Industry and Competitive Analysis for entry Market

Iran markets are strategically positioned thereby making it possible for customers to access products that are to be sold by this company. From the survey done, it is evident that there are numerous industries in Tehran and its outskirts. It is also evident that these industries are prone to pollution making the population around them to have respiratory-related complications.Advertising We will write a custom essay sample on Industry and Competitive Analysis for entry Market specifically for you for only $16.05 $11/page Learn More Quantitatively, about 27 lives are lost every day due to pulmonary illnesses. It is imperative to note that the quality of air in Iran’s city is extremely hazardous. Moreover, the transport system has also contributed towards increased air pollution in the city. Moreover, cars and motorcycles contribute to approximate of 30% of air pollution. In this essence, Iran can serve as a better entry market since population will highly prefer nasal screens as an alternative to prevent themselves from pollution challenge. In terms of industrial mapping, it is always advisable to consider the attractiveness of a market entry (Global Business Union, 2011 par 2). In this case, Iran has wide range of industries in which workers need protective facilities such as defense filters to safeguard their health. For instance, within the medical hospitals, most of the allergy sufferers might prefer to use safety defense nasal screens in order to free themselves from foreign antigens such as dust, smell and pollen. In line with this, it is clear that majority of laborers work in agricultural farms and industries. On behalf of the company, it is highly likely that workers would benefit from our products and in the long run, they will be safe from harmful chemicals from farms and agro-industries. Eminently, Iran emerges as a competitive market entry point. There are certain competitive sets that make viable market entry fo r certain product (Porter, 1990 p.22). For instance, there are no sophisticated means of eliminating nasal borne illnesses in Iran. In actual sense, there are certain strains of flu that affect the population during cold weather. Therefore, people prefer taking antibiotics in ampoule form in order to prevent or cure such strains.Advertising Looking for essay on business economics? Let's see if we can help you! Get your first paper with 15% OFF Learn More Ironically, physicians in the country give prescriptions to patients which they are not even sure whether it will work or not. This implies that there are no better strategies of preventing people from getting respiratory complications in Iran. Moreover, people in hazardous conditions rely on masks that are not efficient for the risky environment. In this case, Iran offers this company a chance to make it a viable market entry point for the nasal screens. Therefore, it is beyond doubt that Iran is an attractive market for the nasal screens. It is also apparent that there are five forces which must be considered when evaluating the attractiveness of a target market (Porter, 1990, p.20). Considering the five forces analysis, there are no possible threats since there seems to be no many competitors for this market site. An empirical research conducted indicates that there are no other companies selling a similar product in the country. Moreover, there is lack of other significant products that might act as substitutes for the nasal screens since they are highly effective than the antibiotics. In line with this, the customer bargaining power is favorable since they are not price sensitive to an extent of putting pressure on the company (Porter, 1980, p. 10). On the other hand, suppliers bargaining power is excellent due to lack of substitute products in the country. Additionally, competition and market rivalry on the industry might not affect the company since there are means of developing sustainab le competition through use of new technology. The major competitors of Free Defense Nasal Screens include some of the diverse industries that manufacture face masks in USA. Although face mask industries have reached out for customers globally, these masks have not been fully convenient for people who work in hazardous air conditions. The strategies used by such companies include online advertisements and social media marketing in order to create market entries. Nevertheless, their products in the market are being replaced by other significant products such as nasal screens. According to the analysis done, the competitors target market entries in strategic places for easier shipping of their products (Porter, 1980 p. 12).Advertising We will write a custom essay sample on Industry and Competitive Analysis for entry Market specifically for you for only $16.05 $11/page Learn More Moreover, their marketing agents are located on sites that are accessible and densely populated to ensure that customers can easily get the products. In any business weaknesses and strengths goes hand in hand (Global Business Union, 2011 par 3). There are certain weaknesses that are associated with the competitors’ products. For instance, face masks fail to offer absolute nasal protection as opposed to nasal screens. Moreover, the other shortcoming is that competitors’ products are not very reliable to protect individuals from highly toxic pollutants (Porter, 1990 p.22). This compels users to prefer nasal screens as opposed to face masks. Moreover, the technology used by competitors is inconveniencing as opposed to that of FDNS. For instance, our company ensures that the model of making nasal screens is attractive and does not impede one from doing certain activities. In this case, face masks impedes an individual from talking, eating or even sleeping. Contrastingly, free defense nasal screens are highly convenient since they don’t interf ere with sleep, work and communication. However, positive strength associated with the competitor’s products is that they are barely noticeable and are relatively cheaper than nasal screens. For instance, face masks are effective and therefore convenient for use. References Global Business Union. 2011. Market and Research Web. Available from  http://www.globalbusinessunion.com/market-research.php Porter, M. 1980. Competitive Strategy. New York: Free Press Inc. Porter, M. 1990. The Competitive Advantage of Nations. New York: Free Press Inc.Advertising Looking for essay on business economics? Let's see if we can help you! Get your first paper with 15% OFF Learn More This essay on Industry and Competitive Analysis for entry Market was written and submitted by user Kali Kirk to help you with your own studies. You are free to use it for research and reference purposes in order to write your own paper; however, you must cite it accordingly. You can donate your paper here.

Looking for God Essays

Looking for God Essays Looking for God Essay Looking for God Essay Where are you supposed to look for God? How are you to look for God, and does it help to decide what sort of thing you are looking for first? What kind of thing is God?Looking from the perspective of someone with no previous faith, looking for general revelation, I would have to say you have to perceive what you think is God-like first. If you ask the majority of people with a faith what God is like, they would probably say all good and all-powerful. So are you looking for general signs of goodness, beauty, power and awe? If you are then you can rule out finding God in evil, ugliness, weakness and un-impressiveness. Or can you? I know that black isnt white, but people thought Hitler was good; beauty is in the eye of the beholder, so an ugly person to me might be stunningly beautiful to someone else; the queen bee is supreme over mere drones, however it looks a bit pathetic in comparison to even simple humans; and David Beckhams match and free kick against Gree ce last year left thousands of footy fans awe-struck, yet my brother just couldnt see what the fuss was about.God is deeply personal, so will be found in different places for everyone.If you thought that The Miracle Of life was completely un-earthly, and could only be explained with a super-human being, then I bet I could find half a dozen more that just think we are here accidentally.If you are solely looking for good, you can still find it in atrocities. In Sept. 11 you could find God in the power and awe of it all, but also in those brave people whose spirit never wavered, in those volunteers determined to help, in those fore-fighters who gave their life to save others, those selfless rescuers, counsellors, vicars, children. Osama tried to devastate American spirit and attitude as well as massacre, but he failed in crushing the love and goodness in peoples heats and minds. You could argue that God was acting in every fire-fighter that tragic day, helping to put lives back togethe r, but Muslims would say God (or Allah also all-good and all-powerful) was acting in those heroic terrorists.Here is the miracle of life presented to us, and what we to it.Here is the beautiful world: but which picture is which? Which is beautiful? Which is miraculous? Which is hideous? Which one has God in it, or which one has God acting in it?Here is another view. If God is linked with personal feelings and opinions, then what is a feeling, and what makes it right or wrong (because if we knew who was right, we would know whom has God sussed). Feelings are only felt or experienced by a living creature. So what is a life? Is a tree any more than just a bunch of leaves and wood? Yes and no. Yes it has a life, it can feel stuff. No leaves and wood is all it is made of physically. So if personal feelings govern what is good, beautiful, miraculous, etc, then does God love in feelings, actually IN us? Is God everything, good and bad? Pantheism?

