Is It A Disease Of The Nation - 1337 Words

Jocelyn Pennington Freshman Composition Argumentative Paper 14 November 2014 Perfection is a Disease of the Nation Just like Beyoncà © says in her new song Pretty Hurts, you automatically see what you think is wrong , so therefore you want to fix it. But just because you fix it does not mean your satisfied. Plastic surgery has become the new fad in the 21st century. Your not satisfied with your nose? Get a nose job. Do you think your boobs are too small? Get a breast reduction. Are you tired of exercising and want a flat tummy now? Get lipo! Anything that you do not like with yourself, you can get that fixed. But once you get your problem area fixed, will you really be happy? Young†¦show more content†¦The second is a person that has lowered self-esteem due to an obvious part of their body that they have tried to deal with. There are some who are not suited for cosmetic surgery. Sometimes awkwardness and shame about the way one looks can prevent honest communication to one’s surgeon. Some examples of poor candidates for cosmetic surgery are the Idealist, the Chaotic, and the Jumper. The Idealist patient has unrealistic expectations of surgery,for example, an idealist patient may want to look like a famous person. The Chaotic patient is in a crisis and feels the need to be â€Å"fixed.† Most â€Å"chaotic† patients have been through a divorce , have experienced rejection, or have experienced some other type of life changing event. They often believe that having surgery will heal their grief. The â€Å"jumper† patient goes to surgeon after surgeon looking for the doctor that will tell them what they want to hear. Last ly, there is the pleaser. The â€Å"Pleaser† patient is the person who gets surgery because they believe that it will make someone else like or accept them. In other words, make them more pleasing to the eye. According to recent findings in psychology, patients who had not been satisfied with their previous surgeries, or patients who have a history with depression are less likely to be satisfied with the result of their surgeries. An estimated 7 to 15 percent of plastic surgery patients have body dysmorphic disorder, which is when a

The Movie Park Avenue Money, Power And The American Dream

The Truth behind a Dream It is easy for a person to put away a dream as a mere fantasy that cannot be achieved in life. For this reason, people never take the opportunity to evaluate the true facts behind that dream. In the documentary â€Å"Park Avenue: Money, Power and the American Dream† by Director Alex Gibney, an analysis of the true facts behind the ‘American dream’ is presented (Lee). Similarly, the poem â€Å"Dulce et Decorum Est† by Wilfred Owen speaks about the true facts of a war that people foolishly dream to go to for honor (Owen). The two are distinct in the sense of their nature. The first piece by Gibney is a documentary film while the second one by Owen is a poem. For this reason, they apply different strategies to put across their themes to their audiences. Despite the clear distinction between the two, there exist clear similarities in trying to analyze the true facts behind a dream. The documentary directed by Gibney, analyzes the true facts of the gap between the poor and the rich in the America dream. It shows that the gap has been increasing over the last thirty years. The Park Avenue that passes through Manhattan (where the rich stay) and South Bronx (where the poor stay) shows this distinction (Lee). The two are separated by a river making the gap appear smaller but in real sense it wide. The intended audience of this documentary is the American population, especially the rich. In the documentary, the tone is negative especially towards the rich people whoShow MoreRelatedStructural Racism And Racial Equity Analysis Essay1850 Words   |  8 Pagesa better life here in the states, â€Å"The American Dream†. However, is the â€Å"American Dream† really a dream or a nightmare for these immigrants. The whole thought of the American Dream is that everyone has an equal opportunity in America, but this is truly not the case. 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Time Series Free Essays

