Human Resource Management Essay Example | Topics and Well Written Essays - 4000 words - 3

Human Resource Management - Essay Example it is responsible for the development of people, the most vital element in identifying a strategic plan for future use and improving the operation of any organization. From my experience and reading, an HR specialist is only as good as his knowledge of people. For this reason I have seen many HR managers in some large companies who have attained at least a master’s degree. This level of qualification is important to HRM. I remember when the employment of the company was handled by the owner of company himself. The performance of organization at that time was very poor. Later on, a manager who specializes in Human Resource Management came along and the performance of all the departments improved. To sum up, all organizational departments depend on HRM to some extent and HRM skills becomes a main support of any organization. Collaborative work between HR and line managers is necessary but it is better to give each manager a specific function. Overlapping of function and powers may cause serious internal threats to an organization. Clear delineation of roles would lead to fewer objections that could work for the benefit of both workers and staff. Some of the external factors that affect Human Resource Management include economic factors surrounding the organization as well as the economic situation in the country. However, more important than economics is the education of the workforce because having qualified personnel assumes that each hired person has achieved education which qualifies him or her in that position. On the other hand, internal factors include the financial situation of the company which dictates to some extent its hiring limits compared to the marginal productivity of a person. Logistical support from senior management of the company also plays a key role as they must have smooth interpersonal skills with both clients and employees. Lastly, personal factors, which includes the satisfaction of the employee with his or her work along with the

African American in 19th Century Essay Example for Free

African American in 19th Century Essay The Civil war after effects; set the scene for what would become a long road of discovery, hardship, violence, and freedom however, during this process of transition the American people went through emotional as well economical changes which added additional stress to an already stressed nation where many groups became fearful and were subjected to racism which crossed over the boundary of liberty and Justice for all. Equality had become an endangered liberty guaranteed by a country build upon democracy, regrettably the African American people were not the only ones to suffer many vast groups faced hard days in America at the turn of the century several violent attacks were specifically carried out on the African American men and women even though, the civil war brought a lot of changes it produced little or no results for African American men; however, it did bring harsher persecution all over the country, whereas mass numbers of black men were lynched in the lower southern states in a show of defiance. The Emancipation Act did nothing for the white man but still provided less for the African black man who were still unable to vote in addition to having descent jobs with adequate pay many were forced back to the farm as sharecroppers despite the set backs they percervered through the racial remarks and slanders. Black men and women were segragated from the start and separate waiting rooms bathrooms and dinning facilities openly poject the sentiments of the American people of the era,within the State of Mississippi; In Plessy vs. Ferguson (1896), The Supreme Court reinforced that Blacks and Whites should be separate, but equal. The statement SEPERATE but EQUAL! thosewords only produced Segregation on a bias legal system of fairness and equality in which a country struggling already became the fuel on a fire already burning and would later divided the country in later years sparking new violence and refocused hatred. More over the Men and women of that time were forced to swear on separate bibles, they couldnt vote in the election in the country in which they were guaranteed equal rights because they were under disfranchisement, and the racism was developing more and more is some southern locations, for an example many southern states legislated that if your grandfather had cast a ballot then you are allowed to vote and this law supported that nearly all southern white mean were permitted to vote and excluded all African Americans in most situations men whose grandparents had most likely been slaves never voted. Booker T. Washingtons submitted a lot for the African Americans in turn of the century, after his famous speech in Atlanta 1895 (Atlanta Compromise) in about one year the African Americans got more rights, they began to use separated but equal facilities, it was stupid to say the least but it provided a line of truths temporarily and unfourantely included racist ideas inside but it was better than it had been before. Booker T. Washingtons met the American president Theodore Roosevelt at the white house in 1901 and that was a good step towards get the African American and their rights another great pioneer of that time was Du Boise who supported the right for equality and the strive to have equal opportunities within society however Booker T. Washingtons did a lot more for the African American rights, Washington became the Founder of the Niagara movement in 1905. In 1909 the Niagara movement efforts led to foundation of the National Association for the Advancement of Colored People (NAACP) which now is the enforcing representation of the African American whereas Booker T. Washingtons inspiration became a door way to freedom and allowed the African American man to have a voice in society. Finally, if I was African American living at that time, I will say that Booker T. Washingtons and Du Bois were the best representatives of the African Americans all over the country, and Booker T. Washingtons started the movement of the African Americans civil rights, while Du Bois came later to continue and support his efforts, they were great team and deserve the respect.