Discuss the advantages and disadvantages of conducting personal Essay

Discuss the advantages and disadvantages of conducting personal communication in the public sphere - Essay Example An increasing number of people are using social networking websites in order to supplement personal communication. In this context, it can be stated that one of the most popular social networking websites Facebook provides the capability to organize and communicate in an effective manner. Simultaneously, Facebook also provides the capability to express the thoughts of people and to categorize them independently (Westling, 2007). Thesis Statement Considering this aspect, the essay is based on analyzing the vital aspects of conducting personal communication through Facebook. The objective of the essay is to understand the advantages and disadvantages of utilizing an online public sphere medium i.e. Facebook for conducting personal communication. Statement of Intent The essay will intend to address the facet of personal communication in traditional era as well as in present era. Besides, the essay also intends to discuss the suitability of internet as a public sphere medium. Personal Co mmunication Strategies in Past and Present There is a huge dissimilarity between personal communication strategies of past and present times. The actual dissimilarities have been observed due to the evolution of technology. In order to clarify the perspective, it can be depicted that traditionally, the common personal communication techniques were letter writing or telephone conversation among others. The personal communication techniques have evolved from letter and telephone to mobile communication and internet communication technologies. Mobile phone technologies are continuously being updated and developed with new features and devices and internet communication technologies such as social networking sites are continuously accumulating new applications and functionalities. Social networking websites in a sense is a grouping of every internet communication method. In social networking websites such as Facebook, people can create accounts and communicate with other people. It also provides the opportunity to stay up-to-date about the activities of friends and send information either publically or privately. In recent times, the popularity of social networking has increased dramatically where Facebook leads the competition with millions of active users. According to the research conducted by United Nations Research Institute for Social Development in 2000, the introduction of internet and social networking websites has increased the communication level. The level of information exchange through this new medium has far surpassed the traditional communication media such as telephone, face-to-face conversation and meeting among others. This trend represents financial benefits particularly to the poor nations. The present personal communication strategy of using social networking websites provides the opportunity for exploring new customs of other nations and also helps to enhance the knowledge (Serbanescu, 2011). Internet as Public Sphere Public sphere is partic ularly vital for communication in current civilization. It acts as a medium in which people can communicate effectively regarding important matters along with permitting people to inform about vital aspects. Considering the significant role played by internet, several researchers have framed normative theories which demonstrate how public sphere can be organized in order to ideally accomplish its roles. One of the most conspicuous

Insight into the Stagnant Performance of Textile Industry in Pakistan Literature review

Insight into the Stagnant Performance of Textile Industry in Pakistan - Literature review Example Pakistan is a major player globally in the textile industry. The textile industry in Pakistan can be traced back to its origins in 1947. In 1947, the country produced approximately 1.1 million bales of cotton (Iqbal 2010). Over the years, the production of cotton has increased to reach over twelve million bales in 2010. The country has also diversified its cotton products to increase the quality and quantity of products that are exported. For instance, Pakistan textile industry has increased the quantity of ready made garments that are exported. However, the textile industry in Pakistan has been faced with numerous challenges. One of the issues that have affected the textile industry in the country is the stagnation in the amount of textile products that are exported (Channar & Nannik 2010). Also, other Asian countries such as Bangladesh and China have become fierce competitors in the textile industry. Due to these challenges, it is necessary for the Pakistan textile industry to reinvent itself in order to improve the quantity and quality of cotton products so as to stay competitive. The study would evaluate the current status of the Pakistan textile industry and globally. The research will examine the global market and production of cotton and cotton related products in to contextualize the situation in Pakistan. The rapid globalization of the world economies has made all countries to be intertwined such as that international issues significantly affect national economies. The research will also look at the amount of investment that Pakistan has dedicated to the development of the textile industry in the country. The research aims to evaluate the factors that have led to the stagnation of the Pakistan textile industry with a view of answering the research objectives. A critical understanding of the issues affecting the growth of the textile industry will help the

El Salvador Essay Example | Topics and Well Written Essays - 750 words - 1

El Salvador - Essay Example The poor groups are characterized with poverty and without property, with little opportunity to explore the possibility of expanding their income as well as education opportunity. This population increasingly lives in poverty, a situation that has been associated with high birthrate. (Romanoff, Steven, 28) Study reveals that the increasing population of the El Salvador is as a result or poverty rather than the later being the course of the former. (Romanoff, Steven, 30) The largest share of the population in El Salvador is employed in the Agricultural sector work in the plantation. This presents another question of what could be the structure of the population and what is the evolution it has obtained overtime? What are the functions that have perpetuated poverty among those who live in the rural, and what are the consequences of the increase in the rate of population growth. There is a high level of poverty in rural as well as in urban among the people of El Salvador. (Romanoff, Steven, 32) This has been contributed to the fact that there is a high level of unequal distribution of land. (Maxwell, Daniel G., John W. Parker, and Heather C. Stobaugh, 69) Land is a key factor of production in El Salvador and lack or possession of it has a bearing in determination of the poverty gap. In a study conducted on the family income in El Salvador, it was revealed that 10% of those who received their income among the wealthiest obtained more wealth than the remaining 90% put together. While those who could be classified as top 1% obtained more income than that of the 50% of the poorest in the society. The wealthy families at 5.2% and were having more than 10 hectares of land were controlling overwhelming 73%. (Maxwell, Daniel G., John W. Parker, and Heather C. Stobaugh, 69) A different study conducted in 1992 revealed that 72% of the rural

