The Great Gatsby As A Satire Essay -- essays research papers

The Great Gatsby as a Satire   Ã‚  Ã‚  Ã‚  Ã‚  Satire is an implement used by authors to point out a flaw of society or group of people in general. There are different levels of satire that the author can use. For example, the author may employ a type a formal satire known as Juvenalian satire. Here, the writer points out a subject with anger and contempt for it in a bitter fashion. There is also the contrasting form of Juvenalian satire called Horatian satire. Here, the writer points out a subject with a gentleness and jovial tenderness. The second main type of satire is informal. This is the type of satire used in The Great Gatsby. Here, Fitzgerald uses Nick to point out the character's flaws and makes each person the butt of the witticism by what they themselves do. The supposed 'guests'; at all of Gatsby's parties are prime examples of satire in The Great Gatsby. Many people who attended the parties were never even invited. This disregard for propriety illustrates the crassness and thoughtlessness that seemed to run rampant among the rich and famous during the twenties. An example of carelessness is when a large group of people at one of the huge soirees, decides to continue the party in the massive, expensive fountain in Gatsby's lawn. They just jump right in and begin to dance without concern for their health, much less concern for the well being of the fountain. After the galas had died down, most of the participants went home, leaving a monstrous mess...

Computational Efficiency of Polar

Lecture Notes on Monte Carlo Methods Fall Semester, 2005 Courant Institute of Mathematical Sciences, NYU Jonathan Goodman, [email  protected] nyu. edu Chapter 2: Simple Sampling of Gaussians. created August 26, 2005 Generating univariate or multivariate Gaussian random variables is simple and fast. There should be no reason ever to use approximate methods based, for example, on the Central limit theorem. 1 Box Muller It would be nice to get a standard normal from a standard uniform by inverting the distribution function, but there is no closed form formula for this distribution 2 x unction N (x) = P (X < x) = v1 ? e? x /2 dx . The Box Muller method is a 2 brilliant trick to overcome this by producing two independent standard normals from two independent uniforms. It is based on the familiar trick for calculating ? 2 e? x I= /2 dx . This cannot be calculated by â€Å"integration† – the inde? nite integral does not have an algebraic expression in terms of elementary f unctions (exponentials, logs, trig functions). However, ? 2 e? x I2 = ? /2 e? y dx 2 ? /2 ? 2 e? (x dy = +y 2 )/2 dxdy . The last integral can be calculated using polar coordinates x = r cos(? ), y = r sin(? with area element dxdy = rdrd? , so that 2? I2 = r = 0? e? r 2 /2 rdrd? = 2? r = 0? e? r 2 /2 rdr . ? =0 Unlike the original x integral, this r integral is elementary. The substitution s = r2 /2 gives ds = rdr and ? e? s ds = 2? . I 2 = 2? s=0 The Box Muller algorithm is a probabilistic interpretation of this trick. If (X, Y ) is a pair of independent standard normals, then the probability density is a product: 2 2 1 1 ? (x2 +y2 )/2 1 e . f (x, y ) = v e? x /2  · v e? y /2 = 2? 2? 2? 1 Since this density is radially symmetric, it is natural to consider the polar coordinate random variables (R, ? de? ned by 0 ? ? < 2? and X = R cos(? ), and Y = R sin(? ). Clearly ? is uniformly distributed in the interval [0, 2? ] and may be sampled using ? = 2? U1 . Unlike the original dis tribution function N (x), there is a simple expression for the R distribution function: 2? r G(R) = P (R ? r) = r =0 ?=0 r 1 ? r 2 /2 e rdrd? = 2? e? r 2 /2 rdr . r =0 The same change of variable r 2 /2 = s, r dr = ds (so that r = r when s = r2 /2) allows us to calculate r 2 /2 e? s dx = 1 ? e? r G(r) = 2 /2 . s=0 Therefore, we may sample R by solving the distribution function equation1 G(R) = 1 ? e? R 2 /2 = 1 ?U2 , whose solution is R = ? 