Obesity A Major Problem Today Society Within The United...

Obesity is a major problem in today’s society within the United States. To be more specific, childhood obesity. Childhood obesity is becoming worse, and the adults don’t realize the impact it has on the rest of the child s life. According to Americas Let’s Move initiative the definition of Obesity is, â€Å"excess body fat. Because body fat is difficult to measure directly, obesity is often measured by body mass index (BMI), a common scientific way to screen for whether a person is underweight, normal weight, overweight, or obese ( Obama). According to Jean Cowie, â€Å"obesity is caused by an imbalance in equilibrium between energy intake and energy expenditure† (Cowie). Metabolism plays a huge role in the upcoming stages of becoming obese. One of†¦show more content†¦The complications can range from heart disease, type 2 diabetes, asthma, high cholesterol levels, high blood pressure, and even sleep apnea. These health complications lead to many social complications. Social complications that can be linked to things such as bullying. According to Americas Let’s Move initiative, â€Å"overweight and obese children can often be targets of early social discrimination†. â€Å"Numerous studies have documented harmful weight-based stereotypes that overweight and obese individuals are lazy, weak-willed, unsuccessful, unintelligent, lack self-discipline, have poor willpower, and are noncompliant with weight-loss treatment (Obama). This being stated allows for psychological stress in an individual which can also lead to lower self-esteem. With lower self-esteem comes self-image problems. Self-image is becoming a major problem in today’s society because of the pressure that is put on both males and females to look a certain way in order to be portrayed as â€Å"beautiful†. At this point in the obese stage, it can cause the child to begin to go into a depressed state. Depression can have lifelong circumst ances such as poor health, poor living conditions, no motivation, and with these individuals tend to become obese. Along with depression, there are many other lifelong complications that can evolve from being obese from a young age. Self-image is a huge problem in obese children and can make an

Power Utility Consumption Capm in Uk Stock Markets Free Essays

string(114) " for values of risk aversion \(\? \) between 0 and 10 and values of the beta coefficient \(\? \) between 0 and 1\." Pricing of Securities in Financial Markets 40141 – How well does the power utility consumption CAPM perform in UK Stock Returns? ******** 1 Hansen and Jagannathan (1991) LOP Volatility Bounds Volatility bounds were first derived by Shiller (1982) to help diagnose and test a particular set of asset pricing models. He found that to price a set of assets, the consumption model must have a high value for the risk aversion coefficient or have a high level of volatility. Hansen and Jagannathan (1991) expanded on Shiller’s paper to show the duality between mean-variance frontiers of asset portfolios and mean-variance frontier of stochastic discount factors. We will write a custom essay sample on Power Utility Consumption Capm in Uk Stock Markets or any similar topic only for you Order Now Law of one price volatility bounds are derived by calculating the minimum variance of a stochastic discount factor for a given value of E(m), subject to the law of one price restriction. The law of one price restriction states that E(mR) = 1, which means that the assets with identical payoffs must have the same price. For this constraint to hold, the pricing equation must be true. Hansen and Jagannathan use an orthogonal decomposition to calculate the set of minimum variance discount factors that will price a set of assets. The equation m = x* + we* + n can be used to calculate discount factors that will price the assets subject to the LOP condition. Once x* and e* are calculated, the minimum variance discount factors that will price the assets can be found by changing the weights, w. Hansen and Jagannathan viewed the volatility bounds as a constraint imposed upon a set of discount factors that will price a set of assets. Therefore, when deriving the volatility bounds, we calculate the minimum variance stochastic discount factors that will price the set of assets. Discount factors that have a lower variance than these values will not price the assets correctly. Furthermore, Hansen and Jagannathan showed that to price a set of assets, we require discount factors with a high volatility and a mean close to 1. After deriving these bounds, we can use this constraint to test candidate asset pricing models. Models that produce a discount factor with a lower volatility than any discount factor on the LOP volatility can be rejected as they do not produce sufficient volatility. Hansen and Jagannathan find evidence that using LOP volatility bounds, we can reject a number of models such as the consumption model with a power function analysed in papers such as Dunn and Singleton (1986). 2 Methodology To test whether the power utility CCAPM prices the UK Treasury Bill (Rf) and value weighted market index returns, we first calculate the LOP volatility bounds. The volatility bound is derived by calculating the minimum variance discount factors that correctly price the two assets for given values of E (m). The standard deviations of the stochastic discount factors are then plotted on a graph to give the LOP volatility bound shown in figure one. Figure 1 here The CCAPM stochastic discount factors are then calculated for different levels of risk aversion. The mean and standard deviation of these discount factors are then plotted on the graph and compared to the LOP discount factor standard deviations. Pricing errors can then be calculated and analysed to see whether the assets are priced correctly by the candidate model. To accept the CCAPM model in pricing the assets, we expect the stochastic discount factors variance to be greater than the variance of the LOP volatility bounds. It is also expected that pricing errors and average pricing errors (RMSE) will be close to zero. These results will be analysed more closely in the later questions. 3 Power Utility CCAPM vs LOP Volatility Bounds In order for the power utility CCAPM to satisfy the Law of One Price volatility bound test at any level of risk aversion, the standard deviation f the CCAPM stochastic discount factor at that level of risk aversion must be above the Law of One Price standard deviation bound for the mean value of the CCAPM stochastic discount factor at the same level of risk aversion. This is the null hypothesis and if it is accepted then the model satisfies the test. The alternative hypothesis is that it the stand ard deviation of the stochastic discount factor is below the Law of One Price standard deviation bound for the mean value of the stochastic discount factor. If the null hypothesis is rejected and the alternative hypothesis is accepted then the model does not satisfy the test. Table 1 here Figure 2 here Figure 2 shows LOP volatility bounds and the standard deviations and means of the CCAPM stochastic discount factors for levels of risk aversion between 1 and 20. It is obvious the standard deviations (Sigma(m)) of the CCAPM stochastic discounts factors are much lower than the LOP volatility bounds corresponding to the means (E(m)) of the CCAPM stochastic discount factors. This is true for any level of risk aversion, because the entire CCAPM (green) line lies below the LOP volatility bounds (dark blue) line. Table 1 shows the standard deviations of the stochastic discount factors and the precise LOP volatility bound values, corresponding to the stochastic discount factor means so that the CCAPM can be formally tested. All of the standard deviations are lower than their respective volatility bound values. Therefore the null hypothesis is to be rejected and the alternative hypothesis is to be accepted for all levels of risk aversion between 1 and 20. Furthermore it would take a risk aversion of at least 54 to accept the null hypothesis. Therefore the power utility CCAPM stochastic discount factor does not satisfy the Law of One Price volatility bound test. These results are consistent with the equity premium puzzle study by Mehra and Prescott (1985). The study examines whether a consumption growth based model with a risk aversion value restricted to no more than 10 accurately prices equities. They have found that according to the model equity premiums should not exceed 0. 