Exploring Optimal Levels of Data Filtering

Exploring Optimal Levels of Data Filtering It is customary to filter raw financial data by removing erroneous observations or outliers before conducting any analysis on it. In fact, it is often one of the first steps undertaken in empirical financial research to improve the quality of raw data to avoid incorrect conclusions. However, filtering of financial data can be quite complicated not just because of the reliability of the plethora of data sources, complexity of the quoted information and the many different statistical properties of the variables but most importantly because of the reason behind the existence of each identified outlier in the data. Some outliers may be driven by extreme events which have an economic reason like a merger, takeover bid, global financial crises etc. rather than a data error. Under filtering can lead to inclusion of erroneous observations (data error) caused by technical (e.g. computer system failure) or human error (e.g. unintentional human error like typing mistake or intentional human err or like producing dummy quotes for testing).[1] Likewise, over filtering can also lead to wrong conclusions by deleting outliers motivated by extreme events which are important to the analysis. Thus, the question of the right amount of filtering of financial data, albeit subjective, is quite important to improve the conclusions from empirical research. In an attempt to somewhat answer this question, this seminar paper aims to explore the optimal level of data filtering.[2] The analysis conducted in this paper was on the Xetra Intraday data provided by the University of Mannheim. This time-sorted data for the entire Xetra universe had been extracted from the Deutsche Bà ¶rse Group. The data consisted of the historical CDAX components that had been collected from Data stream, Bloomberg and CDAX. Bloombergs corporate actions calendar had been used to track dates of IPO listing, delisting and ISIN changes of companies. Corporations not covered by Bloomberg had been tracked manually. Even though few basic filters had been applied (for e.g. dropping negative observations for spread/depth/volume), some of which were replicated from Market Microstructure Database File, the data remained largely raw. The variables in the data had been calculated for each day and the data aggregated to daily data points.[3] The whole analysis was conducted using the statistical software STATA. The following variables were taken into consideration for the purpose of identifying outliers, as commonly done in empirical research: Depth = depth_trade_value Trading volume = trade_vol_sum Quoted bid-ask spread = quoted_trade_value Effective bid-ask spread = effective_trade_value Closing quote midpoint returns, which were calculated by applying Hussain (2011) approach: rt = 100*(log (Pt) log (Pt1)) Hence, closing_quote_midpoint_rlg = 100*log(closing_quote_midpoint(n)) log(closing_quote_midpoint(n-1)). Where closing_quote_midpoint = (closing_ask_price+ closing_bid_price)/2 Our sample consisted of the first fifteen hundred and ninety five observations, out of which two hundred observations were outliers. Only the first two hundred outliers were analyzed (on a stock basis chronologically) and classified as either data errors or extreme events. These outliers were associated with two companies: 313 Music JWP AG and 3U Holding AG. Alternatively, a different approach could have been used to select the sample to include more companies but the basics of how filters work should be independent of the sample selected for the filter to be free of any biases so for instance if a filter is robust, it should perform relatively well on any stock or sample. It should be noted that we did not include any bankrupt companies in our sample as those stocks are beyond the scope of this paper. Moreover, since we selected the sample chronologically on a stock basis, we were able to analyze the impact of these filters more thoroughly on even the non-outlier observations in the sample, which we believe is an important point to consider when deciding the optimal level of filtering. Our inevitably somewhat subjective definition of an outlier was: Any observation lying outside the 1st and the 99th percentile of each variable on a stock basis The idea behind this was to classify only the most extreme values for each variable of interest as an outlier. The reason why the outliers were identified on a per stock basis rather than the whole data was because the data consisted of many different stocks with greatly varying levels of each variable of interest for e.g. the 99% percentile of volume for one stock might be seventy thousand trades, while that of another might be three fifty thousand trades and so any observations with eighty thousand trades in both stocks might be too extreme for the first stock but completely normal for the second one. Hence, if we identified outliers (outside the 1st and the 99th percentile) for each variable of interest on the whole data, we would be ignoring the unique properties of each stock which might result in under or over filtering depending on the properties of the stock in question. An outlier could either be the result of a data error or an extreme event. A data error was defined using Dacorogna (2008) definition: An outlier that does not conform to the actual condition of the market The ninety four observations in the selected sample with missing values for any of the variables of interest were also classified as data errors.[4] Alternatively, we could have ignored the missing values completely by dropping them from the analysis but the reason why they were included in this paper was because if they exist in the data sample, the researcher has to deal with them by deciding whether to consider them as data errors, which are to be removed through filters or change them for e.g. to the preceding value and hence it might be of value to see how various filters interact with them. An extreme event was defined as: An outlier backed by economic, social or legal reasons such as a merger, global financial crises, share buyback, major law suit etc. The outliers were identified, classified and analyzed in this paper using the following procedure: Firstly, the intraday data was sorted on a stock-date basis. Observations without an instrument name were dropped. This was followed by creating variables for the 1st and 99th percentile value for each stocks closing quote midpoint returns, depth, trading volume, quoted and effective bid-ask spread and subsequently dummy variables for outliers. Secondly, after taking the company name and month of the first two hundred outliers, while keeping in consideration a filtering window of about one week, it was checked on Google if these outliers were probably caused by extreme events or the result of data errors and classified accordingly using a dummy variable. Thirdly, different filters which are used in financial literature for cleaning data before analysis were applied one by one in the next section and a comparison was made on how well each filter performed i.e. how many probable data erro rs were filtered out as opposed to outliers probably caused by extreme events. These filters were chosen on the basis of how commonly they are used for cleaning financial data and some of the popular ones were selected. 4.1. Rule of Thumb One of the most widely used methods of filtering is to use some rule of thumb to remove observations that are too extreme to possibly be accurate. Many studies use different rules of thumb, some more arbitrary than others.[5] Few of these rules were taken from famous papers on market microstructure and their impact on outliers was analyzed. For e.g.: 4.1.1. Quoted and Effective Spread Filter In the paper Market Liquidity and Trading Activity, Chordia et al (2000) filter out data by looking at effective and quoted spread to remove observations that they believe are caused by key-punching errors.   This method involved dropping observations with: Quoted Spread > à ¢Ã¢â‚¬Å¡Ã‚ ¬5 Effective Spread/Quoted spread > 4.0 % Effective Spread/%Quoted Spread > 4.0 Quoted Spread/Transaction Price > 0.4 Using the above filters resulted in the identification and consequent dropping of 61.5% of observations classified as probable data errors, whereas none of the observations classified as probable extreme events were filtered out. Thus, these spread filter looks very promising as a reasonably large portion of probable data errors was removed while none of the probable extreme events were dropped. The reason why these filters produced good results was because it looked at the individual values of quoted and effective spread and removed the ones that did not make sense logically rather than just removing values from the tails of the distribution for each variable. It should be noted that these filters removed all the ninety four missing values, which means that only five data errors were detected in addition to the detection of all the missing values. If we were to drop all the missing value observations before applying this method, it would have helped filter out only 7.5%[6] of probab le data errors while not dropping any probable extreme values. Thus, this method yields good results and should be included in the data cleaning process. Perhaps, using this filter in conjunction with a logical threshold filter for depth, trading volume and returns might yield optimal results. 