2 ln(U2 ). Altogether, the Box Muller method takes independent standard uniform random variables U1 and U2 and produces independent standard normals X and Y using the formulas ? = 2? U1 , R = ?2 ln(U2 ) , X = R cos(? ) , Y = R sin(? ) . (1) It may seem odd that X and Y in (13) are independent given that they use the same R and ?. Not only does our algebra shows that this is true, but we can test the independence computationally, and it will be con? rmed. Part of this method was generating a point â€Å"at random† on the unit circle. We sug gested doing this by choosing ? niformly in the interval [0, 2? ] then taking the point on the circle to be (cos(? ), sin(? )). This has the possible drawback that the computer must evaluate the sine and cosine functions. Another way to do this2 is to choose a point uniformly in the 2 ? 2 square ? 1 ? x ? 1, 1 ? y ? 1 then rejecting it if it falls outside the unit circle. The ? rst accepted point will be uniformly distributed in the unit disk x2 + y 2 ? 1, so its angle will be random and uniformly distributed. The ? nal step is to get a point on the unit circle x2 + y 2 = 1 by dividing by the length.The methods have equal accuracy (both are exact in exact arithmetic). What distinguishes them is computer performance (a topic discussed more in a later lecture, hopefully). The rejection method, with an acceptance probability ? ? 4 78%, seems e? cient, but rejection can break the instruction pipeline and slow a computation by a factor of ten. Also, the square root needed to compute 1 Re call that 1 ? U2 is a standard uniform if U2 is. for example, in the dubious book Numerical Recipies. 2 Suggested, 2 the length may not be faster to evaluate than sine and cosine.Moreover, the rejection method uses two uniforms while the ? method uses just one. The method can be reversed to solve another sampling problem, generating a random point on the â€Å"unit spnere† in Rn . If we generate n independent standard normals, then the vector X = (X1 , . . . , Xn ) has all angles equally n likely (because the probability density is f (x) = v1 ? exp(? (x2 + ·  ·  ·+x2 )/2), n 1 2 which is radially symmetric. Therefore X/ X is uniformly distributed on the unit sphere, as desired. 1. 1 Other methods for univariate normals The Box Muller method is elegant and reasonably fast and is ? ne for casual omputations, but it may not be the best method for hard core users. Many software packages have native standard normal random number generators, which (if they are any good) use e xpertly optimized methods. There is very fast and accurate software on the web for directly inverting the normal distribution function N (x). This is particularly important for quasi Monte Carlo, which substitutes equidistributed sequences for random sequences (see a later lecture). 2 Multivariate normals An n component multivariate normal, X , is characterized by its mean  µ = E [X ] and its covariance matrix C = E [(X ?  µ)(X ?  µ)t ].We discuss the problem of generating such an X with mean zero, since we achieve mean  µ by adding  µ to a mean zero multivariate normal. The key to generating such an X is the fact that if Y is an m component mean zero multivariate normal with covariance D and X = AY , then X is a mean zero multivariate normal with covariance t C = E X X t = E AY (AY ) = AE Y Y t At = ADAt . We know how to sample the n component multivariate normal with D = I , just take the components of Y to be independent univariate standard normals. The formula X = AY w ill produce the desired covariance matrix if we ? nd A with AAt = C .A simple way to do this in practice is to use the Choleski decomposition from numerical linear algebra. This is a simple algorithm that produces a lower triangular matrix, L, so that LLt = C . It works for any positive de? nite C . In physical applications it is common that one has not C but its inverse, H . This would happen, for example, if X had the Gibbs-Boltzmann distribution with kT = 1 (it’s easy to change this) and energy 1 X t HX , and probability 2 1 density Z exp(? 1 X t HX ). In large scale physical problems it may be impracti2 cal to calculate and store the covariance matrix C = H ? though the Choleski factorization H = LLt is available. Note that3 H ? 1 = L? t L? 1 , so the choice 3 It is traditional to write L? t for the transpose of L? 1 , which also is the inverse of Lt . 