.e. more than a few standard deviations) from the mean drops off extremely rapidly. As a result, statistical inference using a normal distribution is not robust to the presence of outliers (data that are unexpectedly far from the mean, due to exceptional circumstances, observational error, etc.). When outliers are expected, data may be better described using a heavy-tailed distribution such as the Students t-distribution. 3.6.1 Mathematical Definition The simplest case of a normal distribution is known as the standard normal distribution, described by the probability density function phi(x) = frac{1}{sqrt{2pi}}, e^{- frac{scriptscriptstyle 1}{scriptscriptstyle 2} x^2}. The factor scriptstyle 1/sqrt{2pi} in this expression ensures that the total area under the curve à Ã¢â‚¬ ¢(x) is equal to one[proof], and 12 in the exponent makes the width of the curve (measured as half the distance between the inflection points) also equal to one. It is traditional in statistics to denote this function with the Greek letter à Ã¢â‚¬ ¢ (phi), whereas density functions for all other distributions are usually denoted with letters f or p.[5] The alternative glyph à Ã¢â‚¬   is also used quite often, however within this article à Ã¢â‚¬   is reserved to denote characteristic functions. Every normal distribution is the result of exponentiating a quadratic function (just as an exponential distribution results from exponentiating a linear function): f(x) = e^{a x^2 + b x + c}. , This yields the classic bell curve shape, provided that a 0 everywhere. One can adjust a to control the width of the bell, then adjust b to move the central peak of the bell along the x-axis, and finally one must choose c such that scriptstyleint_{-infty}^infty f(x),dx = 1 (which is only possible when a Rather than using a, b, and c, it is far more common to describe a normal distribution by its mean ÃŽÂ ¼ = à ¢Ã‹â€ Ã¢â‚¬â„¢Ãƒ ¢Ã¢â€š ¬Ã¢â‚¬ °b2a and variance à Ã†â€™2 = à ¢Ã‹â€ Ã¢â‚¬â„¢Ãƒ ¢Ã¢â€š ¬Ã¢â‚¬ °12a. Changing to these new parameters allows one to rewrite the probability density function in a convenient standard form, f(x) = frac{1}{sqrt{2pisigma^2}}, e^{frac{-(x-mu)^2}{2sigma^2}} = frac{1}{sigma}, phi!left(frac{x-mu}{sigma}right). For a standard normal distribution, ÃŽÂ ¼ = 0 and à Ã†â€™2 = 1. The last part of the equation above shows that any other normal distribution can be regarded as a version of the standard normal distribution that has been stretched horizontally by a factor à Ã†â€™ and then translated rightward by a distance ÃŽÂ ¼. Thus, ÃŽÂ ¼ specifies the position of the bell curves central peak, and à Ã†â€™ specifies the width of the bell curve. The parameter ÃŽÂ ¼ is at the same time the mean, the median and the mode of the normal distribution. The parameter à Ã†â€™2 is called the variance; as for any random variable, it describes how concentrated the distribution is around its mean. The square root of à Ã†â€™2 is called the standard deviation and is the width of the density function. The normal distribution is usually denoted by N(ÃŽÂ ¼,à ¢Ã¢â€š ¬Ã¢â‚¬ °Ãƒ Ã†â€™2).[6] Thus when a random variable X is distributed normally with mean ÃŽÂ ¼ and variance à Ã†â€™2, we write X sim mathcal{N}(mu,,sigma^2). , 3.6.2 Alternative formulations Some authors advocate using the precision instead of the variance. The precision is normally defined as the reciprocal of the variance (à Ã¢â‚¬Å¾ = à Ã†â€™Ãƒ ¢Ã‹â€ Ã¢â‚¬â„¢2), although it is occasionally defined as the reciprocal of the standard deviation (à Ã¢â‚¬Å¾ = à Ã†â€™Ãƒ ¢Ã‹â€ Ã¢â‚¬â„¢1).[7] This parameterization has an advantage in numerical applications where à Ã†â€™2 is very close to zero and is more convenient to work with in analysis as à Ã¢â‚¬Å¾ is a natural parameter of the normal distribution. This parameterization is common in Bayesian statistics, as it simplifies the Bayesian analysis of the normal distribution. Another advantage of using this parameterization is in the study of conditional distributions in the multivariate normal case. The form of the normal distribution with the more common definition à Ã¢â‚¬Å¾ = à Ã†â€™Ãƒ ¢Ã‹â€ Ã¢â‚¬â„¢2 is as follows: f(x;,mu,tau) = sqrt{frac{tau}{2pi}}, e^{frac{-tau(x-mu)^2}{2}}. The question of which normal distribution should be called the standard one is also answered differently by various authors. Starting from the works of Gauss the standard normal was considered to be the one with variance à Ã†â€™2 = 12 : f(x) = frac{1}{sqrtpi},e^{-x^2} Stigler (1982) goes even further and insists the standard normal to be with the variance à Ã†â€™2 = 12à Ã¢â€š ¬ : f(x) = e^{-pi x^2} According to the author, this formulation is advantageous because of a much simpler and easier-to-remember formula, the fact that the pdf has unit height at zero, and simple approximate formulas for the quintiles of the distribution. 