4.1.2. Absolute Returns Filter Researchers are also known to drop absolute returns if they are above a certain threshold/ return window in the process of data cleaning. This threshold is subjective depending on the distribution of returns, varying from one study to another for e.g. HS use 10% threshold, Chung et al. 25% and Bessembinder 50%.[7] In case of this paper, we decided to drop (absolute) closing quote midpoint returns > |20%|. Perhaps, a graphical representation of time series returns of 313music JWP 3U Holding can be used to explain why this particular threshold was chosen. Figure 1. Scatter plot of closing quote midpoint return and date As seen in the graph, most of the observations for returns lie between -20% and 20%. However, applying this filter did not yield the best results as only 2.5% of probable data errors were filtered out as opposed to 10.3% probable extreme events from our sample. Therefore, this filter applied in isolation doesnt really seem to hold much value. Perhaps, an improvement to this filter could be achieved by only dropping returns which are extreme but reversed[8] within the next few days as this is indicative of data error. For e.g. if T1 return= 5%, T2 return= 21% and T3 return=7%, we can tell that in T3 returns were reversed, indicating that T2 returns might have been the result of a data error. This filter was implemented by only dropping return values > |20%| which in the next day or two, reverted back to the value of return, +/- 3%[9]of the day before the outlier occurred as shown below: r(_n)> |20%| |r(n-1) -r(n+1)| |r(n-1) -r(n+2)| Where r(_n) is closing quote midpoint return on any given day. This additional filter seemed to work as it prevented the filtering out of any probable extreme events. However, the percentage of filtered data errors from our sample fell from 2.5% to 1.9%. In conclusion, it makes sense to use this second return filter which accounts for reversals in conjunction with other filters for e.g. spread filter. Perhaps, this method can be further improved by using a somewhat more objective range for determining price reversals or an improved algorithm for identifying return reversals. 4.1.3. Price Filter We constructed a price filter inspired by the Brownlees Gallo (2006) approach. The notion behind this filter is to gauge the validity of any transaction price based on its comparative distance to the neighboring prices. An outlier was identified using the following algorithm: | pi -   ÃŽÂ ¼ | > 3*à Ã†â€™ Where pi is the log of daily transaction price, the reason why logarithmic transformation was used is because the standard deviation method assumes a normal distribution.[10] ÃŽÂ ¼ is the stock sorted mean and à Ã†â€™ is the stock sorted standard deviation of log daily prices. The reason why we chose the stock sorted mean and standard deviation was that the range of prices vary greatly in our data set from one stock to another, hence, it made sense to look at each stocks individual price mean as an estimate of neighboring prices. This resulted in filtering 56.5% of probable data errors which were all missing values. Thus, this filter doesnt seem to hold any real value when used in conjunction with a missing value filter. Perhaps, using a better algorithm for identifying the mean price of the closest neighbors might yield optimal results. 4.2. Winsorization and Trimming A very popular filtering method used in financial literature is trimming or winsorization. According to Green Martin (2015a), p. 8, if we want to winsorize the variables of interest at ÃŽÂ ±%, we must replace the nÃŽÂ ± largest values by the nÃŽÂ ± upper quantile of the data, and the nÃŽÂ ± smallest values by the nÃŽÂ ± lower quantile of the data. Whereas, if we want to trim the variables of interest by ÃŽÂ ±%, we should simply drop observations outside the range of ÃŽÂ ±% to 1- ÃŽÂ ±%. Thus, winsorization only reduces extreme observations rather than dropping them completely like trimming. For the purpose this paper, both methods will have similar impacts on dropping outliers outside certain ÃŽÂ ±%, hence, we will only analyze winsorization in detail. However, winsorization introduces an artificial structure[11] to the dataset because instead of dropping outliers it changes them, therefore, if this research was to be taken a step further for e.g. to condu ct robust regressions, choosing one method over the other would depend entirely on the kind of research being conducted. The matter of how much to winsorize the variables, is completely arbitrary,10 however, it is a common practice in empirical finance to winsorize each tail of the distribution at 1% or 0.5%.5 We first winsorized the variables of interest at the 1% level, on a stock basis, which led to limiting 100% of probable extreme events and only 42.9% of probable data errors. Even though intuitively it would make sense for all the identified outliers to be limited because the method used for identifying outliers for each variable considered observations which were either greater than the 99th percentile or less than the 1st percentile, and winsorizing the data at the same level should mean that all the outliers would be limited. However, this inconsistency in expectation and outcome results from the existence of missing values winsorization only limits the extreme values in the data, overlooking the missing observations which have been included in data errors. We then winsorized the variables of interest at a more stringent level i.e. 0.5%, on a stock basis, which led to 51.3% of the identified data errors and 18.6% of probable extreme events to be limited which doesnt exactly seem ideal as in addition to data errors, quite a large portion of extreme events identified was also filtered out. Taking this analysis a step further, the variables of interest were also winsorized on the whole data (which is also commonly done) as opposed to on a per stock basis, at the 0.5% and 1% level. Winsorizing at the 1% level led to limiting 51% extreme events, 24.2% data errors and an additional one thirty four observations in the sample not identified as outliers. This points toward over filtering. Doing it at the 0.5% level led to limiting 28% extreme events, 12.4% data errors and an additional seven observations in the sample not identified as outliers. Thus, it seems that no matter which level (1% or 0.5%) we winsorize on or whether we do it on a per stock basis or on the whole data, a considerable percentage of probable extreme events is filtered out. Of course, our definition of an outlier should also be taken into consideration when analyzing this filter. Winsorizing on a per stock basis does not yield very meaningful results as it clashes with our outlier definition. However, doing it on the whole data should not clash with this definition as we identify outliers outside the 1st and the 99th percentile of each variable on the data as a whole. Regardless, this filter doesnt yield optimal results as a substantial portion of probable extreme events get filtered out. This is because this technique doesnt define boundaries for the variables logically like the rule of thumb method, rather it inherently assumes that all outliers outside a pre-defined percentile must be evened out and outliers caused by extreme events dont necessarily lie within the defined boundary. It must also be noted that the winsorization filter does not limit missing values which are also clas sified as data errors in this paper. Thus, our analysis indicates that this filter might be weak if we are interested in retaining the maximum amount of probable extreme events. Perhaps, using it with an additional filter for limiting missing values might yield a better solution if the researcher is willing to drop probable extreme events for the sake of dropping probable data errors. 4.3. Standard Deviations Logarithmic transformation Many financial papers also use a filter based on x times the standard deviation: xi > ÃŽÂ ¼ + x*à Ã†â€™ xi x* à Ã†â€™ Where xi is any given observation of the variable of interest, ÃŽÂ ¼ is the variable mean and à Ã†â€™ is variable standard deviation.[12] An example would be Goodhart and Figliuoli (1991) who use a filter based on four times the standard deviation.