3 A = L? t works. Computing X = L? t Y is the same as solving for X in the equation Y = Lt X , which is the process of ba ck substitution in numerical linear algebra. In some applications one knows the eigenvectors of C (which also are the eigenvectors of H ), and the corresponding eigenvalues. These (either the eigenvectors or the eigenvectors and eigenvalues) sometimes are called principal com2 ponents.Let qj be the eigenvectors, normalized to be orthonormal, and ? j the corresponding eigenvalues of C , so that 2 Cqj = ? j qj , t qj qk = ? jk . t Denote the qj component of X by Zj = qj X . This is a linear function of X and t therefore Gaussian with mean zero. It’s variance (note: Zj = Zj = X t qj ) is 2 t t t 2 E [Zj ] = E [Zj  · Zj ] = qj E [XX t ]qj = qj Cqj = ? j . A similar calculation shows that Zj and Zk are uncorrelated and hence (as components of a multivariate normal) independent. Therefore, we can generate Yj as independent standard normals and sample the Zj using Zj = ? j Yj . (2) After that, we can get an X using Zj qj . X= (3) j =1 We restate this in matrix terms. Let Q be the orthogonal matrix whose columns are the orthonormal eigenvectors of C , and let ? 2 be the diagonal ma2 trix with ? j in the (j, j ) diagonal position. The eigenvalue/eigenvector relations are CQ = Q? 2 , Qt Q = I = QQt . (4) The multivariate normal vector Z = Qt X then has covariance matrix E [ZZ t ] = E [Qt XX t Q] = Qt CQ = ? 2 . This says that the Zj , the components of Z , are 2 independent univariate normals with variances ? j . Therefore, we may sample Z by choosing its components by (14) and then reconstruct X by X = QZ , which s the same as (15). Alternatively, we can calculate, using (17) that t C = Q? 2 Qt = Q Qt = (Q? ) (Q? ) . Therefore A = Q? satis? es AAt = C and X = AY = Q? Y = QZ has covariance C if the components of Y are independent standard univariate normals or 2 the components of Z are independent univariate normals with variance ? j . 3 Brownian motion examples We illustrate these ideas for various kids of Brownian motion. Let X (t) be a Brownian motion path. Choose a ? nal time t and a time step ? t = T /n. The 4 observation times will be tj = j ? t and the observations (or observation values) will be Xj = X (tj ).These observations may be assembled into a vector X = (X1 , . . . , Xn )t . We seek to generate sample observation vectors (or observation paths). How we do this depends on the boundary conditions. The simplest case is standard Brownian motion. Specifying X (0) = 0 is a Dirichlet boundary condition at t = 0. Saying nothing about X (T ) is a free (or Neumann) condition at t = T . The joint probability density for the observation vector, f (x) = f (x1 , . . . , xn ), is found by multiplying the conditional densities. Given Xk = X (tk ), the next observation Xk+1 = X (tk + ? ) is Gaussian with mean Xk and variance ? t, so its conditional density is v 2 1 e? (xk+1 ? Xk ) /2? t . 2? ?t Multiply these together and use X0 = 0 and you ? nd (with the convention x0 = 0) f (x1 , . . . , xn ) = 3. 1 1 2? ?t n/2 exp ?1 2 ? Deltat n? 1 (xk+ 1 ? xk )2 . (5) k=0 The random walk method The simplest and possibly best way to generate a sample observation path, X , comes from the derivation of (1). First generate X1 = X (? t) as a mean zero v univariate normal with mean zero and variance ? t, i. e. X1 = ? tY1 . Given X1 , X2 is a univariate normal with mean X1 and variance ? , so we may v take X2 = X1 + ? tY2 , and so on. This is the random walk method. If you just want to make standard Brownian motion paths, stop here. We push on for pedigogical purposes and to develop strategies that apply to other types of Brownian motion. We describe the random walk method in terms of the matrices above, starting by identifying the matrices C and H . Examining (1) leads to ? 2 ? 1 0  ·Ã‚ ·Ã‚ · ? ? ? 1 2 ? 1 0  ·Ã‚ ·Ã‚ · ? ? .. .. .. . . . 1 ? 0 ? 1 ? H= ?. .. ?t ? . . 2 ? 1 ?. ? .. ? . ? 1 2 0  ·Ã‚ ·Ã‚ · 0 ? 1 ? 0 .? .? .? ? ? ? ? 0? ? ? ?1 ? 1 This is a tridiagonal matrix with pattern ? 1, 2, ? except at the bottom right corner. O ne can calculate the covariances Cjk from the random walk representation v Xk = ? t (Y1 +  ·  ·  · + Yk ) . 5 Since the Yj are independent, we have Ckk = var(Xk ) = ? t  · k  · var(Yj ) = tk , and, supposing j < k , Cjk = E [Xj Xk ] = ? tE [((Y1 +  ·  ·  · + Yj ) + (Yj +1 +  ·  ·  · + Yk ))  · (Y1 +  ·  ·  · + Yj )] = 2 ?tE (Y1 +  ·  ·  · + Yj ) = tj . These combine into the familiar formula Cjk = cov(X (tj ), X (tk )) = min(tj , tk ) . This is the same as saying that the ? 1 ?1 ? ?. ?. C = ? t ? . ? ? ? 