3.7 Cramer-Rao Bound In estimation theory and statistics, the Cramà ©r-Rao bound (CRB) or Cramà ©r-Rao lower bound (CRLB), named in honor of Harald Cramer and Calyampudi Radhakrishna Rao who were among the first to derive it,[1][2][3] expresses a lower bound on the variance of estimators of a deterministic parameter. The bound is also known as the Cramà ©r-Rao inequality or the information inequality. In its simplest form, the bound states that the variance of any unbiased estimator is at least as high as the inverse of the Fisher information. An unbiased estimator which achieves this lower bound is said to be (fully) efficient. Such a solution achieves the lowest possible mean squared error among all unbiased methods, and is therefore the minimum variance unbiased (MVU) estimator. However, in some cases, no unbiased technique exists which achieves the bound. This may occur even when an MVU estimator exists. The Cramà ©r-Rao bound can also be used to bound the variance of biased estimators of given bias. In some cases, a biased approach can result in both a variance and a mean squared error that are below the unbiased Cramà ©r-Rao lower bound; see estimator bias. statement The Cramà ©r-Rao bound is stated in this section for several increasingly general cases, beginning with the case in which the parameter is a scalar and its estimator is unbiased. All versions of the bound require certain regularity conditions, which hold for most well-behaved distributions. These conditions are listed later in this section. Scalar unbiased case Suppose theta is an unknown deterministic parameter which is to be estimated from measurements x, distributed according to some probability density function f(x;theta). The variance of any unbiased estimator hat{theta} of theta is then bounded by the reciprocal of the Fisher information I(theta): mathrm{var}(hat{theta}) geq frac{1}{I(theta)} where the Fisher information I(theta) is defined by I(theta) = mathrm{E} left[ left( frac{partial ell(x;theta)}{partialtheta} right)^2 right] = -mathrm{E}left[ frac{partial^2 ell(x;theta)}{partialtheta^2} right] and ell(x;theta)=log f(x;theta) is the natural logarithm of the likelihood function and mathrm{E} denotes the expected value. The efficiency of an unbiased estimator hat{theta} measures how close this estimators variance comes to this lower bound; estimator efficiency is defined as e(hat{theta}) = frac{I(theta)^{-1}}{{rm var}(hat{theta})} or the minimum possible variance for an unbiased estimator divided by its actual variance. The Cramà ©r-Rao lower bound thus gives e(hat{theta}) le 1. General scalar case A more general form of the bound can be obtained by considering an unbiased estimator T(X) of a function psi(theta) of the parameter theta. Here, unbiasedness is understood as stating that E{T(X)} = psi(theta). In this case, the bound is given by mathrm{var}(T) geq frac{[psi'(theta)]^2}{I(theta)} where psi'(theta) is the derivative of psi(theta) (by theta), and I(theta) is the Fisher information defined above. Bound on the variance of biased estimators Apart from being a bound on estimators of functions of the parameter, this approach can be used to derive a bound on the variance of biased estimators with a given bias, as follows. Consider an estimator hat{theta} with biasb(theta) = E{hat{theta}} theta, and let psi(theta) = b(theta) + theta. By the result above, any unbiased estimator whose expectation is psi(theta) has variance greater than or equal to (psi'(theta))^2/I(theta). Thus, any estimator hat{theta} whose bias is given by a function b(theta) satisfies mathrm{var} left(hat{theta}right) geq frac{[1+b'(theta)]^2}{I(theta)}. The unbiased version of the bound is a special case of this result, with b(theta)=0. Its trivial to have a small variance à ¢Ã‹â€ Ã¢â‚¬â„¢ an estimator that is constant has a variance of zero. But from the above equation we find that the mean squared errorof a biased estimator is bounded by mathrm{E}left((hat{theta}-theta)^2right)geqfrac{[1+b'(theta)]^2}{I(theta)}+b(theta)^2, using the standard decomposition of the MSE. Note, however, that this bound can be less than the unbiased Cramà ©r-Rao bound 1/I(ÃŽÂ ¸). See the example of estimating variance below. Multivariate case Extending the Cramà ©r-Rao bound to multiple parameters, define a parameter column vector boldsymbol{theta} = left[ theta_1, theta_2, dots, theta_d right]^T in mathbb{R}^d with probability density function f(x; boldsymbol{theta}) which satisfies the two regularity conditions below. The Fisher information matrix is a d times d matrix with element I_{m, k} defined as I_{m, k} = mathrm{E} left[ frac{d}{dtheta_m} log fleft(x; boldsymbol{theta}right) frac{d}{dtheta_k} log fleft(x; boldsymbol{theta}right) right]. Let boldsymbol{T}(X) be an estimator of any vector function of parameters, boldsymbol{T}(X) = (T_1(X), ldots, T_n(X))^T, and denote its expectation vector mathrm{E}[boldsymbol{T}(X)] by boldsymbol{psi}(boldsymbol{theta}). The Cramà ©r-Rao bound then states that the covariance matrix of boldsymbol{T}(X) satisfies mathrm{cov}_{boldsymbol{theta}}left(boldsymbol{T}(X)right) geq frac {partial boldsymbol{psi} left(boldsymbol{theta}right)} {partial boldsymbol{theta}} [Ileft(boldsymbol{theta}right)]^{-1} left( frac {partial boldsymbol{psi}left(boldsymbol{theta}right)} {partial boldsymbol{theta}} right)^T where The matrix inequality A ge B is understood to mean that the matrix A-B is positive semi definite, and partial boldsymbol{psi}(boldsymbol{theta})/partial boldsymbol{theta} is the Jacobian matrix whose ijth element is given by partial psi_i(boldsymbol{theta})/partial theta_j. If boldsymbol{T}(X) is an unbiased estimator of boldsymbol{theta} (i.e., boldsymbol{psi}left(boldsymbol{theta}rig