[13] However, this method assumes a normal distribution, 9 so problems might arise with distributions that are not normal and in our data set, except for returns (because we calculated them using log), the rest of the distributions for depth, trading volume, effective and quoted bid-ask spread are not normally distributed. Therefore, we first log transformed the latter four distributions using: y = log (x)[14] Where y is the log transformed function and x is the original function. The before and after graphs, using log transformation are shown in Exhibit 4. We then dropped observations for all the log transformed variables that were greater than Mean + x*Standard Deviation or less than Mean x*Standard Deviation, first on a stock basis and then on the whole data for values of x=4 and x=6. Applying this filter at the x=6 level on a stock basis seemed to yield better results than applying it at the x=4 level. This is because x=6 led to dropping 25.6% less probable extreme events for a negligible 3.1% fall in dropping probable data errors. The outcomes are shown in Exhibit 3. However, upon further investigation, we found that 100% of the probable data errors identified by the standard deviation filter at the x=6 level were all missing values. This means that if we dropped all missing values before applying this filter at this level, our results would be very different as this filter would be dropping 7.7% extreme events for no drop in data errors. Applying this filter on the whole data led to the removal of less outlier than applying it on a per stock basis. Using the x=6 level (whole data) appeared to yield the best results 58.4% of probable data errors were filtered out while no probable extreme events were dropped. For more detailed results, refer to Exhibit 3. However, even in this case, 100% of the probable data errors identified were missing values. This means that if we were to drop all missing values before applying this filter, this filter would identify 0% of the probable extreme events or probable data errors. Thus, the question arises if we are actually over filtering at this level? If yes, then should x Data cleaning is an extremely arbitrary process which makes it quite impossible to objectively decide the level of optimal filtering, which is perhaps, the reason behind limited research in this area. This limitation of research in this particular field and inevitably this paper should be noted. That being said, even though some filters chosen were more arbitrary than others, we have made an attempt to objectively analyze the impact of each filter applied. The issue of missing values for any of the variables should be taken into consideration because they are data errors and if we were to ignore them, they would distort our analysis because they interact with the various filters applied. Alternatively, we could have dropped them before starting our analysis, but we dont know if researchers would choose to change them to the closest value for instance or filter them out, therefore, its interesting to see how the filters interact with them. Our analysis indicates that when it comes to the optimal amount of data cleaning, rule of thumb filters fare better than statistical filters like trimming, winsorization and the standard deviation method. This is because statistical filters assume that any extreme value outside a specified window must be a data error and should be filtered out but as our analysis indicates, extreme events dont necessarily lie within this specified window. On the other hand, rule of thumb filters set logical thresholds, rather than just removing/limiting observations from each tail of the distribution. The outcomes of different filters which are shown in exhibit 1, 2 and 3 are represented graphically below. Figure 2. Box plot of outcomes of all the data cleaning methods As shown in section 4.2 and the graph above, Winsorization whether on a stock basis or on the whole data, tends to filter out a large portion of probable extreme events. Thus, it is not a robust filter if we want to retain maximum probable extreme events and should be probably avoided if possible. As far as the standard deviation filter is concerned, as shown in section 4.3, applying it at the x = 6 level, whether on a per stock or whole data basis, seems to perform well but it is not of much value if combined with a missing values filter and all other scenarios tested, actually dropped more probable extreme events than data errors. Therefore, it is not advisable to simply drop outliers existing at the tails of distributions without understanding the cause behind their existence. This leaves us with the rule of thumb filters. We combined the filters that performed optimally spread and additional return filter which accounts for reversals, along with a filter for removing the missing values. This resulted in dropping one hundred and two i.e. 63.4% of all probable data errors without removing any probable extreme events. At this point, a payoff has been made: in order to not drop any probable extreme events, we have foregone dropping some extra probable data errors because over scrubbing is a serious form of risk.[15] This highlights the struggle of optimal data cleaning, because researchers often dont have the time to check the reason behind the occurrence of an outlier, they end up removing probable extreme events in the quest to drop probable data errors. Thus, the researcher has to first determine what optimal filtering really means to him does it mean not dropping any probable extreme events albeit at the expense of keeping some data errors like done in this paper, or does it mean giving precedence to dropping maximum amount of data errors, albeit at the expense of dropping probable extreme events? In the latter case, statistical filters like trimming, win sorization and standard deviation method should also be carefully used. The limitations of this paper should also be recognized. Firstly, only two hundred outliers were analyzed due to time constraint, maybe, future research in the area can look at a larger sample to get more insightful results. Secondly, other variables can also be looked at in addition to depth, volume, spread and returns and more popular filters can be applied and tested on them. Moreover, a different definition can be used to define an outlier or to select the sample for e.g. the two hundred outliers could have been selected randomly or based on their level of extremeness but close attention must be paid to avoid sample biases. Future research in this field should perhaps, also focus on developing more objective filters and method of classifying outliers as probable extreme events. It should also look into the impact of using the above[16]two approaches of optimal filtering on the results of empirical research for e.g. on robust regressions, to verify which approach of optimal filtering performs the best. Table 1: Outcome of Rule of Thumb Filters Applied Table 2: Outcome of Winsorization Filters Applied Table 3: Outcome of Standard Deviation Filters Applied Figure 3: Kernel Distribution before and after log transformation 3.1 Depth 3.2 Effective Spread 3.3 Quoted Spread 3.4 Volume   Ãƒâ€šÃ‚   Figure 4. Kernel Distribution before and after log transformation of transaction price   Ãƒâ€šÃ‚   References Bollerslev, T./Hood, B./Huss, J./Pedersen, L. (2016): Risk Everywhere: Modeling and Managing Volatility, Duke University, Working Paper, p. 59. Brownlees, C. T/Gallo, M. G. (2006): Financial Econometric Analysis at Ultra-High Frequency: Data Handling Concerns. SSRN Electronic Journal, p. 6 Chordia, T./Roll, R./Subrahmanyam, A (2000): Market Liquidity and Trading Activity, SSRN Electronic Journal 5, p. 5 Dacorogna, M./Mà ¼ller U./Nagler R./Olsen R./Pictet, O (1993): A geographical model for the daily and weekly seasonal volatility in the foreign exchange market, Journal of International Money and Finance, p. 83-84 Dacorogna, M (2008): An introduction to high-frequency finance, Academic Press, San Diego, p. 85 Eckbo, B. E. (2008): Handbook of Empirical Corporate Finance SET, Google Books, p. 172 https://books.google.co.uk/books?isbn=0080559565 Falkenberry, T. N. (2002): High Frequency Data Filtering, S3 Amazon, https://s3-us-west-2.amazonaws.com/tick-data-s3/pdf/Tick_Data_Filtering_White_Paper.pdf Goodhart, C./Figliuoli, L. (1991): Every minute counts in financial markets, Journal of International Money and Finance 10.1 Green, C. G./Martin D. (2015): Diagnosing the Presence of Multivariate Outliers in Fundamental Factor Data using Calibrated Robust Mahalanobis Distances. University of Washington, Working paper, p. 2, 8 Hussain, S. M (2011): The Intraday Behaviour of Bid-Ask Spreads, Trading Volume and Return Volatility: Evidence from DAX30, International Journal of Economics and Finance, p. 2 Laurent, A. G. (1963): The Lognormal Distribution and the Translation Method: Description and Estimation Problems. Journal of the American Statistical Association, p. 1 Leys, C./Klein O./Bernard P./Licata L. (2013):   Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median, Journal of Experimental Social Psychology, p. 764 Scharnowski, S. (2016): Extreme Event or Data Error?, Presentation of Seminar Topics (Market Microstructure), Mannheim, Presentation Seo, S. (2006): A Review and Comparison of Methods for Detecting Outliers in Univariate Data Sets, University of Pittsburg, Thesis, p. 6 Verousis, T./Gwilym O. (2010): An improved algorithm for cleaning Ultra High-Frequency data, Journal of Derivativ