1 matrix C is 1  ·Ã‚ ·Ã‚ · 2 2  ·Ã‚ ·Ã‚ · 2 . . . 3  ·Ã‚ ·Ã‚ · . . . 2 3  ·Ã‚ ·Ã‚ · ? 1 2? ? ? 3? .? .? .? .. . (6) The random walk method for generating X may be expresses as ? ? ? Y ? X1 1 1 0  ·Ã‚ ·Ã‚ · 01 ? ? ? ?1 1 0  ·Ã‚ ·Ã‚ · 0 ? ? . ? ?.? ?.? v? ? . ? ?.? 1 0 . . ? . .? ? . ? = ? t ? 1 1 ? ? ? ? ?. . .. ? ? ? ?. . . .. ? ? ? ? 11 1  ·Ã‚ ·Ã‚ · 1 Yn Xn Thus, X = AY with ? ? 1 0  ·Ã‚ ·Ã‚ · 01 ?1 1 0  ·Ã‚ ·Ã‚ · 0 ? ? ? v? .? .? . ?1 1 1 0 .? A = ? t ? ?. . ? .. .. ?. . ? . 11 1  ·Ã‚ ·Ã‚ · 1 (7) The reader should do the matrix multiplication to check that indeed C = AAt for (6) and (7). Notice that H is a sparse matrix indicating short range interactions while C is full indicating long range correlations.This is true of in great number of physical applications, though it is rare to have an explicit formula for C . 6 We also can calculate the Choleski factorization of H . The reader can convince herself or himself that the Choleski factor, L, is bidiagonal, with nonzeros only on or immediately below the diagonal. However, the formulas are simpler if we reverse the order of the coordinates. Therefore we de? ne the coordinate reversed observation vector t X = (Xn , xn? 1 , . . . , Xn ) and whose covariance matrix is ? tn ? tn? 1 ? C=? . ?. . t1 tn? 1 tn? 1  ·  ·  · t1 t1 .. .  ·Ã‚ ·Ã‚ · ? ? ? , ? t1 and energy matrix ? 1 ? 1  ·Ã‚ ·Ã‚ · 0 ? 0 .? .? .? ? ? ?. ? 0? ? ? ?1 ? 2 ? ? ? 1 2 ? 1 0  ·Ã‚ ·Ã‚ · ? ? .. .. .. . . . 1 ? 0 ? 1 ? H= .. ?t ? . . ?. . 2 ? 1 ? ? .. ? . ? 1 2 0  ·Ã‚ ·Ã‚ · 0 ? 1 We seek the Choleski factorization H = LLt ? l1 0 ? m2 l2 1? L= v ? m3 ?t ? 0 ? . .. . . . with bidiagonal ?  ·Ã‚ ·Ã‚ · ? 0 ? ?. .. ? . ? .. . Multiplying out H = LLt leads to equations that successively determine the lk and mk : 2 l1 l 1 m2 2 2 l1 + l 2 l 2 m3 = 1 =? l1 = 1 , = ? 1 =? m2 = ? 1 , = 2 =? l2 = 1 , = 1 =? m3 = ? 1 , etc. , The result is H = LLt with L simply ? 1 0  ·Ã‚ ·Ã‚ · ? ? 1 10 1? .. L= v ? . ?t ? ? 1 ? . .. .. . . . . 7 ? ? ? ?. ? ? The sampling algorithm using this Y = Lt X : ? ? ? 1 Yn ? Yn? 1 ? ? ? ? ?0 ? ? 1? ? ? ? ? . ?= v ? ?.? ?t ? ?.? ?. ? ? ?. . Y1 0 information is to ? nd X from Y by solving ?1 0 1 .. . ?1 .. .  ·Ã‚ ·Ã‚ ·  ·Ã‚ ·Ã‚ · .. . 0 0 Xn . ? ? Xn? 1 . . . 0 . . ?1 X1 1 ? ? ? ? ? ? ? ? ? Solving from the bottom up (back substitution), we have Y1 = Y2 = v 1 v X1 =? X1 = ? tY1 , ?t v 1 v (X2 ? X1 ) =? X2 = X1 + ? tY2 , etc. ? t This whole process turns out to give the same random walk sampling method. Had we not gone to the time reversed (X , etc. variables, we could have calculated the bidiagonal Choleski factor L numerically. This works for any problem with a tridiagonal energy matrix H and has a name in the control theory/estimation literature that escapes me. In particular, it will allow to ? nd sample Brownian motion paths with other boundary conditions. 3. 2 The Brownian bridge construction The Brownian bridge construction is useful in the mathematical theory of Brownian motion. It also is the basis for the success of quasi Monte Carlo methods in ? nance. Suppose n is a power of 2: n = 2L . We will construct the observation path X through a sequence of L re? ements. First, notice that Xn is a univariate normal with mean zero and variance T , so we may take (with Yk,l being independent standard normals) v Xn = T Y1,1 . Given the value of Xn , the midoint observation, Xn/2 , is a univariate normal4 w ith mean 1 Xn and variance T /4, so we may take 2 Xn 2 v 1 T = Xn + Y2,1 . 2 2 At the ? rst level, we chose the endpoint value for X . We could draw a ? rst level path by connenting Xn to zero with a straight line. At the second level, or ? rst re? nement, we created a midpoint value. The second level path could be piecewise linear, connecting 0 to X n to Xn . 4 We assign this and related claims below as exercises for the student. 8 The second re? nement level creates values for the â€Å"quarter points†. Given n X n , X n is a normal with mean 1 X n and variance 1 T . Similarly, X 34 is a 2 42 2 4 2 1 1T normal with mean 2 (X n + Xn ) and variance 4 2 . Therefore, we may take 2 Xn = 4 1 1 Xn + 22 2 T Y3,1 2 and n X 34 = 1 1 (X n + Xn ) + 2 2 2 T Y3,2 . 2 1 The level three path would be piecewise linear with breakpoints at 1 , 2 , and 3 . 4 4 Note that in each case we add a mean zero normal of the appropriate variance to the linear interpolation value.In the general step, we go from the level k ? 