Hospitality in Homers Odyssey :: Homer Odyssey Essays

Hospitality in Homer's Odyssey      Ã‚  Ã‚  Ã‚  Ã‚   The first four books of Homer’s Odyssey depict certain instances of hospitality which are filled with generosity.   One reason for the importance of this hospitality could have been a respect for foreigners, who were completely at the mercy of their hosts, especially when hosts had themselves been foreigners.   A second reason why hospitality may have been important was to see if the guest was disguised as an enemy.      Ã‚  Ã‚  Ã‚  Ã‚   In Book 4, Homer tells how Telemakhos and Pallas Athena (disguised as Mentor) visited Menelaos in Sparta.   When Eteoneus, the King’s aide, asked whether he should welcome the guests, Menelaos replies, â€Å"before this, Eteoneus, you were never a fool †¦ we’ve both been entertained as guests before we came back home again †¦ so unharness the horses;   bring the strangers to the banquet.† (p. 33 Hull translation).   This comment illustrates the fact that Menelaos, as host, had had the experience as foreigner before, especially when he was at war and had encounters with both friends and foes in distant lands.   He empathizes with the position his guests are in, and Telemarkos and Athena were bathed and fed before they were questioned.      Ã‚  Ã‚  Ã‚  Ã‚   In book three, Telemarkos and Athena go to visit the wise Nestor in Pylos where they are welcomed and well-cared for.   In his wisdom, Nestor recognizes Athena for who she is, and offers up a holocaust to her the following morning (p. 30).   In embracing the company of foreigners and showing the proper hospitality, hosts both warded off the fury of gods visiting as disguised guests as is portrayed in the Odyssey, but in reality, it also may have been a way of making friends of enemies who came to scout the land.   The in-depth inquisitions after the guests were fed may have been not only innocent curiousity, but also a way to weed out ill-intentioned visitors.   This can be seen in Nestor’s wise inquiry, â€Å"Who are you, stranger? [Are you] recklessly roving the sea as pirates do while staking their lives and bringing ills to alien people?† (p. 23).   Hosts not only had to be prepared to entertain gods, but also protect themselves and th eir land.