Chaucers Canterbury Tales - The Nun’s Priest’s Tale :: Nun’s Priest’s Tale Essays

The Nun’s Priest’s Tale      The tale told by the Nun’s Priest is a fable or story with animals as the main characters and usually ends with a moral of some sort. This tale takes place on the farm of and old, poor widow. All that she posses can be summed up in a few lines. It is among her possessions that we find the rooster Chanticleer, who’s crowing is more precise than any clock and a voice that was jollier than any church organ.   The tale is told from the point-of-view of Chanticleer. One night he has the dream of a fox pursuing him and killing him. When he wakes, his wife, Lady Pertelote tries to convince him that it was just a dream and that it has no meaning.   Chanticleer argues with Pertelote and produces a tale of his own. This is the tale of two young travelers who in search of lodging must separate. One of the travelers found a bed in a farmer’s barn, the other in a lodge of some type. In the night, one of the travelers hears his friend in a dream calling out for help. He says that he is to be murdered for his money and his body is to be hidden in a dung cart at the west end of town. In the morning, the man goes in search of is friend and discovers him dead in exact location that he learned from his dream. Chanticleer uses this story to try and prove to Pertelote that dream have meaning.   The fox enters the scene the next morning as the hens and Chanticleer come down from their roost to feed and relax in the sun. The fox waits and watches Chanticleer and the hen’s for a good bit of the day from a nearby cabbage patch. However, right before he is about to crow, Chanticleer catches a glimpse of the fox and silences himself. The fox sensing that his meal maybe lost quickly comes up with a new scheme to trick Chanticleer. He instantly claims to be friendly and means no harm towards Chanticleer. He then uses flattery on Chanticleer, convincing him that the fox came only to hear his beautiful voice and how he had been waiting so long to hear it, this tricks Chanticleer into lowering his guard, it is at that moment that the fox strikes and runs with the almost lifeless body of Chanticleer towards the woods.

Explain the Potential Effects of Five Different Influences on an Individual

Explain the effects of five different life stages on the development of an individual. In today’s society it is almost impossible to grow up without being affected by the things around us. During an individual’s development there are five main key factors that affect them. These include: * Genetic factors * Socio- economic factors * Biological factors * Lifestyle factors * Environmental factors To begin with, there are 23 chromosomes in each cell found in the nucleus.It is these cells that determine the hereditary of a child as well as the sex during conception. Genetic diseases make up a large proportion of the total disease burden, for example 50% of deafness is due to genetics as well as 40-50% of miscarriages. Another disease that is generally caused by genetics is cystic fibrosis which is caused by a defective gene. As many as four people in the UK have this gene; however the gene is recessive meaning children will only get the gene if both parents are carriers. Cy stic fibrosis results in a sticky mucus forming in the lungs, pancreas and intestines, and in the past it meant a low life expectancy but today a lot can be done to solve this. In relation to Jason asthma also seen as a genetic disease and Jason’s development of asthma could be a result of his Mother smoking of drinking during pregnancy. However the causes of asthma may not just be due to genetic inheritance but also due to environmental factors which I will further later in the essay. Closely related to this and sometimes somewhat confused are biological factors.Biological factors also start from conception and one of these is Foetal Alcohol Syndrome (FAS) which is the biggest cause of mental handicap in the western world as well as the only one that is 100% preventable. The first stages in prenatal are the most important and again this relates to Jason as one of the effects of FAS is falling behind with leaning development and Jason found school hard to cope with but in the end caught up, however this could be as a result of many reason and it does not mean his mother consumed alcohol during pregnancy. A woman’s diet also affects the foetus as well as during breast eeding, research shows that if a mother had a high sugar diet her child is more likely to develop high cholesterol and a higher risk of heart disease in later life. Malnutrition or a lack of health food could also lead to poor health for the child in later life Jason’s mother also could have smoke during pregnancy leading to his asthma. Socioeconomic factors also heavy a heavy impact on development for example if a family is poor there is a higher risk a child will suffer from malnutrition as well as the fact in later life they will want to do better.Within the factor is values and attitudes, a prime example of this would be education, if parents have went to college they will more than likely want their children to as well, however it could also be argued that if parent haven ’t went to college they will want their children to achieve more in life than they did. In relation to Jason although he may want to go to college he may not be able to as he does not come from a well off family and may not be able to afford to do so. Continuing on from this is Lifestyle factors, which is a highly varied subject.For example a person who has been brought up in a poor household will be completely different that one brought up in a well off household. Nutrition and dietary is a big part of this however it is only if you are reasonably well off that you can afford to buy the healthier choices. As well as this alcohol intake and the misuse of substances can affect you and this happens to be on of the most controversial issues in society. It can have social, physical and mental effects on an individual from the expense, to long term health risks or the fact it could lead to depression.In relation to Jason’s lifestyle the fact he may not be able to afford col lege and the fact he lives in a high density housing estate may mean he is not that well off money wise meaning he may not get the healthiest foods and could also have been bullied not only as a result of his asthma but also as he cannot afford the things some of the other children could. Lastly is environmental factors, this has a huge impact on our development and research shows that 1 out of 5 children are malnourished. However this is a varied range of situations.Exposure to pollution as well as poor housing can lead to health problems for example carbon monoxide takes away oxygen from the red blood cells as they have a higher affinity for it which can then lead to heart disease and nitrogen and sulphur dioxide can lead to lung disease as these gases irritate the lungs. This is not the only environmental effect however, Jason got bullied a lot at school which means he could suffer from depression and will have low self-esteem in later life. As well as this fact when he was 8 his parents divorced which leaves a great mental effect on individuals and at his age it was a lot to go through.Separation can lead to anger issues for the child as well as abandonment issues and this could even go on to affect Jason in later life it could even lead to AD (anxiety disorder) which could be as the child’s routine is disrupted. Jason’s parents’ divorce may be the reason he fell behind at school and when he reached adolescence he adapted which may be why his school work improved. Environmental factors can affect development both physically and mentally. In conclusion all five factors can affect an individual’s development as shown through the example of Jason, and all five have different effects yet still equally importantBibliography * http://www. livestrong. com/article/217996-factors-affecting-early-child-development/ *http://www. psychologytoday. com/blog/surviving-your-childs-adolescence/201112/the-impact-divorce-young-children-and-adolesc ents *http://www. ucl. ac. uk/support-pages/information/alcohol-and-drug-abuse *http://www. lbl. gov/Education/ELSI/Frames/pollution-health-effects-f. html *http://uk-air. defra. gov. uk/air-pollution/effects *http://www. succeedsocially. com/lifestyle *Class notes *Moodle

