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XII Mostly down his parcel of Rolos, Robbie turned out to be incredibly parched. Krystal had not gotten him a beverage. He moved off the ...

Wednesday, December 25, 2019

The Data and Methodology used in the Hang Seng Index - Free Essay Example

Sample details Pages: 9 Words: 2665 Downloads: 10 Date added: 2017/06/26 Category Finance Essay Type Research paper Did you like this example? In this chapter, there is description of data used in this study, which are The Hang Seng Index (HSI), The Hongkong and Shanghai Banking Corporation Holding plc (HSBC Hldgs) and Oriental Press Group Limited. After that, methodologies of predictions based on past volatilities and GARCH class conditional volatility models are presented. Finally, I will talk about how to evaluate the forecasting performances in according to select the best models. Now, let me introduce the data analyzed in my study. 3.1 Data description 3.1.1 The Hang Seng Index (HSI) Global investors treat the Hang Seng Index (HSI) as an indicator of the performance of the stock market in Hong Kong. The HSI was publicly launched on 24 November 1969 and is one of the earliest stock market indexes in Hong Kong. It is now maintained by Hang Seng Indexes Company Limited, which is a subsidiary of Hang Seng Bank, one of the constituent companies. The HSI is a freefloat-adjusted market capitalization weighted stock market index. : The current price at Day t : The closing price at Day t-1 IS : The number of issued shares FAF : Freefloat-adjusted Factor, which is between 0 and 1 CF: Cap factor, which is between 0 and 1 It consists of 43 constituent companies which represent around 60% of the total market capitalization of the Hong Kong Stock Exchange (HKEX). In order to show the price movements of the major sectors of the market clearer, the HSI constituent stocks are grouped into four sub-indexes: Properties, Utilities, Commerce and Industry, and Fina nce. Normally, qualified potential constituents are companies with a primary listing on the Main Board of the Hong Kong Stock Exchange (HKEX). In recent years, more and more mainland China companies listed on the Hong Kong Stock Exchange (HKEX), they can also be one of the qualified potential constituents if they meet several conditions. The companys total ordinary share capital must be in the form of H shares, the shares of the company incorporated in mainland China that are traded on the Hong Kong Stock Exchange. And also, the company needs to complete the process of Share Reform first. As a result, the company has no unlisted share capital. As the market capitalization, the turnover ranking and the financial performances of the companies may change in different periods, the list of constituent stocks is reviewed quarterly. In this paper, the daily closing Hang Seng Index and the weekly Hang Seng Index are used. The data set ranging from 1 July 1997 to 30 June 2008 is retrie ved from Datastream, a U.K. incorporated data service company. The data set is partitioned into the in-sample estimation periods 1997 2007, the out-of-sample forecast periods 2007 2008. This separation provides 2467 and 522 in-sample observations for the daily series and weekly series respectively. For out-of-sample observations, there are 246 for daily series and 52 for weekly series. In order to obtain more accurate results, public holidays and special incidents leading the public announcement of non-trading, such as the black signal of the Rainstorm Warning System and no. 8 storm force wind signal of Hong Kongs Tropical Cyclone Warning Signals. During this period, there were quite a lot of economic events affecting the stock market in Hong Kong. Hong Kong is one of the victims of 1997 Asian Financial Crisis. After the HSI peaked at 16820, it was attacked by international speculators, leading to a 50% plunge. In 1998, the Hong Kong government intervention on stock market to p urchase component shares supported the market and the HSI rebounded to 18000. The rise in both the interest rate and the crude oil price burst the dot-com bubble in 2000. The HSI dropped to 14000 and then fluctuated between 14000 and 16000. 911 Incident occurred in 2001. The HSI kept falling and the minimum was 8894. The stock market started to recover after Mainland and Hong Kong Closer Economic Partnership Arrangement (CEPA) was signed on 29 June 2003. In August 2007, mainland China declared the plan to allow part of citizens to invest on Hong Kong market directly. In October, stimulated by the news of AH stock hedging program, the HSI break through 30000. Unfortunately, the HSI was affected by the fallen chain of major stock markets in the world and dropped 2061.23 on 22 January 2008. During this period, the average point of the HSI is 14313.39 and the stand deviation is 4563.68. The lowest point in these eleven years was 6660.42 which occurred on 13 August 1998. 