A time series is a set of observations, xi each one being recorded at a specific time t. After being recorded, these data are rigorously studied to develop a model. This model will then be used to produce future values, in other words, to make a forecast. We will write a custom essay sample on Time Series or any similar topic only for you Order Now Important Characteristics to Consider First When first looking at a time series, some questions must be asked:Does the time series has a trend or seasonality over time?Are their outliers? With time series data, the outliers are far away from the other data.Is there a long-run cycle or period?Is there constant variance over time? Essential of Good time series Data must be for a sufficient period Equal time ga Constant or normal period. Example1The following plot is a time series plot of the annual number of earthquakes in the world with seismic magnitude over 7.0, for 99 consecutive years. By a time series plot, we simply mean that the variable is plotted against time.Some features of the plot:There is no trend.The mean of the series is 20.2There is no seasonality as the data are annual data.There are no outliers. Example 2 The plot at the top of the next page shows a time series of quarterly production of beer in Australia for 18 years.Some important features are:There is an increasing trend.There is seasonality.There are no obvious outliers.The Components of Time SeriesThe components of time series are factors that can bring changes to the time series:Trend component, TtWhen there is an increase or a decrease over a long period of time in the data, then we say that there is a trend. Sometimes, a trend is said to be changing direction when it goes from an increasing trend to a decreasing one. It is the result of events such as price inflation, population growth or economic changes.Seasonal component, StA seasonal pattern exists when the time series exhibits regular fluctuations at specific time. It arises from influences such as natural conditions or social and cultural behaviors. For example, the sales of ice-cream are relatively high in summer. So, the salesman expects greater profit in summer than in winter. Cyclic component, CtIf the time series shows an up and down movement around a given period of time, it is said to have a cyclical pattern.Irregular component, ItIrregular components consist of changes that are unlikely to be repeated in a time series. Examples are floods, fires, earthquakes or cyclones.Combining the time series componentsTime series is a combination of the components which were discussed above. These components can be either combined additively or multiplicatively.Additive modelIt is linear, and the changes are made by the same amount over time.Yt = Tt + Ct + St + ItMultiplicative modelIt is non-linear such as quadratic or exponential, and the changes increase or decrease over time. Yt = Tt Ãâ€"Ct Ãâ€" St Ãâ€" ItUsesTime series can be useful in the following fields:StatisticsSignal processingEconometricsMathematical financeAstronomyEarthquake predictionsWeather forecastingImportance of Time series for businessesThere are many benefits of time series for business purposes:Helpful for study of past behaviorBusinessmen use time series to study the past behaviors and to see the trend of the sales or profit of their businesses. Helpful in forecastingTime series is a great tool for forecasting. Businesses can make a time series of the past strategies of their competitors and make an estimate of their future strategies. In this way, they make can built a better strategy and make more profits.Helpful in comparisonTime series can be used to calculate the trend of two or more branches of the same company and compare their performance. On their performances, rewards can be given. However, time series can have some limitations for a business. Sales forecasting relies on the past results to predict future expectations. But, if a company is new, there is a limited amount of data to make predictions. Even so, past results do not always indicate what the future sales will be.To fully understand this topic, we will work out this example.Example 2We will consider the actual arrival of passengers from an airport over the year 1949 to 1960. From these data, we will make a forecast. The first step is to plot the data and obtain descriptive measures such as trends or seasonal fluctuations.The second step is to check for the stationarity of the time series.StationarityA time series is said to be stationary if its mean and variance does not change over time. Obviously, not all the time series that we encounter are stationary. It is important because, most of the models we work on, assumes that the time series is stationary. If the time series has the same behavior over time, there will be a high probability that it will follow the same trend in the future.How to check for stationarity?For the graph that was plotted, we can see that it has an increasing trend with some seasonal pattern. But, it is not always evident to see whether a plot is increasing or has a seasonal trend. We can check for stationarity using the following:Plotting rolling statisticsWe plot the moving average or variance and see whether it changes with time. But, as it is a visual technique, we will take more consideration for the next test.Dickey-Fuller testIt is one of the statistical methods to check for stationarity. The null hypothesis is that the time series is non-stationary, and the alternative hypothesis is the converse.As shown below, the test consists of the test statistics and critical values at different significant