5% for values of risk aversion (? ) between 0 and 10 and values of the beta coefficient (? ) between 0 and 1. You read "Power Utility Consumption Capm in Uk Stock Markets" in category "Papers" However the average observed equity premium based on the average real return on nearly riskless short-term securities and the SP 500 for the period 1989-1978 was 6. 18%. This is clearly inconsistent with the predictions of the model. In particular if risk aversion is close to 0 and individuals are almost risk neutral, the model fails to explain why the sample’s average equity returns are so high. If risk aversion is significantly positive the model does not justify the low average risk-free rate of the sample. The results of Mehra and Prescott’s (2008) empirical study are consistent with our results, because the power utility CAPM did not satisfy our empirical tests. 4 Kan and Robotti (2007) Confidence Intervals The Law of One Price volatility bounds calculated in part 2 are subject to sampling variation. We have calculated point estimates of the volatility bounds, but we did not take into account that our results are based on a finite sample of Treasury Bill and market returns. To more accurately test whether the power utility CCAPM passes the LOP volatility bounds test, we need to identify the area in which the population volatility bound may lie. The area used is that between the upper and lower 95% confidence intervals for Hansen-Jagannathan volatility bounds obtained by Kan and Robotti (2007), shown in table 2. If the standard deviations of the CCAPM stochastic discount factors lie below that area for values of risk aversion between 1 and 20, then the power utility CCAPM model is to be rejected according to this test. Table 2 here Figure 3 here Figure 3 contains point estimates of the LOP volatility bounds, the standard deviations and means of the CCAPM stochastic discount factors for levels of risk aversion between 1 and 20 and the 95% confidence intervals for the volatility bounds. All of the standard deviations are below the area in between the upper and lower confidence intervals for the volatility bounds. This indicates that at a 95% certainty the CCAPM does not satisfy the LOP volatility bound test even when sampling errors are taken into account. Performance of Power Utility CCAPM In recent academic literature on the subject of asset pricing models a common formal method of evaluating model performance is to calculate the pricing errors on a set of test assets. In this report the test assets are the Treasury Bill and Market Index quarterly returns from Q1 1963 to Q4 2009. The pricing error is calculated as [pic] Where [pic], [pic] Treasury Bill and Market Index returns, and [pic] is the pri cing errors. Table 3 here For a model to correctly price an asset it would require that the pricing errors are as close to zero as possible since the pricing error is a measure of the distance between the model pricing kernel and the true pricing kernel. From Table 3 we can see that the pricing errors for the different values of risk aversion are not close to zero and the size of the errors actually increases with the level of risk aversion. We can also see that the Route Mean Square Pricing Error (RSME) which measures the average distance from zero of the pricing errors is not as close to zero as we would hope and also increases with the level of risk aversion. If we note the case for a risk aversion level of 20 then the RSME is 6. 76%, since this is quarterly data this works out to an annual RSME of approximately 27%. With such large pricing errors we would not expect this model to perform strongly. Hansen and Jagannathan (1997) found that for different levels of risk aversion the pricing errors do not vary greatly. As noted above, this is not the case in our sample in which the error increases with the level of risk aversion, thus creating an ever wider dispersion of pricing errors. This is counterintuitive to what we would usually assume as with increased levels of risk aversion the consumer is only willing to accept a certain level of return for lower and lower levels of risk, therefore we would expect at some point that the mean variance level would pass the volatility bounds and therefore correctly price the assets. Conforming with this report Cochrane and Hansen (1992) found that in order to satisfy the levels of variance necessary to surpass the volatility bounds a risk aversion level of at least 40 was necessary. It should be noted that in reality this is quite unreasonable and also that for this level of variance to be attained the expected return might also have to drop below the level necessary to surpass the volatility bounds. Table 4 here From Hansen and Jagannathan (1991) we know that in order to price a set of assets correctly the stochastic discount factor (SDF) should be close to one and have high levels of volatility. Table 4 shows that SDF’s at low levels of risk aversion are relatively close to one but have very low levels of volatility. When the level of risk aversion increases the SDF’s get further and further away from one yet the volatility also increases. Therefore it seems reasonable to conclude that we would not expect any of these SDF’s to price the assets correctly. The results illustrated above are consistent with the earlier analysis and point to the conclusion that the power utility CCAPM does not do a good job in pricing the two test assets and thus does not perform well in UK stock returns. Cochrane and Hansen (1992) agree with this conclusion but Kan and Robotti (2007) find the opposite. The reason for this could be the use of sampling error in the Kan and Robotti paper and the different data used the in the analysis. This report illustrates that there exists not only an equity premium puzzle but also a risk free rate puzzle. This risk free rate puzzle as noted by Weil (1989) states that if consumers are extremely risk averse, a result of the equity premium puzzle, then why is the risk free rate so low. Weil cites market imperfections and heterogeneity as the probable causes of this puzzle; however, this is not the explanation that Bansal and Yaron (2004) find. Using a model that accounts for investor reaction to news about growth rates and economic uncertainty they are able to go some way to resolving not only the risk free rate puzzle but also the equity risk premium puzzle. One method that could be used to improve the performance of the power utility CCAPM would be to construct the model using conditioning information; this would enlarge the possible payoff space available to investors. Kan and Robotti (2006) find that including conditioning information in models reduces the pricing errors by allowing the prices of volatility to move in line with the market. Although as Roussanov (2010) finds, conditioning information does not necessarily improve model performance and may actually exacerbate the problem. 