31638.22 was the highest point and occurred on 30 October 2007. Don’t waste time! Our writers will create an original "The Data and Methodology used in the Hang Seng Index" essay for you Create order 3.1.2 HSBC hldgs Besides the HSI, one of the constituent stocks is randomly selected. At the beginning of the selection process, each constituent stock is assigned a number, 1 43. Then, a number, between 1 43, is randomly generated by Excel. If the assigned number of the company matches the generated number, that company will be selected and studied in this paper. The Hongkong and Shanghai Banking Corporation Holding plc (HSBC Hldgs) is the selected constituent company and belongs to the Finance Sub-indexes. HSBC hldgs is one of the most favourite stocks in Hong Kong. In 1865, it was founded in Hong Kong in March and in Shanghai one month later. The banks headquarter was in Hong Kong until 1992. Then it moved to London as a condition of the acquisition of Midland Bank in UK, and also due to the handover of Hong Kongs sovereignty. Currently, HSBC hldgs is both the worlds largest banking and financial services group and the 5th largest stock in the Hong Kong Stock Exchange (HKEX) by market capit alization. HSBC hldgs is also listed on the Bermuda, New York, London and Paris Stock Exchanges. It is a constituent of the FTSE 100 Index and the largest company listed on the FTSE. In 2010, CEO Michael Geoghegan moved to Hong Kong since HSBC hldgs focuses on Asia more now. Similarly, both daily and weekly closing prices of HSBC hldgs ranging from 1 July 1997 to 30 June 2008 are retrieved from Datastream. The data set is also divided into the in-sample estimation periods 1997 2007 and the out-of-sample forecast periods 2007 2008. The number of in-sample observations and out-of-sample observations for both daily and weekly series are also the same as the HSI series and the non-trading days are excluded. For in-sample observations, there are 2467 for daily series and 522 for weekly series. For out-of-sample observations, there are 246 for daily series and 52 for weekly series. During this period, the average closing price of HSBC hldgs is 96.92 and the stand deviation is 23.2 6. The lowest closing price in these eleven years was 40.48 which occurred on 21 September 1998. 140.586 was the highest closing price and occurred on 15 October 2007. 3.1.3 Oriental Press Group Limited In this paper, another stock which is not the constituent stock is also studied. The stock is selected randomly like the chosen constituent stock. Oriental Press Group Limited is selected and is incorporated in Hong Kong. The group takes part in the publication of daily newspapers, Oriental Daily News and The Sun, and The Sun Racing Journal. Oriental Daily News was first published in 1969. Today, the paper is at the top in the daily circulation and has a record readership of around 3,100,000. Unlike Oriental Daily News, The Sun targets at a younger and fresher reader groups. The Sun was published since 1999, and can also be found outside Hong Kong. By considering a lot of Hong Kong people living in North America and in order to develop the market of The Sun, there is a North America version in New York and Toronto. The Sun Racing Journal was established in 1991. It is one of the major horse racing magazines in Hong Kong. Like the HSI and HSBC Hldgs, both daily and weekly clos ing prices of Oriental Press Group Limited ranging from 1 July 1997 to 30 June 2008 are retrieved from Datastream. The data set is also divided into the in-sample and the out-of-sample with the same period as the above two series. The number of in-sample observations and out-of-sample observations for both daily and weekly series are also equal to as that of the HSI series and HSBC hldgs series. During this period, the average closing price of Oriental Press Group Limited is 1.499 and the stand deviation is 0.657. The lowest closing price in these eleven years was 0.533 which occurred on 23 June 1998. 3.175 was the highest closing price and occurred on 14 January 2004. 3.1.4 Transformation of Data As this paper is studying the volatility, I focus on the returns of the HSI and the stocks rather than the index and closing prices. Therefore, a transformation of data is needed before modeling. Data are transformed into daily returns by taking the first difference of the natural logarithm of the daily index and closing prices. : The daily return : The current price at Day t : The closing price at Day t-1 Mean equals to 0.00138 and stand deviation is 0.0176. Mean equals to 0.00163 and stand deviation is 0.0169. Mean equals to -0.00032 and stand deviation is 0.0315. 