Financial Forecasting †Time Series Models Essay

The economic recession felt in the United States since the collapse of the housing market in 2007 can be seen by various trends in the housing market. This collapse claimed some of the largest financial institutions in the U.S. such as Bear Sterns and Lehman Brothers, as they held over-leveraged positions in the mortgage backed securities market. Credit became widely available to unqualified borrowers during the nineties and the early part of the next decade which caused bankers to act predatorily in their lending practices, as they could easily sell and package subprime mortgage loans on leverage. This act caused a bubble that would later burst when unqualified homebuyers began defaulting on their loans causing a tremendous downfall in the U.S housing market. Understanding which direction key market factors, such as the housing market, are going can help re-establish stability in the market, which is at an all-time premium. This paper is designed to help better predict the direction of the housing market in the future via the use of time series models, in an effort to re-establish a sense of stability in the housing market. The following chart (Figure 1) represents the time series data for non-seasonally adjusted home sales in the U.S. (NHS) from January 1975 to February 2012. The length of this period is significant because over a long period of time we can analyze trend, seasonality, cycles, and irregularity allowing us to better understand the future direction of the market. Trend is the long term change in the level of data. We can find trend in the data by simply looking at the chart and observing the general direction of the data over a long period of time. These trends can be deduced to a consistent change in the mean level of the data over a significant period of time, keeping in mind that seasonality will occur year over year therefore annual recurring changes to the level of data should not account for an increase or decrease in the trend mean. Seasonality is the regular fluctuations in levels of data in a time series that occur every year at the same time of the year. Seasonality is often see n in data that fluctuates regularly in accordance with calendar seasons. When analyzing this data we must also take into account cycles. Cyclical data can be recognized by it smooth elongated upward and downward movements on a long term scale. These reactions are more irregular than seasonal patterns, but more regular than a change in the trend. Generally the cause of a cycle is less apparent right away and occurs because of the ups and downs in the economy making it harder to predict. In Figure 1 we can observe a distinct upward trend until late in 2005, with strong seasonality, and three distinct cycles. The final component that we must acknowledge is irregularity. Irregularities are the random fluctuations that are not affected by the other three components making it the hardest to predict or rationalize. There is some irregularity in Figure 1, but it does not seem to be dominant as most of the fluctuations noticed in the time series could be rationalized by one of the previous three factors. Data source: National Association of Realtors One way to verify a trend in a time series is to analyze a k-period plot of autocorrelations, also known as an autocorrelation function (ACF). If a trend is present we should notice a gradual decline, however if we see a steep decline we should note that there is no trend. In Figure 2, which represents time series data for non-seasonally adjusted home sales in the U.S. (NHS) from January 1975 to February 2012 we can observe a gradual decline meaning a positive trend is present. Additionally we can use the rule of thumb stating each value in the 12-period plot of ACF is greater than .0947 (2/446) and thus greater than the upper limit, representing statistical significance from zero and concluding that a trend is present. Now that we have verified the presense of a trend in the data we will look to verify the seasonality we saw earlier represented by regularly reoccurring fluctuations in the levels of data in accordance with the calendar seasons. To do this we will use an autocorrelation function for the first differenced new home sales data. We will use a larger sample, in this case 24 months, so that we can see the regularly reoccurring fluctuations from one year to the next. When we look at the graph in Figure 3 we notice great increases with lag 12 and lag 24. The jumps seen in lags 12 and 24 confirms the presense of seasonality as they are above the upper limit representing statistical significance. F Time Series and Regression Models for New One-Family Houses Sold Since the NHS data has been shown to have trend and seasonality we will evaluate the data using four different time series models and compare the results of each to see which model is the most accurate. The models we are going to use are the Modified Naà ¯ve model, Winters Exponential Smoothing model, Time Series Decomposition, and Autoregressive Integrated Moving Average (ARIMA). We will also test a multiple regression model to attempt to forecast future NHS, while taking into consideration independent variables. Regression models determine the future direction of the dependent variable based on the forecasts of the independent variable(s). Often times this can lead to a less accurate forecast as too much emphasis is being applied to the correlation of the independent variables to the dependent variable. In reality large ranges of macroeconomic data such as NHS vary because of numerous variables that may not be taken into account. The Multiple Regression model in Table 1 will have NHS as the dependent variable and use the 30-year conventional mortgage and the Seasonally Adjusted Disposable Person Income as the independent variables. This data comes from the Federal Reserve Bank of St. Louis. We are going to use two separate periods in our analysis. The first period that we are going to use is our historical data from January 1975 through August 2011. The last six months of model from September 2011 through February 2012 is our hold out model in which we test the forecasted NHS results against the actual NHS during the same span of time to test the accuracy of the models forecast’s. We will use ForecastX software to run the models in an attempt to determine which model is the most accurate and thus should be used to forecast NHS to obtain the clearest picture of the future direction of the market. The two error measurements we will use to determine accuracy are mean absolute percentage error (MAPE) and root mean square error (RMSE). To obtain MAPE, we first divide the forecast error or error (actual value – forecast value) by the actual value to yield the percentage error and then calculate the mean of the absolute percentage errors. For RMSE, we first square the forecast error and then take the squared root of the mean of the squared errors. Multiple regression model| 29.54%| 19.07| 32.07%| 105.58%| 43.12| 176.00%| *The mean for the historical period is 59.46 and the holdout period mean is 24.5 We can deduce the most accurate forecasting model from Table 1’s forecasting error results. The model with the least amount of error is the most accurate, which in this case is the Time Series Decomposition model with exponential smoothing for the historical period. However the ARIMA model contains the least amount of error for the holdout period which would leads me to believe that it would be the most accurate indicator over the six month time period from March 2012 to August 2012, however when the model is run over the entire range of data to forecast the next six month period the upper and lower limits drastically trend away from each other each month resulting in a very vague forecast with little confidence. Therefore we will use the Time Series Decomposition to forecast the ex-ante forecast from March 2012 to August 2012. As I touched on earlier, the housing market is a key indicator as to the overall health of the market. Like other markets, the housing market is a victim to cyclical fluctuations. However, if investors and market participants can accurately forecast cyclical fluctuations in the housing market or in interest rates they will have more confidence in the market and be able to make more aggressive moves, spurring economic activity. The forecast summary in Figure 4 shows a continuation in the current NHS cycle through July 2012. This information could be used by a real estate investor to gain insight as to the right time to buy. If NHS continues to decline then we can assume that prices will continue to fall with it, and an investor can wait for the right time to buy real estate at the lowest price. Conclusion The current outlook for NHS for one-family homes in the U.S. according to Figure 4, is seemingly positive. We have experienced a great recession since 2007, and in that recession we have reached depths unrealized since 1975 for NHS. This is shocking because of the population inflation in the country throughout that time, and is really telling of the severity of the recession we endured. Although, with that being said I think that the depleted NHS statistics will encourage investors to jump in and start buying up new property, as well as family that might have never been financially able to do it before. With these low levels in sales we have seen some tremendous deals becoming available, and at the same time banks are starting to lend again. Considering all of those factors I think that NHS will again start to trend positively. References [1] â€Å"Total New Houses Sold: Thousands.† Economagic: Economic Time Series Page. N.p., n.d. Web. 25 Apr. 2012. . [2] Wilson, J. Holton, and Barry Keating. Business forecasting: with forecastX. 6. ed. Boston: McGraw-Hill/Irwin, 2009. Print. [3] Forecast X 7.1. John Galt Solutions, Inc.