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

Walmart :: essays research papers

Overview When Sam Walton founded the first Wal-Mart in 1962, the idea of bringing in a discount-shopping store into rural America was almost unheard of, except for the local five and dime stores. When Walton noticed that he had a lot of competition from regional discount chains, him and his wife Helen traveled the country to study other new retailing concepts, and were convinced that it was the wave of the future. With Walton's vision, Wal-Mart grew to be a multi-billion dollar, international company, operating about 4,600 stores around the world. Wal-Mart competes in many industries that include: 5331- Retail-Variety stores, 5411-Grocery stores, 5311-Department stores, 5812-Eating Places, 5399-Miscellaneous General Merchandise store, and 5912-Drug stores and Proprietary stores. Since there are several industries to choose from, our group chose to go with retail-variety stores, SIC code 5331. These establishments are primarily engaged in the retail sale of a variety of merchandise in the low and popular price ranges. Sales are made on a cash and carry basis, with the open-selling method of display and customer selection of merchandise. Wal-Mart's milestones began in 1962 when the first Wal-Mart was opened in Rogers, Arkansas. Seven years later the company incorporated as Wal-Mart Stores, Inc. Then a year later they opened the first distribution center and home office in Bentonville, Arkansas, and also went public on the New York Stock Exchange. Several years later, in 1988, the first super center was opened. Then in 1991, the first international unit was opened in Mexico City. By the turn of the century, Discount Store News had named Wal-Mart "Retailer of the Century" and made Fortune magazine's lists of the "Most Admired Companies in America" and the "100 Best Companies to Work For." They were also ranked on Financial Times' "Most Respected in the World" list. In 2002, Wal-Mart became number one on the Fortune 500 list and was presented with the Ron Brown Award for Corporate Leadership, a presidential award that recognizes companies for outstanding achievement in employee and community relations. Mission Statement Wal-Mart Stores, Inc. does not have a formal mission statement. This is because Kim Ellis, the Public Relations Coordinator, said that they believe the customers are more interested in other aspects of the business, and they, the company, are focused on meeting their basic consumer needs. Since Wal-Mart does not have a mission statement our group has created a mission statement that they might use. It discusses all nine components of a mission statement.