3.2 Methodology Stationarity If a time series can be estimated using time series models, it must be a stationary process, as non-stationary process cannot be studied directly. If the joint probability distribution of time series data keeps constant when time is moving, this data set can be said to be stationary and the mean and variance do not change over time. On the contrary, a non-stationary process whose joint probability keep changing in the period. The mean and variance vary at different time points. However, usually after a non-stationary series has been transformed, such as differencing or log-difference, the series become stationary. Accordingly, it is necessary to test whether the data set is under a stationary process before any further analyzing. The Augmented Dickey and Fuller (ADF) test is employed in this paper. The ADF test is an improved version of the Dickey and Fuller (DF) test. Both tests null hypotheses are the series with a unit root, which means the series is non-stationary. The DF te st is only based on a simple regression. where the residuals are followed the Dickey-Fuller Distribution. The hypothesis testing is The test statistic is After that, comparing the test statistic with the relevant critical value for the Dickey-Fuller test, if the value is larger than the critical value, the null hypothesis can be rejected, and the model is stationary; vice verse. The ADF test is based on a more complicated more. where is a constant, is the coefficient of a deterministic trend and is the summation of the lag orders of the autoregressive process. Historical mean Probably, the historical mean provides the easiest way to forecast the volatility. All the in-sample observations are used and equally weighted to forecast. where is the number of out-of-sample observations Moving average Under moving average method, the forecast of the volatility is given by an unweighted average of the in-sample observations. Unlike the historical mean method, not all the in-sample observations are used. The average is based on the in-sample observations over a particular time interval of fixed length. where T is called the moving average period or rolling window. Three different lengths are considered for each frequency. For daily data, three months, six months and one year are chosen. For monthly data, six months, one year and two years are chosen. Random walk Both historical mean and moving average methods assume that the volatility will be in stable or change slowing with a trend. But, if the volatility fluctuates unpredictably, the best forecast of the volatility of next period is the current real volatility. Exponential smoothing Under exponential smoothing method, the forecast of the volatility is a weighted average of the previous actual observation and the previous forecast. where is the smoothing factor, and must be between 0 and 1. If is equal to zero, the forecast exactly equals the prior actual observation and the exponential smoothing method becomes random walk. When approaches one, the preceding forecast is the majority. The value of smoothing factor is determined by minimizing the in-sample sum of squared errors. The estimated smoothing factors for daily data are between 0.64 and 0.95. For weekly data, the values lie in the range 0.88 to 0.92. Exponentially weighted moving average (EWMA) The exponential weighted moving average (EWMA) method is similar to the exponential smoothing method. The only difference is that the prior actual volatility is replaced by a moving average method. where is a smoothing factor lying between 0 and 1. Like the exponential smoothing method, the EWMA method becomes random walk if the smoothing factor is equal to zero. When the factor is getting closer and closer one, the moving average will be weighted heavier and heavier. For daily data, the estimated values of factors are in the range 0.001 to 0.53. The smoothing factors lie between 0.001 and 0.4 for weekly data. Generalized autoregressive conditional heteroscedasticity (GARCH) If the variance of the data is constant, the process is called homoscedastic. However, a lot of time series data in the real world have time-varying variance. In 1982, Engle proposed the autoregressive conditional heteroscedasiticity (ARCH) model to deal with time series data. Four years later, Bollerslev suggested the GARCH model. A GARCH (p , q) model is where , , and . There are joint estimations of both the conditional mean and the conditional variance process in the GARCH model. The error term, , follows a normal distribution with zero mean and the variance which varies with time. indicates the persistence of shocks to volatility. If the sum is equal to one, the GARCH model becomes the integrated-GARCH (IGARCH) model. Threshold-GARCH (TGARCH) In the GARCH model, it assumes that shocks with equal magnitude have the same impacts on the volatility no matter which sign shocks are, positive or negative. However, the different sign of the shock may have a different impact on the volatility. In order to parameterize this idea, the TGARCH model is one of a number of ways. The TGARCH