Tom Daschle :: essays research papers

  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  TOM DASCHLE   Ã‚  Ã‚  Ã‚  Ã‚  Tom Daschle was born into a working class family on December 9, 1947 in Aberdeen, South Dakota. Tom was the eldest of four brothers and became the first to graduate from college in 1969 with a political science degree form South Dakota State University. After graduating college, Tom joined the United States Air Force Strategic Air Command. Tom served three years as an intelligence officer. After the Air Force, Tom became an aide to then South Dakota Senator James Abourezk.   Ã‚  Ã‚  Ã‚  Ã‚  In 1978 Tom was elected to the U.S. House of Representatives and served four terms and soon became part of the Democratic Leadership. In 1979 he was elected Rocky Mountain Regional Whip and from 1982-1986, Tom remained Whip-at-large. Tom quickly gained a reputation for humility and a willingness to compromise. He also became known to his critics as a wolfish partisan, whose strong opinions were only partially disguised by a lamb’s demeanor.   Ã‚  Ã‚  Ã‚  Ã‚  In 1986, Tom ran for the U.S. Senate where he won a tough contest against the incumbent James Abnor. After being elected, Tom received the honor of being appointed to the Senate Finance Committee. Tom became the first South Dakota Senator to be appointed a leadership position when in 1988, Senate Democratic Leader George Mitchell selected Tom the first ever co-chair of the Democratic Policy Committee. Tom Daschle was the first U.S. Senator to hire a full-time economic development director and he also made it easier for South Dakotans to reach him by establishing a toll-free telephone line to his office.   Ã‚  Ã‚  Ã‚  Ã‚  South Dakotans re-elected Tom to the Senate in 1992 and 1998. In addition to his leadership duties, Tom also serves as a member of the Senate Agriculture Committee, Veterans Committee, Indian Affairs Committee, Finance Committee, and the Ethic Committee. Senator Daschle has insisted on fiscal responsibility and discipline from both Congress and the White House. Urging his policy of fiscal responsibility, Senator Daschle has advised Congress to use the unprecedented budget surpluses to pay down the national debt, lock up Social Security and Medicare for future generations, cut taxes for working families and invest in other important priorities such as agriculture, education, crime fighting and healthcare. Tom has pushed for fair competition for family farmers and ranchers and worked to make certain that quality education and healthcare are made available to rural communities. Senator Daschle constantly fights for Veterans to get them the benefits they deserve for their dedication to our country.