12 requisitos para patrocinio de visa niñera para EE.UU

12 requisitos para patrocinio de visa nià ±era para EE.UU Trabajar como nià ±era (au pair) en Estados Unidos requiere seguir unas directrices estrictas establecidas por el gobierno para sacar la visa J-1 para ese programa especà ­fico. Adems, las agencias autorizadas para contratar extranjeros para esa posicià ³n pueden establecer sus propias preferencias. En este artà ­culo se informa sobre los requisitos legales y tambià ©n sobre los que habitualmente piden las agencias autorizadas para contratar. Adems, se mencionan otras programas alternativos pensados para jà ³venes extranjeros que quieren pasar una temporada corta en Estados Unidos aprendiendo inglà ©s y disfrutando la experiencia americana. Puntos clave La visa para nià ±eras extranjeras es la J-1, programa de au-pairSolamente agencias autorizadas por el gobierno de EE.UU. pueden patrocinar la visaAunque se habla de nià ±era, lo cierto es que se puede ser mujer o varà ³nEl tiempo mà ­nimo de estancia en EE.UU. es de un aà ±o y el mximo de 2. 8 requisitos legales de visa J-1 para trabajar de nià ±era en EE.UU. Para poder aplicar con à ©xito para una visa J-1 en el programa especà ­fico para nià ±eras es obligatorio cumplir con requisitos de edad, estudios y experiencia. Los principales son los ocho siguientes: Tener entre 18 y 26 aà ±os de edad, gozar de buena salud, no tener rà ©cord criminal y haber completado, como mà ­nimo, los estudios de secundaria. Adems, es necesario poder demostrar experiencia de al menos 200 horas de trabajo cuidando nià ±os. La manera de probarlo admite muchas variantes, desde haber trabajado en una escuela o guarderà ­a a tener muchos hermanos pequeà ±os o participar como voluntario en campamentos infantiles, escuelas dominicales, etc. Tambià ©n se exige no haber estado previamente en Estados Unidos como au pair, es decir, nià ±era. Asimismo, es necesario entender y hablar, como mà ­nimo, un inglà ©s de nivel intermedio. Finalmente, las reglas de esta visa exige a los candidatos comprometerse a vivir con una familia estadounidense un mà ­nimo de un aà ±o. Este periodo se puede extender por tres, seis o doce meses. 4 requisitos de las agencias para patrocinar la visa de nià ±era Estos requisitos son obligatorios, desde un punto de vista legal, pero lo cierto es que para las agencias es mucho ms fcil colocar en una familia a una au pair que los reà ºna y, por lo tanto, suelen pedirlos. Entre los ms frecuentes destacan los siguientes: En primer lugar, ser mujer. Es un hecho, las familias prefieren a una muchacha antes que a un varà ³n para trabajar en sus casas cuidando de los nià ±os. En segundo lugar, tener licencia de manejar. Hay que tener en cuenta que en muchà ­simos lugares de Estados Unidos el transporte pà ºblico puede ser inexistente y que las distancias son enormes. Facilita mucho la contratacià ³n el saber que una persona sabe manejar. En tercer lugar, ser flexible en cuanto al rea geogrfica en la que se quiere vivir y tambià ©n en relacià ³n al nà ºmero de hijos que debe tener la familia de acogida. Una au pair que quiera ser contratada en zonas muy populares como California o Nueva York y en casas con un sà ³lo menor puede tener ms dificultades a la hora de encontrar una familia. Y, en cuarto lugar, no fumar. Estos son los Estados Unidos de Amà ©rica. Va a ser realmente difà ­cil y tomar su tiempo antes de que se encuentre a una familia dispuesta a contratar a una nià ±era fumadora. A tener muy en cuenta para evitar problemas migratorios Para los extranjeros sin papeles para trabajar en EE.UU. sà ³lo es posible pasar una temporada como au pair en Estados Unidos participando en el programa a travà ©s de agencias autorizadas por el gobierno. Si hay una familia dispuesta a patrocinar a una extranjera como nià ±era, la familia deber hablar con una agencia autorizada para patrocinar visas J-1 de au-pair y el papeleo debe hacerse a travà ©s de ellas. En otras palabras, una familia estadounidense no puede patrocinar directamente una visa J-1 de esta categorà ­a. Para asegurarse de que se trata con una agencia legà ­tima y que no se est ante un caso de fraude, se recomienda verificar que est incluida en el listado de la pgina oficial del Departamento de Estado para estos efectos. Si es una agencia ubicada en fuera de los Estados Unidos, verificar para quà © agencia autorizada en Estados Unidos est trabajando. Las au pairs recibirn una visa J, que son de intercambio. Debern respetar sus caracterà ­sticas generales que aplican a todos los programas incluidos en esa categorà ­a de visa.   Adems, para recibir la visa no es suficiente con tener en mano la oferta de la familia y la intervencià ³n de la agencia autorizada. Si se es inelegible o inadmisible para recibir la visa, la peticià ³n de esta ser negada por el oficial consular. Por à ºltimo, se recomienda encarecidamente no mentir sobre los conocimientos de inglà ©s. En el momento de la entrevista en la Embajada o consulado se determinar si realmente se tiene el nivel de inglà ©s requerido. Si no se tiene, la visa ser denegada, el dinero de la aplicacià ³n no se regresa y la mentira quedar en el rà ©cord de la solicitante para las autoridades consulares y migratorias de Estados Unidos. Opciones a la visa de nià ±era Los jà ³venes de otros paà ­ses que desean pasar una temporada en Estados Unidos trabajando y aprendiendo inglà ©s pueden optar a distintos tipos de programas dentro de la categorà ­a de visas J-1, por ejemplo: Prcticas o pasantà ­as en una ONG.Estudiante de high school durante un aà ±o acadà ©mico completoPrcticas profesionalesVisa para trabajar y viajar en veranoTrabajo en un Summer Camp  como staff de un campamento de verano. Este es un artà ­culo informativo. No es asesorà ­a legal.