model was proposed by Glosten, Jagannathan and Runkle in 1993. The model is express as where if , and if . Consequently, a positive shock has an impact of on the volatility, and a negative shock has an impact of on the volatility. If is greater than zero, the negative shock has an larger impact on the volatility and vice versa. quantifies the persistence of shocks. Asymmetric-GARCH (AGARCH) The AGARCH model is another way to parameterize the idea that different sign of shock with equal magnitude has a different impact on the volatility. The AGARCH model looks quite similar to the TGARCH model and is expressed as If is a positive number, the positive shock has a smaller impact on the volatility and vice versa. Exponential-GARCH (EGARCH) The EGARCH model (Nelson, 1991) does not have the non-negativity constraint on the parameter, and , in the above three GARCH models. And also, the EGARCH model is the third asymmetric kind of GARCH models in this paper. The model is expressed as where The positive value of indicates that the positive shock has an larger impact on the volatility and vice versa. 3.3 Forecast evaluation After the volatility has been forecasted, the forecasters need to be evaluated to see how small the forecast errors are. There are several loss functions for the forecast error. The choice of loss functions totally depends on investors preferences. Symmetric loss function The mean error (ME), mean absolute error (MAE) and root mean squared error (RMSE) are the easiest and commonest ways to measure the forecasting performance. The result of the ME can be an indicator showing the direction of overprediction or underprediction on average. As the errors by overprediction and underprediction offset each other, it is not surprising that the ME statistic usually is the lowest one among the others. Unlike the ME, the MAE does not have the problem of the offsetting effect. The RMSE provides a measurement if the investor prefers to impose a more heavily penalty upon the larger forecast errors. Asymmetric loss function As mentioned before, all investors have their individual preference for the loss functions. It is likely that investors have asymmetric loss functions rather than symmetric loss functions. Referring the past research (Pagan and Schwert, 1990; Brailsford and Faff, 1996), the mean mixed error is employed in this paper. Firstly, the mean mixed error with a more heavily penalty on the forecast errors by underpredicting is Secondly, the mean mixed error penalizing the overpredicted forecast errors move heavily is where O is the number of overpredictions and U is the number of underpredication in the out-of-sample forecasts.

Tuesday, December 17, 2019

Class Action Lab Report Essay examples - 1100 Words

Joseph Sulaiman Alya Abourezk 4/13/12-4/14/12 Class Action Lab Report Purpose: To determine, through observations, the identity of substances produced in a chemical reaction. Materials: Safety Goggles Stirring rod Magnesium ribbon Solid CuSO4†¢H2O Matches Test tubes Copper metal 3M HCl Red and blue litmus strips 0.1M AgNO3 Zinc metal 0.1M Pb(NO3)2 Test tube holder 0.1M KI Bunsen burner 0.1M Na2NO3 Crucible tongs 1.0M NaOH Water (H2O) 0.1M Ca(NO3)2 50mL beaker 1.0 CuSO4 Procedure: Using the crucible tongs, take a magnesium strip and hold it in hot spot (just above the inner cone of fire) on the bunsen burner. Use peripheral vision as not to damage your eyes and hold the tongs well so the†¦show more content†¦Dispose in the waste beaker. In a clean test tube place 1mL of Na2CO3. Add 1mL of Ca(NO3)2 to the sodium carbonate solution. In a clean test tube place 1mL of NaOH. add 1mL of CuSO4 to the sodium hydroxide solution. Observations: Chemical Equation Initial Observation Final Observation Type of Reaction 2Mg+O2 -- 2MgO shiny, metallic, grey strip white, powdery, chalky substance synthesis, combustion MgO+H2O -- Mg(OH)2 chalky Mg and clear, colorless water create milky white, but clear solution Red litmus: turned slightly blue Blue litmus: no obvious change synthesis CuSO4†¢H2O -- CuSO4+5H2O cobalt blue crystal blue substance turned white, rocky, opaque-yellowish deposit, water vapor decomposition CuSO4+5H2O -- CuSO4†¢H2O clear, colorless water, white, rocky copper substance crackling, white dissolved, created a blues clear substance decomposition, synthesis 2HCl+2Zn -- H2+2ZnCl Zinc is silver, lustrous; HCl clear and colorless Zn bubbled in the HCl solution single displacement, synthesis 2ZnCl+H2 -- 2HCl+2Zn Zinc was bubbling when exposed to fire, the H gas made a popping noise single displacement 2AgNO3+Cu -- Cu(NO3)2+Ag2 clear, colorless AgNO3; bronze lustrous copper balls Cu gave off bubbles and were coated in a black, near mold like, substance single displacement Pb(NO3)2+2KI -- PbI2+2KNO3 2 clear, colorless substances bright, sunshine yellow substance (residue, solid powder) double displacement Na2CO3+Ca(NO3)2 -- 2NaNO3+CaCO3Show MoreRelatedLaboratory Management- General Functions of a Laboratory Manager1249 Words   |  5 Pagesassignment the general functions of a laboratory manager is being discussed. 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Monday, December 9, 2019