levels. If the test statistics is less than the critical value, we reject the null hypothesis. Results of Dickey-Fuller Test: Test Statistic 0.815369p-value 0.991880#Lags Used 13.000000Number of Observations Used 130.000000Critical Value (1%) -3.481682Critical Value (5%) -2.884042Critical Value (10%) -2.578770According to the Dickey-Fuller test, the test statistics is less than the critical value. Therefore, the time series is not stationary. However, there are various methods to make a time series stationary.How to make a time series stationary?The assumption of stationarity is very important when modelling a time series, but most of the practical time series are not stationary. Eventually, we cannot make a time series one hundred percent stationary, most of the time, it will be with a confidence of 99%.Before going into detail, we will discuss on the reasons why the time series is not stationary. There are two major reasons to that, trend and seasonality.Having discuss the reasons, we will now talk about the techniques to make the time series stationary:TransformationLog transformation is probably the most commonly used form of transformation. Differencing Differencing is a widely used method to make the time series stationary. It is performed by subtracting the previous observation from the current one. When making the forecast, the process of differencing must be inverted to convert the data back to its original scale. This can be done by adding the difference value to the previous value.Using the Dickey-Fuller test we can see that the test statistic is -2.717131 and that the critical values at 1%, 5% and 10% are -3.482501, -2.884398 and -2.578960 respectivelyThe time series is stationary with 90% confidence. The second or third order differencing can be done to get better results. Decomposition In decomposition, the time series is divided into several components mainly trend, cyclical, seasonal and irregular components.The time series can sometimes be broken down into an additive or multiplicative model.We will assume a multiplicative model for our example.Since the trend and seasonality were separated from the residuals, we can check the stationarity of the residuals.Results of Dickey-Fuller Test is test statistic is -6.332387e+00 and the critical values at 1%, 5% and 10% are -3.485122e+00, -2.885538e+00 and -2.579569e+00 respectively. We can conclude that the time series is stationary at 99% confidence.Now, we can go forward with the forecasting.Forecasting the time seriesWe will fit this time series using the ARIMA model, ARIMA is an acronym that stands for Autoregressive Integrated Moving Average. It is a linear equation similar to a linear regression. The first goal is to find the values of the predictors (p, d, q), but before finding these values, two situations in stationarity must be discussed. A strictly stationary series without any dependence among the values. In this case, we can model the residual as white noise.The second case is a series with significant dependency among the values.The predictors mainly depend on the parameters (p, d, q) of the ARIMA model:Number of AR(Auto-Regressive) terms (p)It is the number of lag observation that were included in the model. This term helps to incorporate the effect of the past values into the model. Number of MA (Moving Average) terms (q)It is the size of the moving average window, that is, this term sets the error of the model as a linear combination of the error values observed at previous time points in the past.Number of differences(d)The number of times that the raw observations are differenced.In order to obtain the values of p and q, we will use the following two plots:Autocorrelation Function, ACFThis function will measure the correlation of the time series with its lagged version. Partial Autocorrelation Function, PACFThis function measures the correlation between the time series with a lagged version of itself, controlling the values of the time series at all shorter lagsIn the ACF and PACF plots, the dotted lines are the confidence interval, these values are p and q. The value of p is obtained from the PACF plot and the value of q is obtained from the ACF plot. We can see that both p and q are 2.Now, that we have obtained p and q, we will make three different ARIMA model: AR, MA and the combined model. The RSS of each of the model will be given.AR modelMA modelCombined modelFrom the plots, it is clearly shown that the RSS of AR and MA are the same and that of the combined is much better. As the combined model give a better result, the following steps will take the values back to its original scale.The predicted results are stored.The differencing is converted the log scale. This can be done by adding the differences consecutively to the base numbers.The exponent is taken and is compared to the original scale.Therefore, we have the final result. References Aarshay