6 Sampling Error in the Volatility Bounds When using the volatility bounds as specified by Hansen and Jagannathan (1991) to test asset pricing models we must be wary of sampling error in the bounds. As noted previously if a model does not lie within the Hansen and Jagannathan volatility bounds then we can conclude that it does not price the test assets correctly. However, Gregory and Smith (1992) and Burnside (1994) first noted that this test does not take into account significant sampling variation and could therefore reject models that price assets correctly. Burnside (1994) uses Monte-Carlo simulation to illustrate that over repeated samples if sampling error is ignored the volatility bounds test performs poorly. Gregory and Smith (1992) state that the sampling error could be due to large variability in the estimated bounds or the use of sample data in the analysis. Kan and Robotti (2007) derive the finite sample distribution of the Hansen and Jagannathan bounds in order to take account of this sampling error. They argue that confidence intervals that take into account the variation can be constructed and used to test asset pricing models. The importance of this new method of testing cannot be underestimated as it could affect the decision to reject an asset pricing model or not, this is best illustrated with reference to examples. Kan and Robotti test the equity premium puzzle using data from Shiller (1989) to show the implications of taking into account sampling error. Through constructing the 95% confidence intervals for the Hansen and Jagannathan volatility bounds they are able to show that the time-separable power utility model being tested may not be rejected at low levels of risk aversion. This is in stark contrast to the findings when sampling error is not taken into account where the model is strongly rejected except for unfeasible levels of risk aversion. From Figure 3, as noted earlier, even when sampling error is taken into account for the model tested in this report it does not fall within the volatility bounds. However, it does decreases the distance between the model and the volatility bounds which is the major consequence of the Kan and Robotti paper. This new method goes some way to solving the problem noted by Cecchetti, Lam, and Mark (1994) who found using classical hypothesis tests that the Hansen and Jagannathan bounds without sampling error rejected true models too often. Again, an extension here could be to use conditioning information to improve the volatility bounds by using the methods of Ferson and Siegel (2003) and as a result hopefully reduce the sampling error in the bounds. References Bansal, R. and A. Yaron, 2004, Risks for the long run: A potential resolution of asset pricing puzzles, Journal of Finance, American Finance Association, vol. 59(4), pages 1481-1509, 08. Burnside, C. , 1994, Hansen-Jagannathan Bounds as Classical Tests of Asset-Pricing Models,† Journal of Business Economic Statistics, American Statistical Association, vol. 12(1), pages 57-79 Cecchetti, S. G. , P. Lam, and N. C. Mark, 1994, Testing Volatility Restrictions on Intertemporal Marginal Rates of Substitution Implied by Euler Equations and Asset Returns, Journal of Finance, 49, 123–152. Cochrane, J. H. and L. P. Hansen, 1992, Asset Pricing Explorations for Macroeconomics, NBER Chapters, in: NBER Macroeconomics Annual 1992, Volume 7, pages 115-182 National Bureau of Economic Research, Inc. Dunn, K. , and K. Singleton, 1986, Modelling the term structure of interest rates under Non-separable utility and durability of goods, Journal of Financial Economics, 17, 1986, 27-55. Ferson, W. E. , and A. F. Siegel, 2003, Stochastic Discount Factor Bounds with Conditioning Information, Review of Financial studies, 16, 567–595. Gregory, A. W. and G. W Smith, 1992. Sampling variability in Hansen-Jagannathan bounds, Economics Letters, Elsevier, vol. 38(3), pages 263-267. Hansen, L. P. and R. Jagannathan, 1991, Implications of Security Market Data for Models of Dynamic Economies, Journal of Political Economy, Vol. 99, No. 2 (Apr. , 1991), pp. 225-262   Hansen, L. P. and R. Jagannathan, 1997. Assessing specification errors in stochastic discount factor models. Journal of Finance 52, 591-607. Kan, R. , and C. Robotti, 2007, The Exact Distribution of the Hansen-Jagannathan Bound. Working Paper, University of Toronto and Federal Reserve Bank of Atlanta. Mehra, R. , and E. C. Prescott, (1985), The equity premium: A puzzle, Journal of Monetary Economics 15, 145-161. Roussanov, N. , 2010, Composition of Wealth, Conditioning Information, and the Cross-Section of Stock Returns, NBER Working Papers 16073, National Bureau of Economic Research, Inc. Shiller, R. , 1982, Consumption, Asset Markets and Macroeconomic fluctuations, Carnegie–Rochester Conference Series on Public Policy, Vol. 17. North-Holland Publishing Co. , 1982, pp. 203–238. Shiller, R. J. , 1989, Market Volatility, MIT Press, Massachusetts. Journal of Economic Behavior Organization, Elsevier, vol. 16(3), pages 361-364. Weil, P. , 1989, The equity premium puzzle and the risk free rate puzzle, Journal of Monetary Economics 24. 401-422. Appendix [pic] Figure 1 LOP Volatility Bounds. The figure shows the LOP volatility bounds (dark blue line) which were found by using Treasury Bill and market returns as test assets. [pic] Figure 2 LOP Volatility Bounds with CCAPM. The figure shows the LOP volatility bounds (dark blue line) which were found by using Treasury Bill and market returns as test assets. It also shows the means and corresponding standard deviations of the CCAPM stochastic discount factors (green line) for values of risk aversion between 1 and 20. [pic] Figure 3 LOP Volatility Bounds with CCAPM and Confidence Intervals. The figure shows the LOP volatility bounds (dark blue line) which were found by using Treasury Bill and market returns as test assets. It also shows the means and corresponding standard deviations of the CCAPM stochastic discount factors (green line) for values of risk aversion between 1 and 20. The figure contains the confidence intervals, with a 95% level of confidence, estimated by Kan and Robotti (2007) for E(m) between 0. 97 and 1. 0082 for the Law of One Price volatility bounds for their first set of test assets. The light blue line shows the upper bounds of the confidence intervals and the red line shows the lower bounds of the confidence intervals. Table 1 CCAPM stochastic discount factors’ means and standard deviations and corresponding LOP volatility bounds CCAPM |LOP volatility bounds |CCAPM | | |means | |st. dev. | | |0. 985121 |0. 82806186 |0. 011749 | |0. 980404 |1. 2067111 |0. 023503 | |0. 975849 |1. 57451579 |0. 035275 | |0. 971456 |1. 93015539 |0. 04708 | |0. 967223 |2. 27320637 |0. 58934 | |0. 963151 |2. 60350158 |0. 070853 | |0. 959239 |2. 92096535 |0. 082854 | |0. 955486 |3. 22555764 |0. 0 94953 | |0. 951893 |3. 5172513 |0. 107169 | |0. 94846 |3. 7960217 |0. 11952 | |0. 945187 |4. 06184126 |0. 132027 | |0. 942074 |4. 31467648 |0. 14471 | |0. 939121 |4. 5448604 |0. 15759 | |0. 93633 |4. 7812196 |0. 17069 | |0. 933701 |4. 99481688 |0. 184033 | |0. 931234 |5. 19520693 |0. 197645 | |0. 928931 |5. 38230757 |0. 211552 | |0. 926792 |5. 55602479 |0. 225781 | |0. 92482 |5. 71625225 |0. 240361 | |0. 923016 |5. 8628708 |0. 255322 | This table shows the means of the CCAPM stochastic discount factors for levels of risk aversion between 0 and 20, the corresponding LOP volatility bounds and the standard deviations of the CCAPM stochastic discount factors. Table 2 95% confidence intervals for E(m) between 0. 97 and 1. 0082 E(m) Lower Upper 0. 9700 3. 1823 5. 2069 0. 9710 2. 9385 4. 8383 0. 9719 2. 7038 4. 4830 0. 9729 2. 4781 4. 1411 0. 9738 2. 2617 3. 8125 0. 9748 2. 0544 3. 4974 0. 9757 1. 8565 3. 1959 0. 9767 1. 6680 2. 9080 0. 9776 1. 4890 2. 6337 0. 9786 1. 3195 2. 3731 0. 9795 1. 1597 2. 1262 0. 805 1. 0097 1. 8931 0. 9815 0. 8696 1. 6739 0. 9824 0. 7394 1. 4685 0. 9834 0. 6194 1. 2770 0. 9843 0. 5096 1. 0993 0. 9853 0. 4101 0. 9356 0. 9863 0. 3212 0. 7857 0. 9873 0. 2429 0. 6497 0. 9882 0. 1755 0. 5275 0. 9892 0. 1190 0. 4192 0. 9902 0. 0736 0. 3248 0. 9912 0. 0393 0. 2445 0. 9922 0. 0160 0. 1784 0. 9931 0. 0030 0. 1275 0. 9941 0 0. 0938 0. 9951 0 NaN 0. 9961 0 0. 0938 0. 9971 0. 0029 0. 1279 0. 9981 0. 0159 0. 1798 0. 9991 0. 0395 0. 2474 1. 0001 0. 0745 0. 3302 1. 0011 0. 1212 0. 280 1. 0021 0. 1796 0. 5408 1. 0031 0. 2498 0. 6689 1. 0041 0. 3317 0. 8123 1. 0051 0. 4255 0. 9714 1. 0061 0. 5309 1. 1461 1. 0072 0. 6481 1. 3368 1. 0082 0. 7769 1. 5437 This table shows the upper and lower bounds of the 95% confidence intervals Kan and Robotti (2007) calculated for the volatility bounds for their first set of test assets. The confidence intervals presented are for values of E(m) between 0. 97 and 1. 