Bilingual Education For Foreign Students

Bilingual Education in the northeastern United States and Canada serves many advantages and benefits for students of limited English skills. Since the early sixty†s, it continues to serve a great advantage to foreign students. This is important because it gives these students the opportunity to achieve the â€Å"American Dream†. American educators have argued that the aim of education should be to assimilate a foreign student into the American mainstream, become good American citizens, and not keep their ethnic identity. The proponents of bilingual education believe that this form of instruction belittles a child†s ethnic and cultural heritage, creates low self-esteem, and fosters a high dropout rate. Therefore, certain bilingual education approaches encourages students† to maintain their language, ethnic and cultural identity, while at the same time learning a new language and culture altogether. Bilingual Education provides instruction for students in two languages. The primary goal of bilingual education in the United States is to teach English to students who don†t speak English or have limited English proficiency (LEP). Although Florida and California have decided to do away with this educational approach, the controversy regarding bilingual education will never end. In this paper different bilingual education approaches will be defined and the history of Bilingual Education will be touched upon. The Proposition 227 controversy and several views towards bilingual education will be discussed. The final conclusion will show my feelings toward bilingual education. There are many approaches that are used to instruct LEP students. Transitional bilingual education (TBE), which is currently incorporated in the NJ school systems, is geared to move LEP students into monolingual classes within two or three years. Programs such as this instruct students† in their native language in the same academic level as their monolingual peers. After appropriate English proficiency level is achieved the LEP students† are able to transfer skills to function successfully in a monolingual class. At the same time students† are also enrolled in classes that teach them English as a second language (ESL) (CQ Researcher, 1996). Bilingual proponents who prefer the developmental bilingual education (DBE) believe that the transitional approach defeats the whole purpose of bilingual education, because it doesn†t maintain a student†s native language. The critics prefer DBE because it is designed to teach both the students† native language and English. Using this approach the student is able to enhance his or her skills in their native language and also be able to learn English up until sixth grade. â€Å"The idea, they say, is to teach additive bilingualism, which makes students fluent in two languages while making them more nimble learners† (CQ Researchers, 1996). According to the article, â€Å"Teaching English to Non-English Speakers offers a Wide Range of Techniques† in the CQ Researchers (1996), the most sophisticated developmental approach is called two-way bilingual education. This approach mixes non-English speaking students with roughly an equal number of English speakers in the same classroom. Students are taught in one language in the morning and the other language in the afternoon. This approach gears to make both sets of students bilingual at levels of fluency that allow each to advance in language as well as in other subjects. Such programs are rare, but where they exist, and where they have well trained bilingual teachers (teachers fluent in both languages and who use interactive or group-learning techniques), most researchers and observers say that students perform better at every energy level of learning than their peers, no matter what kind of instruction the non-two-way students receive (CQ Researchers, 1996). Immersion education is another kind of teaching approach to bilingual education. In these classes, known as special alternative instructional programs, the most popular being structured immersion – students learn their second language from instructors who teach them subject matter presented in the new language. While immersion is based on instruction in the student†s second language, it is not what they call â€Å"sink-or-swim†. â€Å"Sink-or-swim† is when the teacher offers no extra help in learning the new language. The United States Supreme Court, in Lau v. Nichols(1974), declared that not offering extra help was a violation of federal civil rights law (CQ Researchers, 1996). The St.Lambert French immersion program was inaugurated in 1965 in Canada. It was designed to provide proficiency in both aspects of the French language, to promote English proficiency, to ensure an appropriate developmental level of achievement in academic subjects, and to have the students understand and appreciate the French Canadian without taking away from the students† identity for the English Canadian culture. These goals were shared by most of the immersion programs in Canada (cited in Paulston, 1988). The final approach is called alternate immersion, also known as sheltered English or sheltered subject-matter instruction. In sheltered classes children learn their second language first by studying subjects. My aunt, Odainy Tansey, who teaches in a bilingual school in Passaic New Jersey, says that the school board is attempting to introduce sheltered English into the classroom. She stated that sheltered English is not going to be a good approach in introducing a new language to foreign students. The language learning situation contains the necessary ingredients for second-language learning. There are three major components: (1) learners that realize that they need that target language (TL) and are motivated to make that move to learn English; (2) instructors who know the target language well enough to provide the learning tools needed to be able to learn English; and (3) a learning environment that allows both the students and the instructors together to be able to put the learning process to work. All three components are crucial in the learning process of a language. Although there are many different approaches, these three major steps are important. The three learning processes can be described as (1) social, (2) linguistic, and (3) cognitive (cited by Bialystok, 1991, 52). There are many questions concerning bilingual studies. For those whose families speak only Spanish, â€Å"it provides an inconsistent and not terribly successful process of remediation† (Kozol, 1985). For many of the most successful English-speaking students, on the other hand, foreign language study is a sign of excellence, preeminence, and academic promise. The law declares that classes conducted exclusively in English are â€Å"inadequate† for the education of children whose native tongue is another language and that bilingual education programs are necessary â€Å"to ensure equal educational opportunity to every child† (Pialorsi, 1974). Massachusetts became the first state to require and provide bilingual programs for children whose first language is not English. Soon after New York, California, Illinois, and Texas had laws permitting local school districts to provide bilingual education (Pialorsi, 1974). Although bilingual education still does exist in many states. Florida has completely done away with it, and California is in the process of also getting rid of bilingual education. The ballot initiative, Proposition 227, will soon end bilingual education in California public schools. Bilingual education was fully designed to involve immigrant parents in the education of their kids or to meet the needs of a sudden influx of refugees. Under this new ballot, children will receive no more than one year in English instruction of what is called sheltered English. Though the sheltered method is untested as a means of moving large numbers of kids into the mainstream classes, it is now the law (The New Republic, 1998). Proposition 227 was written by Ron Unz, a Republican multimillionaire from Silicon Valley, he got the idea from a group of Mexican-American parents. Most of the parents thought that the bilingual education system was holding their children back. Polls taken before Tuesday†s election indicated that anywhere â€Å"between 30 and 60 percent of Latino voters on California approved of the measure (The New Republic, 1998). Latinos agreed with this ballot as opposed to Proposition 187, where they took it as a form of immigrant bashing. In a monograph published by the New Jersey Teachers of English to Speakers of Other Languages-Bilingual Education (NJTESOL-BE), Professor Collier shares some of her less publicized insights. â€Å"We must encourage language-minority parents to speak the first language at home, not to speak English†¦to deny a child the only means of communicating with his parents†¦is tantamount to physical violence to that student† (Amselle, 1996). Bilingual Education can be a rewarding experience if instructed by the right people in the right manner. Sheltered English seems to be a terrible way to introduce English to a non-English speaker. The student will not be able to learn the language correctly and will not be able to get their correct thoughts across. Learning just pieces of a language is not good enough. That is just like going to a country that does not speak English as a first language with only one year of practice in that language. It will be difficult to ask where the bathroom is let alone take a test in that language. The only way for Bilingual Education to work is if they use the two-way bilingual education. This approach will allow both non-English speaking and English speaking students to learn each others† language. This approach will not make either student feel inferior to one another. Bilingual Education in the northeastern United States and Canada serves many advantages and benefits for students of limited English skills. The program has many good points and positive outcomes that out weight the negative outcomes. Most of the students result in success. Every child has their own style of learning and no matter what you are teaching there will always be one or two students that need special attention. Instead of doing away with the Bilingual Education program, they should design it so that it is full proof for the most part. The United States of America is considered the â€Å"melting pot†. There are so many different cultures and languages. There are people who are willing to put in extra effort and assist in making the Bilingual Education approach a successful one, and that is what should be put into perspective.