Jain(2016) A comprehensive beginner’s guide to create a Time Series Forecast (with Codes in Python) [WWW] Available from https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/ [Accessed 14/04/18] Maxime Phillot (2017) How do I interpret the results in an augmented Dickey-Fuller test? [WWW] Available from https://www.quora.com/How-do-I-interpret-the-results-in-an-augmented-Dickey-Fuller-test [Accessed 23/04/18] Jason Brownlee (2016) What Is Time Series Forecasting? [WWW] Available from https://machinelearningmastery.com/time-series-forecasting/ [Accessed 23/04/18] Chris St.Jeor and Sean Ankenbruck (2018) Time Series for dummies- The 3 step process [WWW] Available from https://www.kdnuggets.com/2018/03/time-series-dummies-3-step-process.html [Accessed 22/04/18] Pennsylvania state university (n. d) Overview of Time Series Characteristics [WWW] Available from https://onlinecourses.science.psu.edu/stat510/node/47 [Accessed 22/04/18] How to cite Time Series, Papers Time Series Free Essays Introduction A time series is a set of observations, xi each one being recorded at a specific time t. After being recorded, these data are rigorously studied to develop a model. This model will then be used to construct future values, in other words, to make a forecast. We will write a custom essay sample on Time Series or any similar topic only for you Order Now When looking at a time series, some questions must be asked:Does the time series have a trend or seasonality?Are their outliers? Is there constant variance over time?Essential of Good time seriesThe data must be long enough.There must be equal time gap.There must be a normal period.Example1The following plot is a time series plot of the annual number of earthquakes in the world with seismic magnitude over 7.0, for 99 consecutive years. By a time series plot, we simply mean that the variable is plotted against time.Some features of the plot:There is no trend.The mean of the series is 20.2.There is no seasonality as the data are annual data.There are no outliers.Example 2 This shows a time series of quarterly production of beer in Australia for 18 years.Some features are:There is an increasing trend. There is seasonality.There are no outliers.The Components of Time SeriesThe components of time series are factors that can bring changes to the time series:Trend component, TtWhen there is an increase or a decrease over a long period of time in the data, then we say that there is a trend. Sometimes, a trend is said to be changing direction when it goes from an increasing trend to a decreasing one. It is the result of events such as price inflation, population growth or economic changes. Seasonal component, StA seasonal pattern exists when the time series exhibits regular variations at specific time. It arises from influences such as natural conditions or social and cultural behaviors. For example, the sales of ice-cream are relatively high in summer. So, the salesman expects greater profit in summer than in winter. Cyclic component, CtIf the time series shows an up and down movement around a given period of time, it is said to have a cyclical pattern.Irregular component, ItIrregular components consist of changes that are unlikely to be repeated in a time series. Examples are floods, fires, earthquakes or cyclones.Combining the time series componentsTime series is a combination of the components which were discussed above. These components can be either combined additively or multiplicatively.Additive modelIt is linear, and the changes are made by the same amount over time.Yt = Tt + Ct + St + ItMultiplicative modelIt is non-linear such as quadratic or exponential, and the changes increase or decrease over time.Yt = Tt Ãâ€"Ct Ãâ€" St Ãâ€" ItUsesTime series can be useful in the following fields: StatisticsSignal processingEconometricsMathematical financeAstronomyEarthquake predictionsWeather forecastingImportance of Time series for businessesThere are many benefits of time series for business purposes:Helpful for study of past behaviorBusinessmen use time series to study the past behaviors and to see the trend of the sales or profit of their businesses. Helpful in forecastingTime series is a great tool for forecasting. Businesses can make a time series of the past strategies of their competitors and make an estimate of their future strategies. In this way, they make can built a better strategy and make more profits.Helpful in comparisonTime series can be used to calculate the trend of two or more branches of the same company and compare their performance. On their performances, rewards can be given. However, time series can have some limitations for a business. Sales forecasting relies on the past results to predict future expectations. But, if a company is new, there is a limited amount of data to make predictions. Even so, past results do not always indicate what the future sales will be.To fully understand