0082. Table 3 Pricing errors for the Treasury Bill (Rf) and the value weighted UK market index (Rm), and the Root Mean Square Pricing Error (RSME) for each level of risk aversion Level of Risk Aversion |Error Rf |Error Rm |RSME | |1 |-0. 0104 |0. 0047 |0. 0080 | |2 |-0. 0152 |-0. 0001 |0. 0107 | |3 |-0. 0199 |-0. 0049 |0. 0144 | |4 |-0. 0244 |-0. 0094 |0. 0184 | |5 |-0. 287 |-0. 0138 |0. 0225 | |6 |-0. 0329 |-0. 0180 |0. 0265 | |7 |-0. 0369 |-0. 0221 |0. 0304 | |8 |-0. 0408 |-0. 0260 |0. 0342 | |9 |-0. 0445 |-0. 0297 |0. 0378 | |10 |-0. 0480 |-0. 0333 |0. 413 | |11 |-0. 0514 |-0. 0367 |0. 0446 | |12 |-0. 0546 |-0. 0399 |0. 0478 | |13 |-0. 0577 |-0. 0430 |0. 0508 | |14 |-0. 0606 |-0. 0459 |0. 0537 | |15 |-0. 0634 |-0. 0487 |0. 0564 | |16 |-0. 660 |-0. 0513 |0. 0590 | |17 |-0. 0684 |-0. 0537 |0. 0614 | |18 |-0. 0706 |-0. 0560 |0. 0636 | |19 |-0. 0727 |-0. 0580 |0. 0657 | |20 |-0. 0747 |-0. 0600 |0. 0676 | | | | | | The pricing errors above are calculated as [pic], where [pic], [pic] Treasury Bill and Market Index returns, and [pic] is the pricing errors. The RSME is simply the average pricing error of the stochastic discount factor for each level of risk aversion. Table 4 Summary Statistics for power utility CCAPM stochastic discount factor |Level of Risk Aversion |Average |St Dev |Min |Max | |1 |0. 9851 |0. 0117 |0. 9551 |1. 0436 | |2 |0. 804 |0. 0235 |0. 9214 |1. 1000 | |3 |0. 9758 |0. 0353 |0. 8889 |1. 1595 | |4 |0. 9715 |0. 0471 |0. 8575 |1. 2223 | |5 |0. 9672 |0. 0589 |0. 8273 |1. 2884 | |6 |0. 9632 |0. 0709 |0. 7981 |1. 3581 | |7 |0. 592 |0. 0829 |0. 7699 |1. 4316 | |8 |0. 9555 |0. 0950 |0. 7428 |1. 5090 | |9 |0. 9519 |0. 1072 |0. 7166 |1. 5906 | |10 |0. 9485 |0. 1195 |0. 6913 |1. 6767 | |11 |0. 9452 |0. 1320 |0. 6669 |1. 7674 | |12 |0. 421 |0. 1447 |0. 6434 |1. 8630 | |13 |0. 9391 |0. 1576 |0. 6207 |1. 9638 | |14 |0. 9363 |0. 1707 |0. 5988 |2. 0701 | |15 |0. 9337 |0. 1840 |0. 5777 |2. 18 21 | |16 |0. 9312 |0. 1976 |0. 5573 |2. 3001 | |17 |0. 9289 |0. 116 |0. 5377 |2. 4245 | |18 |0. 9268 |0. 2258 |0. 5187 |2. 5557 | |19 |0. 9248 |0. 2404 |0. 5004 |2. 6940 | |20 |0. 9230 |0. 2553 |0. 4827 |2. 8397 | This table shows the average value, standard deviation, minimum and maximum for the stochastic discount factor at each level of risk aversion. ———————– 24th November 2011 How to cite Power Utility Consumption Capm in Uk Stock Markets, Papers

Hospital Sleeping free essay sample

Even today I study Douglass sleeping face. Eleven years old, sleeping late onSaturday mornings, he rarely sleeps in his own room with its crooked Snoopydecorations. A boy could live forever with the Peanuts gang. Ive watched himsleep since he was six months old after his lengthy hospital stay fordehydration. The way ghosts float around in hospital air, with itsheavy and sanitary stench, has always amazed me. I have not always seen thephantoms: When Doug was admitted, my only frame of reference of hospitals hadbeen visits for routine physicals. I thought the doctors office was pee ina cup and a finger pin-pricked. These illusions were swept away, andreplaced with the notion that dreams are merely a function of something greater:sleep. After three days in the hospital, Douglas was doing worse. Thedoctors had said he would be in and out of the hospital, but now hewouldnt accept food and had to be sedated and fed intravenously. We will write a custom essay sample on Hospital Sleeping or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page In hisroom, my brother lay still in a crib, mobile overhead. I stared at him frombehind my parents. He looked like an albino imp, half-naked and sleeping. The IVdangled above his head, entering his arm. So that he would not remove the needle,his arm had been bound to the crib. In his sleep, he shook a bit, spasming as onedoes after a long time outside in the snow without mittens. Close to the door, aTV hung from the ceiling. Later that night, my father would watch CNN, and thenight after my mom would watch the local news. They took turns staying to watchthe baby sleep. After a week, the doctor finally released my brother. Hehad to rest for the first few days home, an order I found irritating: if he weretruly better, shouldnt he be able to act his usual self? Somehow, Iloved him more after his rehabilitation. While he slept, I would peek at him. Itmust have been strange that my youngest brother was the focus of so much of myattention, but I hardly cared. When my brother sleeps, he always makes this face:His eyes are loosely shut, as to make the eyelids nearly translucent. His mouthinvariably remains half-open with his top row of teeth pensively suspendedmillimeters above the bottom lip. It looks as if he is in heated conversation,waiting patiently for a break in the dialogue so he can explode the revelationthat rocks nervously on his pursed lips. My brother goes to therapy for anauditory processing problem. I frequently become frustrated with his inability toarticulate: Doug, where is Mom? No response; he sits andstares as if in suspended animation, his head tilted slightly to theright. Doug, where is Mom? Where did she go? Noresponse. Douglas! What? I couldnt hearyou! he awakens. Have you been listening? Perhapsit is only when he is asleep that this impediment washes away, and he listens sowell that his discourse develops at the utterance of a syllable. One day, I wantto feel that I live on the brink of explosion: I will be so involved in thedialogue of the world that I will be incapable of restraining myself. Ioften fear that when Douglas grows up, no one will understand when hes havingtrouble listening. I wonder what he will be like, and what hell be. The acuityand compass of his memory are so well developed, but where will that take him? Heonce memorized a book on dogs and could recite the average weight and lifeexpectancy of any breed after only a moments hesitation. Douglas whats a French Bulldog? Years orsize? Size. Um, 14 to 16pounds Sometimes I wonder so much about my brother and his futurethat I want to throw up my hands and yield to destiny. I want to beg the fates togive me the answer, to please let me stop guessing. I know now how awesome andfrightening uncertainty is, but Ive learned so much from watching him sleep. Iknow now the importance of living in the moment and actively participating in thepresent. The rest of lifes demands hardly matter. Right now I need only bepatient, and watch Douglas sleep.

The Physical Development Of A Human Being free essay sample

Essay, Research Paper Catherine The Physical Development of a Human Being As defined in Berk # 8217 ; s, Development Through the Lifespan, physical development is alterations in organic structure size, proportions, visual aspect, and the operation of assorted organic structure systems ; encephalon development ; perceptual and motor capacities ; and physical wellness. The physical development of a human being is the alone because of all of the seeable alterations that every homo being goes through. Physical growing consequences from a uninterrupted and complex interplay between heredity and environment. Humans begin to develop before they are out of their female parent # 8217 ; s uterus. After construct, the fertilized ovum is what I consider the earliest development of the human being. The period of the fertilized ovum is approximately two hebdomads long. The fertilized ovum so becomes an embryo. The period of the embryo lasts from the 2nd hebdomad on through the 8th hebdomad of gestation. The embryo so changes to organize the foetus. We will write a custom essay sample on The Physical Development Of A Human Being or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page The period of the foetus is from the 9th hebdomad until the terminal of the gestation. The fertilized ovum, embryo, and fetus all signifier in the first trimester or gestation. By the 3rd trimester, the foetus is about seven and half lbs. I weighed approximately eight and one 4th lbs when I was born. My female parent still tells me that when I was born I had plenty hair to plait. She says all my hair gave her atrocious pyrosis. Fortunately she did non smoke or utilize any drugs