1 path to the level k paths by creating values for the midpoints of the level k ? 1 intervals. The level k observations are X j . The values with even j are known from the previous 2k? 1 level, so we need values for odd j . That is, we want to interpolate between the j = 2m value and the j = 2m + 2 value and add a mean zero normal of the appropriate variance: X (2m+1)n = 2k? 1 1 2 mn X 2k? 1 + X (2m+2)n 2 2k? 1 + 1 2(k? 2)/2 T Ym,k . 2 The reader should check that the vector of standard normals Y = (Y1,1 , Y2,1 , Y3,1 , Y3,2 , . . . t indeed has n = 2L components. The value of this method for quasi Monte Carlo comes from the fact that the most important values that determine the large scale structure of X are the ? rst components of Y . As we will see, the components of the Y vectors of quasi Monte Carlo have uneven quality, with the ? rst components being the best. 3. 3 Principle components The principle component eigenvalues and eigenvectors for many types of Brownian motion are known in closed form. In many of these cases, the Fast Fourier Transform (FFT) algorithm leads to a reasonably fast sampling method.These FFT based methods are slower than random walk or Brownian bridge sampling for standard random walk, but they sometimes are the most e? cient for fractional Brownian motion. They may be better than Brownian bridge sampling with quasi Monte Carlo (I’m not sure about this). The eigenvectors of H are known5 to have components (qj,k is the k th component of eigenvector qj . ) qj,k = const  · sin(? j tk ) . 5 See e. g. Numerical Analysis by Eugene Isaacson and Herbert Keller. 9 (8) The n eigenvectors and eigenvalues then are determined by the allowed values of ? j , which, in turn, are determined throught the boundary conditions.We 2 2 can ? nd ? j in terms of ? j using the eigenvalue equation Hqj = ? j qj evaluated at any of the interior components 1 < k < n: 1 2 [? sin(? j (tk ? ?t)) + 2 sin(? j tk ) ? sin(? j (tk + ? t)) ] = ? j sin(? j tk ) . ?t Doing the math shown that the eigenvalue equation is satis? ed and that 2 ?j = 2 1 ? cos(? j ? t) . ?t (9) The eigenvalue equation also is satis? ed at k = 1 because the form (8) automatically satis? es the boundary condition qj,0 = 0. This is why we used the sine and not the cosine. Only special values ? j give qj,k that satisfy the eigenvalue equation at the right boundary point k = n. 10

Healthy environment Essay

A healthy environment to me would be a community with low pollution, stable economical levels, clean water and communicates who communicate well with one another. The residents in this community would be happy, and would encourage others to come live in their community (Maurer & Smith, 2013). There would be quality health care services available and good schools for the children to promote knowledge. This would help provide education to promote successful lifestyles (Maurer & Smith, 2013). Nursing cares could be provided depending on what the needs were. Nurses could hold seminars to provide the community ways to better their health. They can provide teaching to assist with decreasing pollution in their communities. If there is a high amount of respiratory illness in that community, then minimizing the pollutants would be beneficial. They could start smoke-free areas, teach about decreasing litter to prevent pests, then the pollutant of exterminating gases would be decreased. Nursing could gather statistics about the community’s health to provide information to help promote good health. Maurer, F.A. & Smith, C.M. 2013. Community/Public Health Nursing Practice: Health for Families and Populations, 5th edition. Retrieved from: http//pagebursts.elsevier.com

SAT Score Tables - Compare Admissions Data for Colleges