Economics and Quantitative Analysis Economy

Question: (a) Provide a descriptive analysis of the two variables (e.g., mean, standard deviation, minimum and maximum). (b) Develop a scatter diagram with retention rate as the independent variable. What does the scatter diagram indicate about the relationship between the two variables? (c) Develop and estimate a regression equation that can be used to predict the graduation rate (%) given the retention rate (%). (d) State the estimated regression equation and interpret the meaning of the slope coefficient. (e) Is there a statistically significant association between graduation rate (%) and retention rate (%). What is your conclusion? (f) Did the regression equation provide a good fit? Explain. (g) Suppose you were the president of South University. After reviewing the results, would you have any concerns about the performance of your university compared to other online universities? (h) Suppose you were the president of the University of Phoenix. After reviewing the results, would you have any concerns about the performance of your university compared to other online universities? Answer: Introduction The two variables that are used within this report for the performance of several universities are named as the graduation rate and a retention rate. The graduation rate is dependent on the retention rate where the retention rate means the rate at which the students continue their education from the same university. Through it, the graduation rate will likely increase because the retention rate is higher. This would mean that the increase in the retention rate will have a positive increasing effect on the graduation rate which needs to be assessed through scatter plot and regression equation (Beldona, 2010). Other statistical tools will also be used like min and max method, mean and standard deviation. The min method provides us with the most minimum retention rate among the universities and the most minimum graduation rate among the universities. The max method will provide us with the most maximum retention and graduation rate. The mean will provide the analyst with the most common percentage among the universities signifying the retention rate and graduation rate. The standard deviation will provide us the variability from the mean. The scatter plot will facilitate in identifying the relationship between the two variables and the regression equation will help in illustrating the relationship in an equation. Purpose The 29 online universities are to be analyzed in accordance with their performance using statistical tools to statistically analyze the data for it. Those statistical measures would include the min-max method, scatter plot, mean, standard deviation and regression equation. All of the terms are already explained in the previous section. The purpose would notify as to how the two variables are interrelated with each other. Background The statisticians use these measures to understand the dataset provided within the study. The statistical tools will help us in understanding the statistical relationship between the two variables and hence, it would be quite effective to apply the statistical tools to determine the exact relationship between the two variables. (Armstrong, 2013) In accordance with the regression equation, this is the most significant statistical tool because the relationship between the two variables is described in to an equation where the relationship provides us with a good analysis of the independent and dependent variable. This is done through an equation below: Y = m X +c The retention rates and graduation rates have been a in significant relation between each other which has resulted the retention rate to officially think of it that all such retention rates were increasing and the graduation rates are decreased. Method Firstly, mean is determined which would let us know the most common rate among the 29 online universities. The mean would identify the most common rate in accordance with the retention rate and graduation rate. In the same way, then standard deviation method will let the statisticians know as to how much variability is there from the determination of mean. Some other methods like min and max method. The min means the least possible value for the retention rate or graduation rate. However, the scatter plot will also be used which represents the illustration of the graph through which the type of relationship will be evident. There are three types of relationship which can be illustrated through a scatter plot (Kahng, 20010). The first one is known as a positive relationship