10+1 Reasons Why Students HATE College

10+1 Reasons Why Students HATE College This article’s going to bluntly set the record straight and explain why modern students increasingly hate college. Is it just the unwillingness to wake up in the morning and go to classes? Boring lectures? Poor grades? Teacher-student relations? We believe there is much more about the college hating issue and we are going to dig deeper. It’s not going to be pretty so be forewarned. Read at your own risk and enjoy! Reason #1: Students Feel Forced Into It Many youngsters see no other good options, it’s â€Å"student debt or bust.† The workforce seems in rapid systemic decay and the military isn’t their cup of tea. For whatever reason, these folks don’t believe they’re ready to be entrepreneurs. So, while they go through the motions and get decent grades perhaps, their heart really isn’t in it because they feel backed into a corner where college is the only escape. Reason #2: College is Earning a Bad Rap In the Western World â€Å"college† is definitely on the decline. 60% of college graduates are living at home with parents or working minimum wage jobs that don’t technically even require a high school diploma. The central banks high-jacked college educated and turned it into a debt-disbursement mechanism that few students seem to be benefiting from and the overall morale is suffering. Reason #3: The Costs are Getting Ridiculous It’s insane. In America over the last 30 years the price of a general college degree has risen by over 1000%! Interest rates have also gone up, along with the amount of student loans (over a trillion in the student loan bubble so far) bogging down the system. The whole mess wanes on the minds of students†¦ Reason #4: Social Anxiety Awkwardness Some college cultures are awesome beyond words, while others are hell holes for those that aren’t socially proficient or who havent yet matured enough to get along with the general college-going crowd. Reason #5: High School Days are OVER! In high school they were kick ass. In high school they were popular. In high school they rolled with the cool kids. In high school they were someone. Then they arrive on a four year college campus as a freshmen and find out that a) all that’s gone and b) they aren’t nearly as talented as they thought. This happens often in music departments, among thespians, sports, the arts, etc. Reason #6: Most Course Knowledge is Online Now Seriously, the system’s charging outrageous rates for knowledge that can be found via a quick Google search for free. How about all the free and inexpensive e-courses showing up online? How about the increasing library of free and inexpensive e-books containing the same knowledge directly from experts in their fields? The list goes on and on†¦yet still many are compelled by the â€Å"piece of paper† the established route awards (diploma). Reason #7: The Food’s Crummy With the amount of cash flowing into the bank coffers you’d think colleges could afford to feed students nothing but the best. They should be shelling out 5-Star delicacies for these prices! But, alas, dorm food is usually crummy and anything bought on-campus tastes like cheap buffet grubs. Reason #8: Indecisiveness Students get to college and have no idea what to do, what to major or minor in, or why they’re on campus at all. This indecisiveness can be crippling, daunting, and overwhelming to say the least. It causes both social and scholastic paralysis and this does not make their experience all that pleasant. Reason #9: Low Grades and Gargantuan Classes These two things go together because they feed on one another. Low grades obviously suck, but oftentimes the reason behind poor performance has to do with a lack of proper engagement with experts within the major. Being just another face in a crowd, or one in 50-100 students in a classroom, isn’t inspiring. Reason #10: Dashed Expectations Throughout middle school and high school they built up this fantasy of what college would be like, or should be like. Within a couple months of their freshmen year all these expectations are promptly laid to waste. Maybe they aren’t being invited to the mega parties. Maybe they didn’t make the team. Maybe the major they chose turned out to be a bummer in terms of course work. You get the idea. Extra Reason: The Future of College is Uncertain So there’s all of that stuff†¦ coupled with the rise of automation, AI, the digital workforce (no college necessary), and culture-wide disillusionment with â€Å"higher† education. Where will the traditional college institution be in another 4 or 5 years? It’s hard to say. Many will have closed their doors, while others will have transitioned into something else altogether. Speaking of which, when you picture the colleges of the future, what do you see? Do they even exist or has the internet and mobile devices completely taken over? Do you hate college too? Or is there a bright side?