this topic, we will work out this example. Example 2We will consider the actual arrival of passengers from an airport over the year 1949 to 1960. From these data, we will make a forecast.The first step is to plot the data and obtain descriptive measures such as trends or seasonal fluctuations.The second step is to check for the stationarity of the time series.StationarityA time series is said to be stationary if its mean and variance does not change over time. Obviously, not all the time series that we encounter are stationary. It is important because, most of the models we work on, assumes that the time series is stationary. If the time series has the same behavior over time, there will be a high probability that it will follow the same trend in the future.How to check for stationarity?For the graph that was plotted, we can see that it has an increasing trend with some seasonal pattern. But, it is not always evident to see whether a plot is increasing or has a seasonal trend. We can check for stationarity using the following:Plotting rolling statisticsWe plot the moving average or variance and see whether it changes with time. But, as it is a visual technique, we will take more consideration for the next test. Dickey-Fuller testIt is one of the statistical methods to check for stationarity. The null hypothesis is that the time series is non-stationary, and the alternative hypothesis is the converse.As shown below, the test consists of the test statistics and critical values at different significant levels. If the test statistics is less than the critical value, we reject the null hypothesis. Results of Dickey-Fuller Test: Test Statistic 0.815369p-value 0.991880#Lags Used 13.000000Number of Observations Used 130.000000Critical Value (1%) -3.481682Critical Value (5%) -2.884042Critical Value (10%) -2.578770According to the Dickey-Fuller test, the test statistics is less than the critical value. Therefore, the time series is not stationary. However, there are various methods to make a time series stationary.How to make a time series stationary?The assumption of stationarity is very important when modelling a time series, but most of the practical time series are not stationary. Eventually, we cannot make a time series one hundred percent stationary, most of the time, it will be with a confidence of 99%.Before going into detail, we will discuss on the reasons why the time series is not stationary. There are two major reasons to that, trend and seasonality.Having discuss the reasons, we will now talk about the techniques to make the time series stationary:TransformationLog transformation is probably the most commonly used form of transformation. DifferencingDifferencing is a widely used method to make the time series stationary. It is performed by subtracting the previous observation from the current one. When making the forecast, the process of differencing must be inverted to convert the data back to its original scale. This can be done by adding the difference value to the previous value. Using the Dickey-Fuller test we can see that the test statistic is -2.717131 and that the critical values at 1%, 5% and 10% are -3.482501, -2.884398 and -2.578960 respectivelyThe time series is stationary with 90% confidence. The second or third order differencing can be done to get better results.DecompositionIn decomposition, the time series is divided into several components mainly trend, cyclical, seasonal and irregular components. The time series can sometimes be broken down into an additive or multiplicative model.We will assume a multiplicative model for our example.Since the trend and seasonality were separated from the residuals, we can check the stationarity of the residuals.Results of Dickey-Fuller Test is test statistic is -6.332387e+00 and the critical values at 1%, 5% and 10% are -3.485122e+00, -2.885538e+00 and -2.579569e+00 respectively. We can conclude that the time series is stationary at 99% confidence.Now, we can go forward with the forecasting.Forecasting the time seriesWe will fit this time series using the ARIMA model, ARIMA is an acronym that stands for Autoregressive Integrated Moving Average. It is a linear equation similar to a linear regression. The first goal is to find the values of the predictors (p, d, q), but before finding these values, two situations in stationarity must be discussed.A strictly stationary series without any dependence among the values. In this case, we can model the residual as white noise.The second case is a series with significant dependency among the values. The predictors mainly depend on the parameters (p, d, q) of the ARIMA model:Number of AR(Auto-Regressive) terms (p)It is the number of lag observation that were included in the model. This term helps to incorporate the effect of the past values into the model.Number of MA (Moving Average) terms (q)It is the size of the moving average window, that