College: A Sound Investment Essay

Cost is always about loss. When we consider opportunity cost, we consider what losses an opportunity brings us. One of the most enduring â€Å"opportunities† individuals receive is education. Society is immersed in messages about the benefits of education, but what about the costs? Any important decision should involve a sound cost-benefit analysis. Do the benefits outweigh the costs? This is the basic question of decision-making—of investment. Is college worth the investment? In consideration of this question, I would first need an estimate of the cost of college. First, I would tally the basic, overt costs. Tuition, books, room and board, travel expenses (for commuters): each of these components would subtracts from my finances. Suppose all of these expenses add up to roughly fifteen thousand dollars per year. Now, if I subtracted around five thousand dollars for scholarship, I would be left with a net debt of ten thousand dollars per year. At the end of my college experience, the cost of college would be around forty thousand dollars, correct? Incorrect—I have failed to consider the â€Å"hidden† costs of college. Namely, I have not aken into account the wages I have lost because I chose to be in the classroom rather than in the workforce. With a high school education, I might have expected a job that pays perhaps fifteen thousand dollars a year. Four years of this wage would leave me with a gross profit of sixty thousand dollars. So, what is my overall cost of college now?†¦ 100,000 dollars. What was I thinking? For one, I was thinking that without a higher education I could have expected my wages to rise by at most five thousand dollars, if I was being generous. No advancement in my nowledge or skill sets would have corresponded with no job growth opportunities and thus no significant income growth. Therefore, short of winning the lottery, my wages would remain relatively stagnant. I was also thinking that with a college degree as my backing, I would have the leverage to position myself into an entry-level job that would pay at the very minimum twenty-thousand to twenty-five thousand dollars. Further, I was thinking that entry-level positions lead to advancement. Entry-level positions lead to opportunity. With a decade of strong ork ethic, I have a respectable chance of cracking the forty to fifty thousand dollar barrier, if not more. With just two years of such a salary, I would compensate for the one hundred thousand dollar investment I made to ensure my career. I was thinking that I would much rather retire with a net worth of in the hundreds of thousands rather than the prospect of a retirement where the number 100,000 is still as magical and elusive as that winning lottery number. Most important, I was thinking that I would gladly pay the â€Å"opportunity cost† today to realize the â€Å"opportunity†Ã¢â‚¬â€for myself and for my future family—tomorrow.