Responsibility Essay Essay

Personal responsibility is holding myself accountable for my actions and accepting the outcome of those actions. Any decisions I make, no matter how significant they are, will affect my life and those around me. To me, accepting personal responsibility is the first step in taking control of my life. Someone who demonstrates personal responsibilities achieves academic success. My college success depends on my personal responsibility. This relationship exists because being responsible for my actions will directly affect my education. As a student, I need to have self-discipline or self-control. The ability to be in control of my emotions and actions will show great results in my academic success. The lack of self-control could lead to showing up late in class, turning in the assignment late, or being absent from class. This could also lead me to such as dropping out of school and not being able to accomplish my goals. As an adult, I have many responsibilities and decisions to make. Som e of these responsibilities are taking care of the household, running my own business, staying healthy and getting a college degree. When paying my mortgage, insurance, and utility bills, or paying taxes for my company, I am taking action and accepting responsibility to pay those bills on time. Also, managing my own business takes much commitment, dedication and self-discipline. I also need to keep myself healthy by doing regular exercise and eating healthy food. Keeping my health top notch will help me do my daily routines. To stay on top of my responsibilities, developing a plan, writing down my goals, and setting a date keep me on track. Plus, keeping everything organized helps me focus on achieving my other goals. The other goal I want to achieve is to finish my education and earn a college degree. As an adult student, I understand accepting personal responsibility is the key to success. Also, having a better understanding of personal responsibility will guide me through to my academic success. To become a successful student, I need to create a study plan, practice time management and set realistic goals. Crea ting a study plan is a good way to complete my goals in school. Time management is also critical; setting a schedule to study will keep me on the right course. Setting realistic goals for myself that are attainable can motivate me to do better in school. Planning ahead of time will help me through my academic success.

Guggenheim Bilbao essays

Guggenheim Bilbao essays Phillip Johnson, the dean of American architects called Guggenheim Museum Bilbao the greatest building of our century. Designed by Frank O. Gehry, this sprawling, organic plan resembles a living organism, like some gigantic metallic flower growing along the bank of a river. This unique Museum built on a 32,500 square meter site in the center of Bilbao represents an amazing construction feat. On one side it runs down to the waterside of the Nervin River, 16 meters below the level of the rest of the city of Bilbao. One end is pierced through by the huge Puente de La Salve, one of the main access routes into the city. It is a truly divine architectural achievement of the century. Concepts of architecture for arts sake, todays museum buildings are not only storage for art pieces, but the building itself is an element of art. The choice of Bilbao as the venue for one of the Guggenheim European centers is best understood in the context of the initiatives implemented by the Basque authorities as a contribution to the process of revitalizing the Basque Country's recession-plagued economic structure. These initiatives were also seen as a means of increasing the chances of the city's metropolitan area becoming the major reference point for European regions on the Atlantic seaboard The Guggenheim Museum Bilbao is one of the most important ingredients in the plan to redevelop the city of Bilbao. The plan, involving a number of major projects conceived by some of the world's most prestigious architects, includes the work now in progress to increase operational capacity at the city's port, the restoring of the city's airport, a mission entrusted to Spanish architect Santiago Calatrava, a new Conference and Performing Arts Center, designed by Federico Soriano, the construction of a metropolitan railway - much of it underground - designed by Sir Norman Foster, and a new footbridge crossing the river a...

The 1800s and the Native American Plains Indians essays

The 1800s and the Native American Plains Indians essays In the latter half of the 19th century, the United States government began to take actions that would ultimately limit the presence and culture of Native Americans in the Great Plains region. These government actions were often corrupt in how they prompted mistreatment of the Plains Indians while serving as advantageous for Americans. New inhabitants of the Plains region viewed the land as a resource for production and thus adopted a selfish approach in which the landscape would be used for commercial purposes only. Consequently, the Plains Indians were often abused and taken advantage of. Simultaneously, at a time when agricultural development was evolving, technological developments helped drive the Native Americans back. New advancements in technology, such as the Transcontinental Railroad, promoted settlement in the Great Plains region. Thus, the encroachment of Indian land became habitual for newcomers. In one way or another, both technology and government actions led to the ultimate downfall of Native American culture and society in the Great Plains. Likewise, the lives of the Great Plains Indians would forever be transformed. The Transcontinental Railroad, completed in 1869, wielded tremendous economic and political power throughout the West. Moreover, the rapid settlement of the West could not have taken place without the railroad. More than 2 million Europeans, many recruited by professional promoters, settled the Great Plains between 1870 and 1900. Along with providing transportation links between the East and the West and potential markets as distant as China, the Western railroads directly encouraged settlement. While this would make trade and communication more efficient, it was bad news for the Native Americans. The Plain Indians knew that the institution of the railroad would bring white settlers to the Plains and would result in the encroachment of their land. The railroad essentially changed the land. Its construct...