SAT Score Tables - Compare Admissions Data for Colleges Below youll find links to dozens of articles that can help you put your SAT scores in context for a wide range of colleges and universities. Always keep in mind that the SAT is just one part of your application, and less-than-ideal scores dont need to torpedo your chances of admission if you have strengths in other areas. Top College and University SAT Tables: See how the countrys most prestigious colleges and universities compare on the SAT front (or you can check out the ACT comparison charts). The Ivy LeagueTop Universities (non-Ivy)Top 10 Liberal Arts CollegesTop 10 Public Universities22 More Top Public UniversitiesTop Public Liberal Arts CollegesTop Engineering Schools (PhD Granting)Top Engineering Schools (Bachelors and Masters)Top Womens CollegesTop Catholic Colleges and Universities State University SAT Data: Admissions criteria vary widely from campus to campus within state university systems. These charts can help you find schools that match your SAT scores. Alabama: Four-Year Alabama Colleges and UniversitiesAlaska: Four-Year Alaska Colleges and UniversitiesArizona: Four-Year Arizona Colleges and UniversitiesArkansas: Four-Year Arkansas Colleges and UniversitiesCalifornia: Cal State SystemCalifornia: UC SystemCalifornia: Top California Colleges and UniversitiesColorado: Four-Year Colorado CollegesConnecticut: Four-Year Colleges and UniversitiesDelaware: Four-Year Delaware Colleges and UniversitiesDistrict of Columbia: Four-Year Washington D.C. Colleges and UniversitiesFlorida: State University SystemFlorida: Top Florida Colleges and UniversitiesGeorgia: Top Georgia Colleges and UniversitiesHawaii: Four-Year Hawaii Colleges and UniversitiesIdaho: Four-Year Idaho Colleges and UniversitiesIllinois: Top Illinois Colleges and UniversitiesIndiana: 15 Top Indiana Colleges and UniversitiesIowas: Four-Year Iowa Colleges and UniversitiesKansas: Four-Year Kansas Colleges and UniversitiesKentucky: Four-Year Kentucky Colleges and UniversitiesLouisia na: Four-Year Louisiana Colleges and Universities Maine: Four-Year Maine Collegs and UniversitiesMaryland: Top Maryland Collegs and UniversitiesMassachusetts: Top Massachusetts Collegs and UniversitiesMichigan: 13 Top Michigan Colleges and UniversitiesMinnesota: 13 Top Minnesota Colleges and UniversitiesMississippi: Four-Year Mississippi Colleges and UniversitiesMissouri: 12 Top Missouri Colleges and UniversitiesMontana: Four-Year Montana Colleges and UniversitiesNebraska: Four-Year Nebraska Colleges and UniversitiesNevada: Four-Year Nevada Colleges and UniversitiesNew Hampshire: New Hampshire Colleges and UniversitiesNew Jersey: Four-Year New Jersey Colleges and UniversitiesNew Mexico: Four-Year New Mexico Colleges and UniversitiesNew York: CUNY Senior CollegesNew York: SUNY SystemNew York: Top New York Colleges and UniversitiesNorth Carolina: 16 Public UniversitiesNorth Carolina: Top North Carolina Colleges and UniversitiesNorth Dakota: Four-Year North Dakota Colleges and UniversitiesOhio: 10 Top Ohio Colleges and UniversitiesOhio : 13 University System of Ohio campuses Oklahoma: Four-Year Oklahoma Colleges and UniversitiesOregon: Selective Oregon Colleges and UniversitiesPennsylvania: Top Pennsylania Colleges and UniversitiesRhode Island: Four-Year Rhode Island Colleges and UniversitiesSouth Carolina: Four-Year South Carolina Colleges and UniversitiesSouth Dakota: Four-Year South Dakota Colleges and UniversitiesTennessee: Top Tennessee Collegs and UniversitiesTexas: 13 Top Texas Colleges and UniversitiesUtah: Four-Year Utah Colleges and UniversitiesVermont: Four-Year Vermont Colleges and UniversitiesVirginia: 15 Public UniversitiesVirginia: 17 Top Virginia Colleges and UniversitiesWashington: 11 Top Washington Colleges and UniversitiesWest Virginia: Four-Year West Virginia Colleges and UniversitiesWisconsin: Four-Year Wisconsin Colleges and Universities SAT Scores for Division I Athletic Conferences: For students interested in the excitement of Division I sports, these charts make some of the admissions distinctions between universities clear. America East ConferenceAtlantic 10 ConferenceAtlantic Coast ConferenceAtlantic Sun ConferenceBig East ConferenceBig Sky ConferenceBig South ConferenceBig Ten ConferenceBig 12 ConferenceConference USA (C-USA)Horizon LeagueMetro Atlantic Athletic ConferenceMid-American ConferenceMissouri Valley ConferenceMountain West ConferenceNortheast ConferenceOhio Valley ConferencePac 12Â  ConferenceSoutheastern ConferenceSouthern ConferenceSun Belt ConferenceWestern Athletic Conference More SAT Information: Here are some more articles to help you make sense of the SAT. Whats a Good SAT Score?What Schools Dont Require Scores?When is the SAT?Should I use SAT Score Choice?What Schools Require SAT Subject Tests?Are SAT Prep Courses Worth the Cost?Does the SAT Writing Section Matter?