or intoxicant during the clip that she was pregnant with me. All of these things can make serious injury to the foetus. Boys tend to be a small longer and heavier than misss at birth. Babies change faster than older worlds do. The human organic structure grows and enlarges at the most rapid rate during the first two old ages. Weight is gained steadily during this clip. By nine months baby fat has normally arrived and is at its highest degree. This helps babes to keep a changeless organic structure temperature. Babies become dilutant during the 2nd twelvemonth. This held true for me from what I have seen of my babe images. Babies do non be given to be really muscular or coordinated. The kid grows and size additions, and different parts of the organic structure grow at different velocities. There are two growing forms that represent this. The first is called the cephalocaudal tendency. During this stage the caput takes up a 4th of the organic structure and the legs take up a 3rd. The 2nd form is called the proximodistal tendency. This is when the growing returns from the centre of the organic structure outward. During babyhood, the weaponries and legs continue to turn in front of the custodies and pess. The encephalon is closer to adult size at birth than any other organic structure portion on a babe. When a kid reaches the age of two, the encephalon is already at 70 per centum of its grownup weight. Some of the factors that influence this early growing are heredity, nutrition, and emotional wellbeing. Over the first twelvemonth of life, babes begin to form sounds into complex forms. During the 2nd half of the first twelvemonth, babes begin concentrating on larger address units. These larger units are critical to calculating out the significance of what babes hear. By nine months babes begin to listen to speech for much longer periods of clip, and they begin to comprehend it on wordlike sections. A kid? s vision goes through some extended alterations during the first seven to eight months of the kid? s life. The kid? s vision improves a great trade throughout the first twelvemonth. When a kid begins some signifier of independent motion, they begin to better understand depth perceptual experience. When an grownup moves about on his/her ain, they excessively have a better feel for landmarks and what is around them. The rapid growing in organic structure size that takes topographic point in babyhood Begins to decelerate down in early childhood. During this clip boys still be given to be a small larger than misss are. When I was this age, all of my friends were male childs, and they were a small larger than I was. Increasing control of the kid? s custodies and fingers lead to a immense betterment in the all right motor accomplishments. Their drawings become more and more complex during this clip. I have some illustrations of images that I drew when I was younger, and as my age increased, my drawings became better and better. The skeleton continues to alter throughout early childhood. Near the terminal of the preschool old ages, a kid begins to lose their babe dentitions. I lost my two front dentitions foremost. I have many images without any front dentition. I lost the bottom forepart dentition after this. The dentition that grew back in their topographic point were larger and had a ridged unders ide. They called me Snaggle tooth. Physical development in in-between childhood is an extension of the slow growing form that takes topographic point in early childhood. By age six, an mean kid weighs about 45 lbs and is about three and a half pess tall. On norm, kids tend to add two or three inches to their tallness. In comparing, they gain about five lbs a twelvemonth. From the ages of six to eight, misss are still lighter and shorter than male childs are. During this clip, the lower part of their organic structure is turning the fastest. Besides during the in-between childhood old ages, the castanetss begin to lengthen and broaden. Ligaments are non attached tightly to the castanetss yet. Because of this, kids of this age experience unusual flexibleness. At this age, I was able to set both of my legs around my cervix because I was so flexible. I began gymnastic exercises around this age besides. I began to go interested in many athleticss, and my flexibleness along with my strenuosity supported my public presentati on in many athleticss. Children during this clip be given to see things like bedwetting. I neer was a bed wetter, but my brother went through this phase. Besides during the in-between childhood ages there is a higher rate of unwellness in the first two old ages of simple school. This is chiefly because the immune system is non yet to the full developed s good as the changeless exposure to vomit kids. Along with frequent unwellness, kids of this age are frequently non physically fit. Physical fittingness plans can assist this job. Probably one of the most hard passages that worlds go through physically is puberty. Puberty is the clip when the organic structure of a school-aged kid turns into that of an grownup. This growing is regulated by endocrines that are genetically influenced. Girls normally reach pubescence an norm of two old ages before male childs. During this clip, utmost weight additions are common, and the skeleton begins to maturate. Sex endocrines control sexual ripening. Testosterone in boys brings about their facial hair and musculus growing. It besides leads to muscle growing. The female variety meats release estrogen, and it causes chests, womb, and the vagina to maturate. The two chief classs that the alterations puberty brings about can be divided into are overall organic structure growing and the ripening of sex features. The first seeable mark of pubescence is normally a big growing jet. A growing jet is the rapid addition in tallness and weight. For misss, this growing spurt normally takes topographic point around or after age 10 and for male childs it normally takes topographic point around 12 and a half. Remember when I started to hit pubescence, I was all confused about what was go oning because my organic structure was altering so rapidly. I think it was about 4th class when I started traveling through pubescence. I was sort of chubby. Girls are normally taller and heavier than male childs are during early adolescence. Most misss are wholly grown by age 16. I was full grown by the age of 15. Boys normally are finished turning by the age of 17 and a half. I have known male childs, nevertheless, that have grown after the age of 17. In entire, striplings add about 10 inches in tallness and around 40 lbs in weight. Adolescence is a clip when worlds feel and look awkward because of long legs and elep hantine pess and custodies. There are many major pubescence alterations that take topographic point. In misss, their chests begin to bud at age 10, and so their tallness jet begins. Then their pubic hair appears, followed by the extremum of their strengt H jet and height jet. Next their first menses occurs. By this clip, they have achieved big stature. By 14, misss? chest growing is completed and at 14 and a half, their pubic hair growing is completed. Boys major pubescence alterations occur a little later than misss. First, their testicles begin to enlarge at the age of 11 and a half. Pubic hair appears and the phallus begins to enlarge at 12, and so the tallness jet begins. Their spermarche occurs at 13. At the age of 14, they reach the extremum of their tallness jet, their facial hair begins to turn, and their voice begins to acquire deeper. After this, the growing of their phallus is complete. Then, at 15 their pubic hair growing is complete and the extremum of their strength jet has occurred. Finally, after all of these alterations, grownup stature is reached. The most noticeable portion of male pubescence seems to be the voice as it cracks a batch and the facial hair. For me the most noticeable portion of my pubescence was the growing of my thorax and the reaching of my catamenial rhythm. My face seemed to go greasy during this clip every bit good. This was non a