between the two variables. That means that when X increases, Y also increases. Then there exists a negative relationship. That means that when X increases, Y decreases or when X decreases, Y increases. Therefore, t he two variables are then negatively correlated. After that, the third type of relationship is the no relationship (Omran, 2010). This means that there is no resultant increase in Y with an increase in X. Both the variables remain unchanged. Then the regression method is also used to describe the relationship between the two variables in an equation form. Results The results for the given data are shown below: Astatisticaltechnique used to explain or predict the behaviour of adependent variable. Generally, aregressionequationtakestheformof Y= m X + c , where Y is the dependent variable that the equationtriesto predict, X is theindependent variablethat is being used to predict Y, a is the Y-intercept of the line, and c is avaluecalled the Y intercept (Kahng, 2013). It can be seen that the mean retention rate is 57.4% while the mean graduation rate is 42%. The standard deviation for the retention rate is 23% while the standard deviation for the graduation rate is 9.8%. The max retention rate is 100% while the max graduation rate is 61%. The minimum retention rate is 4% while the minimum graduation rate is 25%. The most common retention rate among the universities is 57.4% emulated by the graduation rate of 42%. Scatter Plot The scatter plot for this data set is shown as under: The m slope is calculated as the graduation rate = M x retention rate + 25.4%. Hence According to the author, the scatter diagram shows a positive relationship between the two variables. However, at certain instances, the retention rates are lower and the graduation rates are substantially higher (Murnance, 2013). This is a great concern for the statistical analyst from this point of view. However, to sum up as a whole, the retention rate increases due to which the graduation rate also increases. Regression equation The regression equation can be described as the relationship between the two variables being studied or researched. It also makes sense for the dependent of any influence on the subject through these two variables (Abernathy, 2011). The regression equation is as under: The above regression equation is Y = 0.284 X + 25.423. Y is the graduation rate, X is the retention and 25.423 is the Y intercept. Hence, the positive relationship is quite significant from the above universities. And, this can be proved in this manner: Y = 0.2845 x 35 + 25.423 = 35% Y = 0.2845 x 40 + 25.43 = 36% Therefore, the increase in any positive aspects will prove to be a great source of influence on the graduation rate. Discussion and Recommendations The University of South has a favorable graduation rate along with a retention rate. The graduation rate increases because the retention rate increases as it has been discussed above already. But the first and foremost concern is that some of the universities have lower retention rates but their graduation rates increase and that might be due to the fact that the retention rate is decreasing. Apart from this, the Universities of Phoenix has a lower retention rate and the most surprising fact is that the graduation rates also increase (Karafiath, 2014). The graduation rates are solely dependent on the retention rate. With such a lower retention rate, the graduation rates are increasing. This would mean that when the retention rate increases, the graduation would increase more than all this. This is due to the fact that with a lower retention rate, the graduation rate is still more favorable. Bibliography Abernathy, J.R., 2011. Smoking as an independent variable in a multiple regression analysis upon birth weight and gestation. BMJ Journal, 56(4), pp.626-33. Armstrong, J.S., 2013. Illusions in Regression Analysis. International Journal of Forecast, 3(2), pp.689-94. Beldona, S., 2010. Regression analysis for equipment auditing. Managerial Auditing Journal, 22(8), pp.809-22. Kahng, S., 20010. TEMPORAL DISTRIBUTIONS OF PROBLEM BEHAVIOR BASED ON SCATTER PLOT ANALYSIS. Journal of Applied Behavior Analysis, 31(4), pp.593-604. Kahng, S., 2013. TEMPORAL DISTRIBUTIONS OF PROBLEM BEHAVIOR BASED ON SCATTER PLOT ANALYSIS. Journal of Applied Behavior Analysis, 31(4), pp.593-604. Karafiath, I., 2014. Estimating cross-sectional regressions in event studies with conditional heteroskedasticity and regression designs that have leverage. International Journal of Managerial Finance, 10(4), pp.418-31. Murnance, J., 2013. U.S. High School Graduation Rates: Patterns and Explanations. Journal of Economic Literature, 51(2), pp.370-422. Omran, M., 2010. Linear Versus Nonà ¢Ã¢â€š ¬Ã‚ linear Relationships Between Financial Ratios and Stock Returns: Empirical Evidence from Egyptian Firms. Review of Accounting and Finance, 3(2), pp.84-102.