Eal School Systems and Policies for Meeting the Needs of Children with Eal and or Black and Minority Pupils Essays

Eal School Systems and Policies for Meeting the Needs of Children with Eal and or Black and Minority Pupils Essays Eal School Systems and Policies for Meeting the Needs of Children with Eal and or Black and Minority Pupils Essay Eal School Systems and Policies for Meeting the Needs of Children with Eal and or Black and Minority Pupils Essay Essay Topic: Middle School EAL TASK 1 (Q13) School systems and policies for meeting the needs of children with EAL and/or Black and Minority Ethnic pupils What are the needs in this school regarding EAL and ethnic minority achievement? Middle Row Primary School has a population of 39% pupils who are bilingual. A total number of 21 different languages are spoken throughout the school. According to the Race Equality Policy, 57% of the pupils have English as an additional language. The school has a majority of the ethnic minority as stated below: 17% Mixed White/Caribbean Moroccan – 15% Black Caribbean – 12% Somali – 11% White British – 11% What written policies are there about the school’s approach? The school has a general EMA policy which highlights the school aims in regards to the needs of EAL children. According to the policy the school employs a full time teacher funded by the Language Development Service other than 2 teachers which are funded by the school. In reali ty there is one full time EAL teacher. There is also a section in this policy on new arrivals to the school where it states that the school ‘ runs an induction programme for pupils at the very early stages of learning English. Furthermore, in terms of learning and teaching, the EMA policy acknowledges the large number of pupils which qualify for EAL support, therefore it prioritises its support offered. The EAL teacher liaises with the class teacher in terms of the child’s assessment and development. The school also has a Race Equality Policy to ensure the school/staff fulfils its obligation under the Race Relation Act 1976 [amended 2000]. What are your school’s procedures for raising the standards of BME children and/or children with EAL – are there any particular support programme, strategies or resources used? Middle Row uses the RBKC’s assessment forms for children with EAL needs. This is practiced twice a year by the school staff. Other than this, the EMA teacher and the class teacher also assess children each term. Target levels are than set for the child to achieve by the end of next term. The assessment includes speaking and listening, reading and writing skills test for literacy. What support is available for children at the whole school and classroom level for both groups? The EMA teacher not only works alongside with the Class teacher during sessions or can arrange one to one sessions depending on the class timetable. Due to shortage of staff it becomes impossible to extend support to each and every EAL child present in the school. Support is still provided to the best of ability. How are parents involved and what systems exist for developing home-school relationship? In autumn term (Nov ’09) MRPS in coordination with the RBKC arranged a session for the parents of EAL children. Many parents attended the session, which was mainly focused on the importance of home languages. The school is working towards involving the parents and having more sessions on developing home-school relationships. In the past there have been sessions on parent child reading in the classroom which was quite a success. Find out how it uses local and national data to inform its practice. Are there any particular groups that are targeted for support? The school prioritizes in providing support to beginner levels of EAL children. So far, the school haven’t categorised any particular target group as the main focus stays with children who are new in the country or the ones with little or no English language at all. In what ways have the children’s knowledge and experiences been taken into ccount in curriculum content? The Friday assemblies are a main feature which highlights different cultures and traditions. The children can relate to these assemblies as the parents of children performing are invited to watch the assembly. Awareness is spread about issues like racism and bullying. When planning sessions, teachers do tend to keep interest of the child’s knowledge and experiences and try and make a relation from the curriculum point of view. What place do first languages and culture have in the school and classroom? Does it promote pluralingualism? First languages are not spoken as commonly probably because English is the main language spoken. Spanish is a popular language in the school because of the presence of specialist Spanish staff. Lessons are delivered every week in Spanish and children tend to enjoy these sessions. The school does promote pluralingualism as the school staffs cherish children who speak other languages. Display posters, dual language books are available in the school but not put into much use as one would imagine.

An Unforgettable Experience.