fun portion of turning up. Once once more, as with all the phas es of physical development, nutrition and heredity have an impact on pubescence, but they chiefly merely trade with the timing. This age spread is characteristic of the rep lacement of babe dentitions with lasting 1s. Writing becomes more legible during this clip every bit good. Organized games with regulations begin to go more and more common at this clip every bit good. Puberty is the most distinguishable portion of human physical development. After pubescence, it seems like there are no more major physical alterations that occur. I do non truly experience like I have changed that much since I was 16. I weigh more, and I seem to be less flexible, but those are both things relative to day-to-day activity. Early on maturity is the following portion of human physical development. Throughout the mid-twentiess and mid-thirtiess, early maturity development occurs. During this clip, physical alterations and the diminutions in the operation of the organic structure are so gradual that one can barely even detect them. When an grownup reaches the age 30, vision starts to worsen because the lens stiffens and thickens, and they can no longer see things up near as good. Some of the variety meats begin to diminish in map as good. When exerting, the respiratory volume lessenings and the external respiration rate additions with age. The sum of air that can be forced in and out of the lungs decreases up to fifty per centum from the age of 20 to the age of 50. For grounds such as this, athletic public presentation extremums from the mid-twentiess to mid-thirtiess and so begins to diminish. The research the have done on unbelievable jocks show us that the upper biological bound of motor capacity is rea ched by the first portion of early maturity. The diminutions in bosom and lungpower normally show up merely during exercising. From the ages of 20 to thirty, the snap of the tegument begins and continues, and weight additions get down and go on through in-between maturity. Sexual activity additions during this clip every bit good. Aging is more utmost for those who lead less active lives. Besides, the generative capacity tends to worsen with age. For adult females it begins to worsen particularly after the age of 30 five, and for work forces it starts to worsen after the age of 40. From 30 to forty old ages of age, vision diminutions along with hearing and the skeletal system. Women get down to see jobs with birthrate, the hair begins to turn grey and thin out, and sexual activity lessenings. Middle maturity is the following age group where important alterations occur physically in human existences. It is simply a continuance of the alterations that begin in early maturity. This is the clip when vision and hearing truly worsen a noticeable sum. Humans in this age group lose ability to see every bit good in dim visible radiation, and seem to ever necessitate a brace of bifocals. Hearing AIDSs seem to get down looking a small spot at this clip in some people as good. Men? s hearing diminutions much faster than adult females? s. I notice more old work forces that can? t hear than I do old adult females. The cuticle becomes non as house and consequences in wrinkling of the tegument. Skin spots become prevalent around the age of 50. Gloat? s pess are among the first major furrows to look. Massive Sun exposure earlier in life tends to do the tegument? s pursing a faster procedure, and smoking seems to hold a similar consequence as good. Muscle aggregate start to worsen a batch in the mid-fortiess as good, and at the same clip, fat is put on at a more rapid rate. Around this clip, adult females begin to see climacteric, which is the terminal to the generative capacity. There is no male opposite number for climacteric, but work forces do see some diminution in their sperm count and seeds. The mineral capacity in the castanetss begins to worsen, even though the castanetss are broadening. The loss of bone mass and bone strength causes tallness to diminish in some people. From the ages of 50 to sixty, the human oculus no longer has its accommodating ability at all. Hearing loss at this clip extends to all frequences. It remains merely for the highest tones. Skin of class, keeps on acquiring furrows and age musca volitanss are more common. Menopause is the major generative alteration that adult females undergo during this clip. Height drops because of lower bone mass, which continues to worsen. Late maturity seems to be the clip when everything declines physically. When I think about old people, the two words that come to my head are furrows and saggy. From the ages of 60 to eighty, many things are deteriorating physically. Neurons get down deceasing at rapid rates. Vision continues to worsen, and people at this age have a harder clip separating colourss. Besides, depth perceptual experience and sensitiveness to glower worsen. This age is the hearing assistance age. Hearing continues to worsen throughout the frequence scope. Sometimes sense of odor lessens every bit good. Loss of touch in fingertips is common. The emphasis during exercising becomes greater because of the continued diminution in respiratory and lung maps. Hazard of unwellness is much greater in this late phase of life because the aged immune system. This age group besides tends to see some trouble in traveling to kip at dark ; this occurs in work forces more so than adult females. Hair evidently continues to thin and grey and wrinkles become much worse and clamber droops and is thinner. Because of the fact that the organic structure is losing thin musculus mass during this clip, tallness and weight continue to decrease. This leads to increased hazard of osteoporosis. Sexual activity becomes non as intense. It seems like every portion of the human organic structure lessons or worsens after a individual reaches tardily maturity. Once a human reaches 80 or older, everything described antecedently merely continues and gets worse. Mobility, nevertheless, goes off, and it gets harder to acquire about. This is because of the diminishing musculus and bone strength. Wheel chairs and Walkers and canes are used as an assistance in motion when mobility no longer exists. Death comes after or even during this phase. There are three ways that decease occurs by and large. The first manner is the agonal stage. During this stage, pant and musculus cramps go on when the organic structure can no longer prolong life. The 2nd stage is clinical decease. This is when a short interval follows in which pulse, circulation, external respiration, and the encephalon maps all halt. Resuscitation is still available at this phase. In the 3rd phase of decease, mortality, the individual really passes into lasting decease. Soon subsequently, this dead individual becomes shriveled looking, and has therefore undergone all of the physical development a individual goes through in a life-time. Physical development is the lone type of development that one can really witness. Ever individual by and large goes through some signifier of the same development through each age group. The human organic structure is really alone and all of these alterations that we experience are what makes us so interesting.

Business intelligence (BI)and business alignment, maturity and appropriate organisational diffusion models The WritePass Journal