Innovation Report Essay Example | Topics and Well Written Essays - 2500 words

Innovation Report - Essay Example Perhaps the most widely accepted definition is provided by Michael Vance, according to him â€Å"Innovation is the creation of the new or the re-arranging of the old in a new way†. Most of the products, services or processes that are considered to be innovative are often found to be either ideas or thoughts of others or rework of already existing products, services or processes (Sarkar, 2007). According to Peter Drucker innovation is perceptual as well as conceptual in nature. Therefore innovation necessitates an attitude to go out and look, ask and listen. Successful innovators are found to be using both left and right sides of their brains. In order to make the innovation process effective, the focus must be simple but intense. Most of the effective innovations are found to be exceptionally simple. (Drucker, 2003). This paper is on iPhone, an innovative product, which has taken the generation by its stride. It presents a comparative study of the major drivers of innovation and their role in the innovation process. It also scrutinizes those factors that hinder the innovation process. A study of various theories has been applied to find out the possible challenges faced by the company while developing this innovative product. The paper also includes a critical evaluation of the role of knowledge, design and creativity in the process of making an innovative product. In today’s competitive business environment, innovation is the key to success for any organization. Over the past century most of the business organizations had focused only on continual improvement of the products and services in order to gain competitive advantage. However According to Jim Clifton, CEO and chairman of Gallup Management, such approach is not enough in the current state of economy. According to him today’s organizations must come up with completely new ideas rather than marginally better ones. This was exactly what Apple did over the past few years. The company kept on innovating

Risk in the Theme Park Industry Essay Example | Topics and Well Written Essays - 750 words

Risk in the Theme Park Industry - Essay Example According to Lukas, accidents and deaths are the most closely monitored risk despite the statistical fact that economic concerns and poor weather affect business more frequently. (2005) However, when accidents or deaths do occur, there are several direct consequences. First, the park is often closed during the process of an investigation. In addition to the lost ticket sales during that timeframe, the public perception of danger is increased. "The mere perception of an unsafe ride can affect park attendance, while accidents and deaths can result in park closure and decline in ticket sales." (Lukas, 2005) Statistically, the risk is minimal. In 2003, there were only 78 ride-related injuries which resulted in an overnight hospital stay. (Banay, 2005) Nevertheless, the costs are high when accidents do occur. Insurance underwriters are very concerned with safety, and the cost of insurance is dictated by safety records. While patrons of amusement parks are seeking danger and thrills, park management must balance these desires with the risk of increasing insurance rates. Still, the cost of bad publicity is the highest cost resulting from this statistically insignificant risk to the industry. Terrorism, despite occurring far less frequently than accidents, is considered to be a greater risk to the industry. While the publicity surrounding accidents generally affects one park, or perhaps one family of parks, the effect of terrorist activity is industry-wide. "After an incident of civil unrest, natural disaster, or terrorism, there is an immediate 30% downward spike in the perception of safety at such public locations." (Banay, 2005) She reports that the resultant decline in theme park attendance continues as much as three years after a major terrorist episode. According to sources cited by Debora Vrana in her article for the LA Times, visits to theme parks worldwide were down by 1.5% in 2003. (2004) This decline was attributed to terrorism in addition to poor weather and a poor economy. Under current terrorism threat conditions, the cost to the industry is potentially high, and the US government currently classifies the risk of another strike as moderate to high. Unfortun ately, due to the large crowds that gather at these establishments, they are considered to be a potential target. The specific consequence of that risk appears to be a long-standing decline in park attendance stemming from a decrease in the public perception of safety.A panel of industry insiders gathered in 2002 to discuss the impact of 9/11 on theme park attendance and revenues. Of note, their conclusions did not identify terrorism as the greatest risk. Rather, they concluded, "at this point, it is the soft economy that is really putting a crimp on vacation plans." (Levine, 2002) Like other businesses within the travel and tourism industry, theme parks rely on patrons to spend their discretionary income. When the economy is poor, fewer people are spending, and those who are spending frequently spend less. "Road blocks in the form of war in Iraq, SARS, a stagnant economy, and currency rates have impacted the amusement industry." (Banay, 2005) Currency rates are known to affect the rates of international travel, and those parks which attract an international audience are subject to those effects as well. The risk comes in