Sunday, December 1, 2019

Oedipus the King Guilty or Innocent free essay sample

A Reaction Paper in English 106 (Greek Drama) Oedipus the King: Guilty or Innocent Submitted to Dr. Ulysses B. Aparece Submitted by Elmer J. Mangubat Guilty or Innocent Guilt presupposes the commission of sin; yet what comprises sin? From the moral standpoint, sin is the denial of what is good that is ought to be done or to happen; or sin is the omission of what is ought to be done. For sin to be categorized as such, there has to be a set of moral standards from which judgment on whether sin happens or not proceeds. Thus, to say Oedipus is guilty remains to be seen.Proponents have long debated over Oedipus’s guilt or innocence. I would like to react on some of P. H. Vellacot’s assertions on the guilt of Oedipus. Firstly, Vellacot says â€Å"the terrible destiny of Oedipus is shown as one put upon him by supernatural powers in general, by that comprehensive Fate which governs every man’s life. We will write a custom essay sample on Oedipus the King: Guilty or Innocent or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page † At the onset of his commentary, Vellacot seems to suggest that Oedipus is already latched into this tragic path of destiny—as ordained by Fate, with the concurrence, of course, of the gods.In the intricate lives of Greek heroes and heroines, the role of the gods and goddesses is a predominant theme. It is not surprising, therefore, that Oedipus is no exception. It’s as if his fate has long been sealed, and the oracles and prophesies are just a confirmation to this tragic playing of his role. As the term comprehensive suggests, Oedipus is bound to follow his fate as preordained by the powers that be. Secondly, Vellacot argues that if everything is because of fate then Oedipus is without sin; therefore, there can be no tragedy.Then he purports this question: â€Å"How can there be a true tragedy without sin? † Following this line of thinking, Vellacot asserts that Sophocles must provide the sound claim for Oedipus’s sin to justify the tragic character of the play—thus Oedipus’s false accusations on Tiresias and Creon. To Vellacot, however, this move is just Sophocles’ ploy of adding sin to justify Oedipus’s downfall. This assertion of Vellacot might be bordering on speculation, yet there could be a grain of truth in it.The assertion is seemingly a potent ploy of Vellacot himself to heighten the debate over Oedipus’s guilt, which is epitomized by the question: â€Å"How can there be a tragedy without sin? † The forego ing propositions of Vellacot on Oedipus’s innocence pave the way for Vellacot’s rebuttal—that although it was clear that the oracle had spoken, Oedipus had full awareness of the things that he should and could have avoided through the premonitions of the drunkard, and later of Tiresias.With the premonitions in place, the foremost thoughts of Oedipus should have been, as Vellacot purports, to avoid (1) killing an old man and (2) avoid marrying an elderly woman. The literary critic E. R. Dodds shares a similar view with Vellacot. The moralist, as he says, might ask, â€Å"Knowing that he was in danger of committing patricide and incest, would not a really prudent man have avoided quarreling, even in self-defense, with men older than himself, and also love-relations with women older than himself? † This is a very valid claim, indeed.Oedipus could have exercised self-control knowing that even the slightest of probabilities could trigger that rush of fate. Dodds reasons, however, that these considerations would have been in place had we been scrutinizing the character of a person. Instead, we should look at it in the light of the dramatist perspective, as Dodds puts it: â€Å"We are examining the intentions of a dramatist, and we are not entitled to ask questions that the dramatist did not intend to ask. † On a personal note, I would say to some degree that Oedipus has certain culpability toward his actuations.Although the Greeks perceive their gods as if they were one with them in all that they do, the fact remains that Oedipus is still a human being; and part of his humanity is that he has the intellect and the will. To some degree, he has the will and freedom to check his actuations in the different circumstances that he is in. This would have been the ideal way that Oedipus could have played his role. However, questions would hound us again: Who are we to question the dramatist’s intentions? What would become of the play—of the tragedy in particular—had things gone the other way?