Business intelligence (BI)and business alignment, maturity and appropriate organisational diffusion models Background Business intelligence (BI)and business alignment, maturity and appropriate organisational diffusion models BackgroundProposed Null and Alternative HypothesesMethodological Approach Research Design ApproachResearcher’s Previous Related Work Significance of this Study ConclusionRelated Background Companies invest millions of dollars in business intelligence (BI) systems to gain useful BI data that helps leaders/managers to make management decisions and build predictive models of what may or may not happen in order for the company to gain a greater competitive advantage. Implementing such systems is no easy task (CGI Group Inc. 2004; Hedgebeth 2007). Studies have shown that there are a limited number of factors that make or break the success of BI within the organisation (Yeoh and Koronios 2020). To analyse the process more closely and ensure an organisation gets the greatest return on investment (ROI) and on the limited resources available, this study will contribute to these studies through highlighting the success factors and barriers faced by businesses. It attempts to provide better understanding for BI systems adoption, as well as the challenges when BI diffusion does not go well. Proposed Null and Alternative Hypotheses This proposal presents a quasi-experimental, time-study methodology to confirm the diffusion model and maximise successful implementation of a BI system as measured by return on investment (ROI). H01 –Businesses do not put more emphasis on accurate and timely data to make operational decisions to gain a competitive advantage. HA1 -Businesses do put more emphasis on accurate and timely data to make operational decisions to gain a competitive advantage. H02. The factors affecting business intelligence implementation do not affect managers’ business decision-making ability.   HA2. The factors affecting business intelligence implementation affect managers’ business decision-making ability.   H03.   Change management skills are not required by users adopting a new business intelligence system as measured by increased utilisation of BI system by the user. HA3 Change management skills are required by users adopting a new business intelligence system as measured by increased utilisation of BI system by the user. H04.   Human resource management skills are not required by users adopting a new business intelligence system as measured by increased utilisation of BI system by the user. HA4 Human resource management skills are required by users adopting a new business intelligence system as measured by increased utilisation of BI system by the user. H05 A model for implementation of BI is not needed for smooth diffusion and maximum results of the BI system as measured by ROI.   HA5 A model for implantation of BI is needed for smooth diffusion and maximum results of the BI system as measured by ROI. Methodological Approach Research Design Approach The conceptual framework for this study is utilising the positivist assumptions to determine as much as clear cause and effect as possible for a smooth diffusion process. The emphasis throughout will lead to a clearer understanding of the cause of successful BI system implementations and the effect of successful and unsuccessful BI implementations (Swanson and Holton 2005). The cause and effect will also be studied regarding use of a framework/model during a BI implementation. To get to the primary factors, the positivist approach is the proper choice to. If this factor is present (x) then a smooth BI diffusion will take place (y) and the organisation will see improved results and increase its’ competitive position. This cause and effect, positivist framework will lead to a quantitative, longitudinal study. A quasi-experimental, time series design will be employed in this study which will be conducted over several years. This will be done to assess the progress of business results and BI data results as an outcome from the BI model diffusion, i.e. â€Å"to determine the influence of a variable or treatment on a single sample group† (Swanson Holton, 2005, p. 91). The data analysis is measured before and after the intervention or treatment. In this case, that would be the BI model of diffusion with changes as a result of the literature analysis. A limitation to the time series is that it is difficult for the researcher to â€Å"separate out interaction effects out of the organisation or (some) other environmental reason† (Swanson Holton, 2005, p. 91). This correlates to the literature as this is the area that has not been studied for a great length of time. This study will be conducted over a 3-year period as the longitudinal method gathers measurements over an extended period of time so as to be able to study long-term ROI with the main concern being dealing with the attrition factor which makes such studies more expensive. Opinions will be elicited from IT executives representing two types of companies: those which adopted BI systems and those which have had problems doing so or have not done so yet. Two questionnaires will be used, one for each type of organisations. The first questionnaire will be organised into four sections: general data; BI success measures; factors for BI adoption; and challenges to BI adoption. Previous surveys will be used to create the survey tool and modified to fit the context (Kamhawi 2008). The second questionnaire will be organised into four sections as well: general data; reasons for problems with implementing BI systems; attitudes; and future intentions towards BI systems. Measurement items for such se ctions will be developed using previous studies (Kamhawi 2008). The questionnaires will be field tested and pilot tested to ensure validity and reliability. Based on the pre-test and pilot test minor changes may be made to the phrases used in the scale questions of the questionnaire, how worded, and how questions will be classified and may be made clearer. Responses to the phrases of both questionnaires will be given through a 5 point-Likert scale. Researcher’s Previous Related Work This researcher has been involved with business and information technology alignment since the mid-1990s which is the area which this research falls into. After having returned from academic studies abroad to southern Africa the researcher was confronted with the task of having to evaluate projects from international donor organisations such as German Technical Cooperation (GTZ), projects that would otherwise be classified as white elephants because the technology used was too advanced for a developing country. This resulted in this researcher submitting an MBA project to Nottingham Trent University in 1999 entitled â€Å".† This area of interest was further enhanced as the researcher was a lecturer on the MBA programme of the University of East London responsible for a course on â€Å"Organisational Change and Business Processes.† As the researcher has been working with SAP BI for a few years the implementation of such complex system has further enhanced the interest i n researching in this area, defining the linkage between business and technology in BI. Though the writer has not come up with any publications due to the nature of his work, this research would correct that and lead to publications of the researcher’s expertise. Significance of this Study The real world of the organization change management can prove challenging and this study could contribute significantly to the existing literature by filling gaps within existing studies and creating new knowledge. As well done as the qualitative study was by Yeoh and Koronios (2010) their model does not account specifically for the effects of human resource management or for the unexpected. The critical and unique elements that this study will be provide: Conclusion Conclusions and perspectives from a global standpoint as a result of retrieving samples from locations worldwide as opposed to one-country focused literature review. This study will be a quasi-experimental, time-series study as a result of the literature review where previous studies have been from one point in time. Create a weighting system for the success factors to determine which one has the most impact on a successful diffusion process. This will be helpful for organisations that have limited time and resources when implementing the BI into the company. Determining the position of change management and human resource management in the BI diffusion model. Many of the model or studies reference training for users as an example, or change management in broad terms, but few are specific about the position of change management in the model or the position of the needs of the people during the transition.