His sparkling green eyes always gave an aura of intelligence. Every time he smiled it made my heart melt like a hot knife cutting through butter. The only one who knew about this feeling of mine was my best friend, Ida. She was a very encouraging friend of me. She even helped me hooked up with Dustin. I was the girl who only focused on my studies and my social life was not exciting. Dustin, on the other hand, was one of the top guys in the girls’ list who was a must-go-date with. His popularity started when he scored a lot of baskets for his basketball team. But most importantly, he was the Captain of the Tiger basketball team. One day, the Tiger Team had a big game with Leopard Team. He played brilliantly with great jump, shots, dunks and assists. His teammates also played their hearts out to win the game. Just two minutes before the game ended, they were trailing 90-96. They managed to force three turnovers, two steals and scored five baskets. Three shots were made by Dustin. Finally, our Tiger Team won by 101-99. Ida and I were very excited and we screamed our lungs out for the victory. Dustin then took the microphone from the announcer. He said in front of hundreds of people that before he left high school, he wanted to tell everyone a secret. He liked someone, a girl. Nobody knew and not even his own friend. Suddenly, the stadium became quiet as all the girls wished that they were the chosen one. Then he said, â€Å"Amanda Ann, I do really like you,† â€Å"What? Did he just mention my name? I asked Ida. I was shocked and felt like fainting at that moment. I could not believe it. It was just like a dream came true. I suddenly dreamed that I was wearing a Cinderella gown and hearing the ringing of bells. Then, everybody in the stadium was staring at me. He then ran towards me and told me that he liked me a lot since the first day I sat foot in this school. I became dumbfounded and my face turned red as all girls w ere very envious of me. Even though this incident had happened a week ago, it was still fresh in my mind. An Unforgettable Experience. His sparkling green eyes always gave an aura of intelligence. Every time he smiled it made my heart melt like a hot knife cutting through butter. The only one who knew about this feeling of mine was my best friend, Ida. She was a very encouraging friend of me. She even helped me hooked up with Dustin. I was the girl who only focused on my studies and my social life was not exciting. Dustin, on the other hand, was one of the top guys in the girls’ list who was a must-go-date with. His popularity started when he scored a lot of baskets for his basketball team. But most importantly, he was the Captain of the Tiger basketball team. One day, the Tiger Team had a big game with Leopard Team. He played brilliantly with great jump, shots, dunks and assists. His teammates also played their hearts out to win the game. Just two minutes before the game ended, they were trailing 90-96. They managed to force three turnovers, two steals and scored five baskets. Three shots were made by Dustin. Finally, our Tiger Team won by 101-99. Ida and I were very excited and we screamed our lungs out for the victory. Dustin then took the microphone from the announcer. He said in front of hundreds of people that before he left high school, he wanted to tell everyone a secret. He liked someone, a girl. Nobody knew and not even his own friend. Suddenly, the stadium became quiet as all the girls wished that they were the chosen one. Then he said, â€Å"Amanda Ann, I do really like you,† â€Å"What? Did he just mention my name? I asked Ida. I was shocked and felt like fainting at that moment. I could not believe it. It was just like a dream came true. I suddenly dreamed that I was wearing a Cinderella gown and hearing the ringing of bells. Then, everybody in the stadium was staring at me. He then ran towards me and told me that he liked me a lot since the first day I sat foot in this school. I became dumbfounded and my face turned red as all girls w ere very envious of me. Even though this incident had happened a week ago, it was still fresh in my mind.

Marketing Communications Plan Essay Example | Topics and Well Written Essays - 2000 words - 1

Marketing Communications Plan - Essay Example 4). A marketing communication plan aims at building market awareness of a product or service. It also aims at coming up with the ways and means of reaching the market and impacting it as much as possible The product in this marketing communication plan is the iPhone and this plan has been designed using the SOSTT+4Ms structure (Blythe 2006, p. 53) IPhone is a brand of smartphones created and marketed by the Apple brand. It was introduced for the first time in the market in 2007 and has undergone four transformations. Currently, the most advanced of the iPhones is the iPhone 4, which has greater capability than the first three generations of iPhones that were introduced into the market. The first two models of the iPhones have ceased being produced. IPhones were the first to add a compass and a front facing camera to their features. The first iPhone was made public in 2007 and it immediately received massive approval in the market with millions of sales being made in the months to come. A year later the iPhone 3G was introduced in the market again and in 2009 the iPhone 3GS was introduced. This latest model was much cheaper than the others, as Apple was making an attempt to get a hold of a larger market than with the first two generations of iPhones. With the iPhone 4 being the latest model in the market, the iPhone 3GS is the cheapest model in the market (Apple Inc., 2011). Upon the introduction of the iPhones into the market, sales have shot through the roof. Millions of sales were made in the first few quarters of the existence of the iPhones in the market. By the end of the financial year 2010, there were more than 72 million units of iPhones sold all over the world. It is expected that 2011 will show a tremendous improvement in these figures thanks to the introduction of the iPhone 4 into the market. Currently, the iPhone holds more than 50% of the worlds profit in mobile phone sales and has for a while