Seattles Suburbs History. Essays - Century 21 Exposition

Seattles Suburbs History. Essays - Century 21 Exposition Seattle's Suburbs History. Preamble Learning about what has changed has made us realize more than ever how constant change is. The way we see things around us is not the way they always have been and more important, are only an intermediate stage to where ever they will be tomorrow. Talking to people who saw what has changed to get here allows us to see the direction we have come, and finally the direction we are going. Outlined in this paper are six different angles viewing different parts of the past that collectively help us to find that direction. World's Fair Three of the prime events that attracted people to Washington in the 20th Century were the World's Fairs. The first of Washington's World Fairs was the Alaskan-Yukon-Pacific Exposition in 1909, which was located on the 250 acre University of Washington campus. The fantastic buildings, most of which still stand today, were designed by the famous Olmstead Brothers' landscape and architecture firm for the $150 million project. Among the celebrities at the exposition were President Howard Taft and industrialist Henry Ford. The Alaskan-Yukon-Pacific exposition opened on June 1st and closed on October 15th . However, no A-Y-P structure was as prominent as the futuristic Seattle Space Needle of the Century 21 World's Fair in Seattle in the year 1962. This, along with the wondrous monorail and Pacific Science Center were all leftover from the great fair. Lasting for almost six months, the '62 Seattle World's Fair attracted approximately ten million visitors, and, as Jack Crawford put it, "It was one busy place." Among the exhibits were the various ethnic and state booths, the hydroelectric waterfall, and the great fountain made from plumbing parts, which has just recently been remodeled. All during the fair, various acts played in the Seattle Opera House. Inside the future exhibit was the famous Bubble-ator elevator, which now resides down in sunny Redondo, California as greenhouse. A lot of wonderful memories were produced at this illustrious event; Gene Duarte recalls, "I remember hearing East Indian music for the first time and falling down on the floor laughing. I was se! ven, and it was the funniest thing I had ever heard." As civic boosters had had hoped, it brought national attention to Seattle, and in spite of early problems, the 1962 Seattle World's Fair became a financial success. Twelve years later, Spokane held EXPO '74 for which the city tore down old buildings and cleaned up the pollution, the theme being the environment. 'Nam 'Nam. What most people seem to remember about the Vietnam War here in Seattle were the big protests. The protesters opposed military escalation and fought to bring the GI's home. Kathy Duarte-Wilson remarks, "People were very scared of being drafted. We wore POW bracelets in honor of those who went to war. They were almost a fad. Then there were the flower children," she laughs, "I remember wanting to be one when I grew up." Some GI's were pulled out of Vietnam when President Nixon's first troop reduction order was acted upon in July of 1969 at McChord Air Force Base. Two days later there was a combination military parade, welcome home celebration, plus antiwar protest filling the streets of Seattle. The antiwar campaign really heated up in the May of 1970. Days of protesting went on against the bombing of Cambodia and the killings of four student demonstrators by national guardsmen in Kent State University in Ohio and two killed by state police in Jackson State College in Mississippi. Ten thousand protesters blocked Interstate-5 in Seattle in a march from the University of Washington to the federal courthouse down town. Boeing Depression When asked about a time remembered for hardship and kindness, the Boeing depression is often the top of the list. When the Boeing company went through hardship the entire state felt the repercussions. When nearly ? of all the people in the region worked for Boeing, layoffs created vast unemployment causing a severe regional depression. "Last one out of Seattle, please turn out the lights" read the sign off the side of the highway, echoing much of the feelings of the region's population. The utter lack of jobs and opportunity lead to a large migration of people away from

Discussion on Learning Experiment Essay Example | Topics and Well Written Essays - 500 words

Discussion on Learning Experiment - Essay Example However, there were certain theories that did predict that no difference would occur in the aformentioned conditions or target cues. This discussion will outline these thoeries. It will likewise discuss why Cue A in the experiment caused impairments in causal judgement about Cue B relative to the other conditions. One of the thoerists that predicted a noticeable difference between target cues E and G was Kamin. His study proposed that in a blocking experiment, if the US is changed during Stage 2 (e.g., by making it significantly stronger or weaker), then significant new learning can occur about the added element (CS2) of the Compound CS, and strong conditioned responses to CS2 will be expressed in Stage 3. The "surprising" change in the US supports formation of new associations during Stage 2, since CS2 is the "best predictor" of the surprising change in the US. However, Kamin also suggested that this will not be the case when for target cue E. He proposed a difference between the two situations which was not demonstrated in the experiment conducted. With regards to the rational behind the ability of cue A to impair causal judgement relative to target cue B, this can best be explained by refering to the associative learning theory promulgated by Rescorla and Wagner.

Ku Klux Klan Research Paper Example | Topics and Well Written Essays - 1250 words

Ku Klux Klan - Research Paper Example The original name of the club was from the Greek word Kuklos meaning ‘for circle and cycle are formed’. The confederates later modified the word to Kuklux adding the word clan at the end since all the founders were of Scottish descent. Kuklux Clan became the name of the organization, later it was divided into three words and a ‘K’ used for each word. The name therefore changed to Ku Klux Klan. There are other suggestions about the origin of the name. Romine states that members of the Klan could have become familiar with the mythology of the ‘God of Light’ who was called Cukulcan since many volunteer troops went to the Mexican War from Tennessee and members of the early Klan sometimes called themselves the sons of light(Quarles 32). Another story told that the name did not come from a Greek word meaning circle but from the two phased cocking sound of a shotgun. Many different stories have been told and written creating mystery about the organizat ion which later came to be known as the ‘Invisible Empire’. The six former confederate members met in the law office of Judge Thomas M. Jones located eighty miles south of Nashville in Pulaski, south central Tennessee. One of the town’s prewar buildings has a plaque marking the occasion. The Klan is an American institution though it has been exported to other lands by racists. The formation of the club was not a major historical event; the group was informally constituted during the first meeting. Directing and planning of the Klan activities began later as further meetings developed goals and objectives, leadership titles were chosen and organizational rules instituted (Quarles 30). All evidence supports the fact that the founding of the Klan was innocent with no ulterior motive or effect. Many scholars also believe that the beginning was innocent; this is because of the tricks that the early Klansmen performed. The first noted activity of the Klan was that it was a brotherly association. Some members used childish methods of pursuing victims. The original purpose of the young confederate veterans was to scare black adults and cause trouble as an amusing way of passing time in a southern society that was altered and a destroyed economy. Klansmen dressed in white sheets and covered their horsemen in the same way. The Ku Klux Klan members believed that racial integration and racial equality of society was a threat and could destroy the white race. The Klan opposed public policies that promote social and political equality for historically disadvantaged groups like the blacks through antidiscrimination laws and affirmative action. Their beliefs were that the nature of mankind is unequal therefore hierarchical. Ku Klux Klan members described themselves as white Protestant Christians. Their primary objection was initially against blacks’ freedom and extension of rights to include blacks. This was after slavery in the United States was brought to an end. In terms of their strength in the political history of th e United States, the Klan has had three specific periods. The first Klan blossomed in the South in