Life In A Temperate Grassland

Life In A Temperate Grassland As much as one-fifth of the Earths surface is covered in wild grasses in biomes known, aptly, as grasslands. These biomes are characterized by the plants that grow there, but they also attract a unique array of animals into their realm.   Savannas and Grasslands: Whats the difference? Both are dominated by grass and few trees as well as hooved animals that can run fast from predators, so whats the difference between a grassland and a savanna? Essentially a savanna is one type of grassland found in tropical regions. It generally gets more moisture and therefore has a few more trees than grasslands in the rest of the world. The other type of grassland - known more simply as a temperate grassland - experiences seasonal changes throughout the year that bring hot summers and cold winters. Temperate grasslands receive just enough moisture to support the growth of grasses, flowers, and herbs, but not much else. This article will focus on the plants, animals, and regions of the worlds temperate grassland biomes. Where in the World Are Grasslands Found? Temperate grasslands are characterized by their  hot summers, cold winters, and very rich soils. They  can be found throughout North America - from Canadas prairies to the plains of the midwestern United States. They are also found in other parts of the world, albeit they are known here under different names. In South America, grasslands are called pampas, in Hungary they are called pusztas, whereas in Eurasia they are known as steppes. Temperate grasslands found in South Africa are called veldts. Plants in the Grassland: More than just grass! As you might expect, grasses are the predominant plant species growing in grasslands. Grasses, such as barley, buffalo grass, pampas grass, purple needlegrass, foxtail, rye grass, wild oats, and wheat are the main plants that grow in these ecosystems. The amount of annual rainfall affects the height of the grasses that grow in temperate grasslands, with taller grasses growing in wetter areas. But thats all there is to these rich and fertile ecosystems. Flowers, such as sunflowers, goldenrods, clover, wild indigos, asters, and blazing stars make their home among those grasses, as do several species of herbs. Precipitation in grassland biomes is often high enough to support grasses and a few small trees, but for the most part trees are rare. Fires and erratic climate generally prevent trees and forests from taking over. With so much of a grass growth occurring underground or low to the ground, they are able to survive and recover from fires more quickly than shrubs and trees. Also, the soils in grasslands, while fertile, are typically thin and dry, making it difficult for trees to survive. Temperate Grassland Animals There are not many places for prey animals to hide from predators in grasslands. Unlike savannas, where there is a large diversity of animals present, temperate grasslands are generally dominated by just a few species of herbivores such as  bison, rabbits, deer, antelope, gophers, prairie dogs,and antelopes. Since there are not many places to hide in all of that grass, some grassland species - such as mice, prairie dogs, and gophers have adapted by digging burrows to hide from predators such as coyotes and foxes.  Birds such as eagles, hawks, and owls also find lots of easy prey in grasslands. Spiders and insects, namely grasshoppers, butterflies, crickets, and dung beetles are in abundance in temperate grasslands as are several snake species. Threats to Grasslands The primary threat faces by grassland ecosystems is the destruction of their habitat for agricultural use. Thanks to their rich soils, temperate grasslands are frequently converted to farm land. Agricultural crops, such as corn, wheat, and other grains grow well in grassland soils and climate. And domestic animals, such as sheep and cattle, love to graze there. But this destroys the delicate balance of the ecosystem and removes the habitat for the animals and other plants that call the temperate grasslands their home. Finding land to grow crops and support farm animals is important, but so are grasslands, and the plants and animals that live there.

What is the most important skill (culinary arts) Essay

What is the most important skill (culinary arts) - Essay Example The chefs should ensure that the food particles spill on their clothing. A person’s outward appearance depicts more of the individual behavior. The second skill is respect. The chefs respect their fellow workmates as well as the working environment and the equipment. Respect entails keeping clean the workspace and the equipment after use. It also ensures that people learn how to economize on the ingredients in the kitchen and the industries as well. People should also respect the natural environment that gave rise to the inputs they process in to get output. Third, people should learn how to manage wastes. The chefs Alison Cayne observed learnt how to dispose of their wastes. They always ensure to use the entire ingredients so as to reduce the amount of waste to a minimum. The chefs also appreciate how expensive the ingredients were and, therefore, minimize the amount of waste to maintain the economic feasibility of their cooking practices. Fourth, the chefs exhibit a sense of appreciation of learning. The chefs learn from the real-time environment and apply the skills to build on the technical skills in the kitchen. In the kitchen, each chef teaches and learns from the other chefs. Applied to the industry, and classroom settings, employees, and the students continuously learn from other members. The fifth skill discussed is the appreciation of the process. The learning process proceeds systematically. The learners begin from a particular point and builds on their career as they continue along with their profession. It requires hard work to obtain the required skill to perform a specific task (Cayne 1). Those who successfully master the skills get promotion and eventually become the CEOs of the institution. She, however, says that the skill is not prevalent in the restaurant community. The sixth skill is the ability of preparedness. The skill is similar to respect and neatness. The ability ensures that the producers of a particular commodity strive to