Air Masses and Fronts

Air Masses and Fronts Air masses is every where around us, it creates pressure around our body and keeps us from floating. Air mass is the pressure in the gravity that also has to the temperature in the atmosphere to create different pressure that effects you in different ways. For example, if hot air were to fill a plastic bag it would rise, but if it where cool air that filled the bag it would be less likely to rise than the other. Fronts, on another hand, are produced when a large amount of cool winds go east and a front of hot air would go west and hit they will create an enormous storm that is most likely to bring heavy showers and thunderstorms. Fronts also have to do with the pressure that is in the atmosphere. Advection is when a large amount of warm moist air current of that goes over a cold body of water of land. Air masses are the differences of the hot air and cold air that come in through different parts of the world and make what are storms that cause many damages. There are a couple of types of air masses that create most of the bodies of winds that collide into each other and sometimes make surging tornadoes. The collision of these air masses usually create many of the worst storms that usually occur in the mid-east. There are also the massive rain storms that have lightning and there are also the tornados that make the wind gust. Fronts are when massive air currents collide, but it usually occurs when it is a cold and hot current that meet. They hit and make a storm that creates a rain that make thunder storms and other natural storms. These front travel cross country to be crashed into another one half way across the nation. They would be produces in one place on the west of the united states and travel eastward to confront another front. Advection is fog or mist of condensed clouds of water vapor in the form ice droplets or ice crystals, suspended above in the atmosphere just over the surface of the earth. In heavy populated city the Advection fog gets mixed with the car emissions and gasses from factories to make it a brownish color. There are four types Advection, Radiation, Upslope, and Precipitation fog. In this essay I learned the different types of Advection fogs there were and how it got mixed with the gas emissions. I also learned that a front are the ones that create huge storms in the center of the United States. Finally I learned the differences in the air masses and there temperature.

In the United States, many states Essay

The rescue game, a joint social responsibility, must be approached with a lot of tact and undying resilience. With mortality rates on the high and visible evidences of the cause stirring us in the face, we are left with but one question, just one: who is the real killer? The very undeniable frequent recurrence of death through heart disease has been on the high for long stretches of time, which earlier was solely traceable to tobacco. The earlier discovery though incomplete has led the Government of the United States to impose heavy taxes on the tobacco manufacturing companies. Much of the funds realized from such taxes have helped to project billboards and campaigns against tobacco use. However, of late a more devastating killer of mankind was sighted. This, which had stayed so closely to the bosom of humans and even lured many until it became a delight, suddenly brings out its silent pistol carting the lives of many away. Who may have realized how potentially harmful a seeming sumptuous high-fat diet would have turned out? The facts are however showing by the day in alarming numbers of the heart disease even in non-smokers. I hereby use this medium to vigorously alert the Government to the incumbent dangers and threat to life these die hard life stealer have been and therewith request urgently that a bill be passed to levy high-fat fast food producers with heavy tax duties as was with the tobacco, seeing now this high fat foods even kills more and faster than other possible causes of heart failure. Such funds can then be re-channeled to educate the still ignorant many through the use of advance multimedia projections on lighted digital boards, billboard, newspaper and television.