is, this term sets the error of the model as a linear combination of the error values observed at previous time points in the past. Number of differences(d)The number of times that the raw observations are differenced.In order to obtain the values of p and q, we will use the following two plots:Autocorrelation Function, ACFThis function will measure the correlation of the time series with its lagged version. Partial Autocorrelation Function, PACFThis function measures the correlation between the time series with a lagged version of itself, controlling the values of the time series at all shorter lagsIn the ACF and PACF plots, the dotted lines are the confidence interval, these values are p and q. The value of p is obtained from the PACF plot and the value of q is obtained from the ACF plot. We can see that both p and q are 2. Now, that we have obtained p and q, we will make three different ARIMA model: AR, MA and the combined model. The RSS of each of the model will be given.AR modelMA modelCombined modelFrom the plots, it is clearly shown that the RSS of AR and MA are the same and that of the combined is much better. As the combined model give a better result, the following steps will take the values back to its original scale. The predicted results are stored.The differencing is converted the log scale. This can be done by adding the differences consecutively to the base numbers.The exponent is taken and is compared to the original scale.Therefore, we have the final result. References Aarshay Jain(2016) A comprehensive beginner’s guide to create a Time Series Forecast (with Codes in Python) [WWW] Available from https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/ [Accessed 14/04/18]Maxime Phillot (2017) How do I interpret the results in an augmented Dickey-Fuller test? [WWW] Available from https://www.quora.com/How-do-I-interpret-the-results-in-an-augmented-Dickey-Fuller-test [Accessed 23/04/18]Jason Brownlee (2016) What Is Time Series Forecasting? [WWW] Available from https://machinelearningmastery.com/time-series-forecasting/ [Accessed 23/04/18]Chris St.Jeor and Sean Ankenbruck (2018) Time Series for dummies- The 3 step process [WWW] Available from https://www.kdnuggets.com/2018/03/time-series-dummies-3-step-process.html [Accessed 22/04/18] Pennsylvania state university (n. d) Overview of Time Series Characteristics [WWW] Available from https://onlinecourses.science.psu.edu/stat510/node/47 [Accessed 22/04/18] How to cite Time Series, Papers

Review Of Riverdance Essay Example For Students

Review Of Riverdance Essay From the moment I started to get near the Gershwin Theatre I felt some kind of uplifting energy inside of me. As soon as I came to the doors I saw lot of people waiting to get in. All of us came for the same reason to see the Riverdance. I felt very enthusiastic as I walked down the lobby to the escalators. The atmosphere of the theatre was solemn: coat-checkers, all people well dressed, everyone is smiling and expecting the event. As I sat down on my seat, I started to look around. It was a big auditorium with a seats arranged so everyone will see the event no matter where you seat. Gershwin Theatre is the proscenium kind of theatre. The stage didnt have any particular decorations that will catch your eye, so I flipped the playbill to get some information before the beginning. As the lights went down, from the first minute of a play I was swept away with a beautiful music and stayed like this until the last minute of the show. It seemed for me, though the musicians are the heart of the play. They brought life and energy. Some of these musicians were actors too. For example, one young lady who played violin came on to the stage a few times in the show, and danced and played solo, and with the rest of dancers at the same time. Music itself, which was a folk Irish transformed to something modern, was fantastic. A cappella singers also perform as a sort of chorus. Throughout the play we were able to hear the voice of unseen narrator, who were telling an impressionistic story of the Irish. The lighting pointed out the important moments and people at the write time. It did not have any special lighting effects and it really didnt need to. All together, lighting and sound created some feeling of spirituality for the show. Scenery of the play was very simple. It didnt use any extravagant decorations. Everything was very simple but it didnt give a feeling of emptiness. I felt that it was good because it didnt disturb attention of an audience. However, it was giving very basic information to what is going on a stage. The colors of the scenery corresponded to the scene. It helped the audience to feel what the dance is about. Most of the decorations were shown as symbolic elements that gave a feeling of appropriateness. Riverdance tells us the story of Irish history and dance in the abstract, mixing Irish step dancing with other folk forms like African American tap, Russian folk and Spanish flamenco. Production of the dances began slow at the first moment and then was getting faster and faster. I felt that I have no time to relax, dancers were holding all the attention. The piece with the Irish dance required from a few people on stage to a full stage of dancers. At the moments when everyone danced on the stage, the movements were so synchronized that it was giving a feeling that everyone flows together as a unit. With its movements dancers were able to show the mood of dance. I were able to laugh, feel joy and sadness. I think it was a most important thing for dancers to transform the feeling of their mood to the audience. What I also liked the most is that dancers did not dance with a stone look on their faces while dancing. Their expression changed to what the dance required it to change. Costumes of the dancers were nothing extravagant, but simple and good. It reflected the period of the play. Style of the costumes depended on the dance. If the dance was Russian folk costumes were right accordingly to it. The haircut corresponded to the dance either. When it was a Spanish flamenco the design of the dancers hair was the one that all the traditional dancers of this type of dance wear. The color of the costumes reflected the dance and story. I felt that director John McColgan completed a task not from an easy one. This show did not contain much of a plot and still director has been able to bring the feeling of unity. Everything: dancers, singers, light, and sound moved together. And production was not only technically correct, it were able to give a feeling to the audience. Pace of the play was very correct. It grasped attention of the audience bit by bit, but then it was holding all the attention to the very end. Rhythm was correctly chosen also. It changed from the fa st tones to the more slower ones and vice versa, so the spectator were able to rest a little without loosing the flow of the emotions. I also think that director used all his available space correctly. Even though there was not much of a decoration, there was not a feeling of emptiness on a stage. Instead of scenery free space was filled up with people as much as it needed to. .ub36b6b7f55aab767a4795c2ef99cc4f8 , .ub36b6b7f55aab767a4795c2ef99cc4f8 .postImageUrl , .ub36b6b7f55aab767a4795c2ef99cc4f8 .centered-text-area { min-height: 80px; position: relative; } .ub36b6b7f55aab767a4795c2ef99cc4f8 , .ub36b6b7f55aab767a4795c2ef99cc4f8:hover , .ub36b6b7f55aab767a4795c2ef99cc4f8:visited , .ub36b6b7f55aab767a4795c2ef99cc4f8:active { border:0!important; } .ub36b6b7f55aab767a4795c2ef99cc4f8 .clearfix:after { content: ""; display: table; clear: both; } .ub36b6b7f55aab767a4795c2ef99cc4f8 { display: block; transition: background-color 250ms; webkit-transition: background-color 250ms; width: 100%; opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #95A5A6; } .ub36b6b7f55aab767a4795c2ef99cc4f8:active , .ub36b6b7f55aab767a4795c2ef99cc4f8:hover { opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #2C3E50; } .ub36b6b7f55aab767a4795c2ef99cc4f8 .centered-text-area { width: 100%; position: relative ; } .ub36b6b7f55aab767a4795c2ef99cc4f8 .ctaText { border-bottom: 0 solid #fff; color: #2980B9; font-size: 16px; font-weight: bold; margin: 0; padding: 0; text-decoration: underline; } .ub36b6b7f55aab767a4795c2ef99cc4f8 .postTitle { color: #FFFFFF; font-size: 16px; font-weight: 600; margin: 0; padding: 0; width: 100%; } .ub36b6b7f55aab767a4795c2ef99cc4f8 .ctaButton { background-color: #7F8C8D!important; color: #2980B9; border: none; border-radius: 3px; box-shadow: none; font-size: 14px; font-weight: bold; line-height: 26px; moz-border-radius: 3px; text-align: center; text-decoration: none; text-shadow: none; width: 80px; min-height: 80px; background: url(https://artscolumbia.org/wp-content/plugins/intelly-related-posts/assets/images/simple-arrow.png)no-repeat; position: absolute; right: 0; top: 0; } .ub36b6b7f55aab767a4795c2ef99cc4f8:hover .ctaButton { background-color: #34495E!important; } .ub36b6b7f55aab767a4795c2ef99cc4f8 .centered-text { display: table; height: 80px; padding-left : 18px; top: 0; } .ub36b6b7f55aab767a4795c2ef99cc4f8 .ub36b6b7f55aab767a4795c2ef99cc4f8-content { display: table-cell; margin: 0; padding: 0; padding-right: 108px; position: relative; vertical-align: middle; width: 100%; } .ub36b6b7f55aab767a4795c2ef99cc4f8:after { content: ""; display: block; clear: both; } READ: Rosa Parks And the Montgomery Bus Boycott EssayTo conclude, I just want to say that this show was fascinating. It didnt seem that there were some mistakes done neither from actors, nor technicians or directors. This was a great experience for me, which I were not ready for, since I am not very fond of dance productions. But I was swept away with the quality of the performance and the beauty of Irish folk music. Theater Essays