Bluebells of Scotland

Compelled by society’s negative opinion of the trombone, Pryor composed â€Å"Bluebells of Scotland† in order to display the capabilities of the trombone and to emphasize that it could play very difficult pieces. This piece is a little more upbeat and physical than other pieces of the same time period, but what makes this piece particularly unique is that it is the only piece with a high level of difficulty for trombone that was composed in the time of its conception. The piece is intended to be accompanied by an orchestra but can be played with piano accompaniment as well. The characteristics of this piece are very dynamic and free. The expressivity of the piece is left up to the interpretation of the performer which allows for a large amount of freedom and creativity. There are quite a few cadences that allow the performer to take control of tempo and dynamics. â€Å"Bluebells of Scotland† includes both very slow and lyrical passages as well as many very quick and difficult runs, double-tonguing, octave jumps, lips slurs, and a vast amount of high register notes. The introduction of the piece begins with a triumphant cadence peaking at a high register C, which slowly decays into a very lyrical melodic sequence that takes the shape of the original lyrics, which read: â€Å"Oh where, tell me where, is your highland laddie gone? Oh where, tell me where, is your highland laddie gone? He’s gone with streaming banners where noble deeds are done. And it’s oh! in my heart I wish him safe at home. † The piece then moves into a much more difficult technical variation of the original melody and progressively builds in speed until the also very triumphant ending. Around the time the then eighteen or nineteen year old Arthur Pryor composed â€Å"Blue Bells of Scotland†, he had only been playing the slide trombone for three to four years. Pryor was given the slide trombone as a payment to a debt and taught himself to play it fairly quickly. Amazingly, Pryor wrote the piece with the knowledge of only a few slide positions and would use false tones and alternate positions for notes in the fifth, sixth, and seventh positions. Despite this, â€Å"Blue Bells of Scotland† is still one of Pryor’s most memorable pieces and is still widely known today. A few years after composing â€Å"Bluebells of Scotland† he arrived in New York by invitation of John Philip Sousa. After arriving to be in Sousa’s new concert band, Pryor was offered by Frank Holton the position of principle chair trombonist. When Holton left the position, Pryor became the featured soloist of over 10,000 solos as principle trombonist for Sousa’s band. â€Å"Blue Bells of Scotland† serves as a constant reminder of the trombone’s capabilities as a very lyrical instrument and its ability for technical prowess as well. Arthur Pryor inspired interest in the trombone with his virtuoso playing, and â€Å"Bluebells of Scotland† in particular has been a standard of trombone literature for decades and a favorite challenge for advanced players†(Kleiner). The piece will always have a special place in the hearts of all trombonists, both past and future. Works Cited http://www. celticscores. com/sheet-music/628_Blue_Bells_of_Scotland http://bluebellstrilogy. com/blog/2010/05/arthur-pryor-blue-bells-and-trombone/

Lean Implementation at Oak Hill Case Study Example | Topics and Well Written Essays - 250 words

Lean Implementation at Oak Hill - Case Study Example Their current lead-time of domestic order for the testing equipment lies in between one to two weeks, with international orders yielding up to three months. Urgent requirement lies within the high costs associated with keeping the exploration idle – costing an average of $50,000 to $100,000 per day. In intending to reduce their lead-time to one week, as well as to increase operational efficiency, Oak Hills also plans to enhance customer service with the help of lean manufacturing techniques. Oak Hills level of inventory is much higher than necessary because of the high demand volatility and lack of vendor management. The main issues that Oak Hills facility is facing are, employee related concerns, worn out machines, workload on quality department, the lack of quality checkpoints, and process inefficiency. All of which need to be solved before implanting the lean system. The lean system will help improve the processes and quality of the facility as well as lessen the wastage. However, employees are the key to this change; we need to change the current work environment to help boost the employee morale. Furthermore, machines and systems need to be updated in order to reduce waste of raw material and to save time and manpower. The management and employees at Oak Hills need to work together and communicate with one another in order to implement lean system successfully. Employee related fears: There is a lack of communication from management to employees regarding current lay-off’s due to the economy issues, which has diminished the employee morale. Additionally, issues of unfair bonuses pools are awarded on team basis, which employees are cross functionally trained, and are sometimes forced to assist other employees to complete the rush jobs. Damaged machines: Oak Hills has been utilizing old worn-out machines for years in which are not up to par with current levels. This results in waste of raw materials and employee turmoil at work