Should Britain Join The Single Currency? :: essays research papers

The debate has waged for several years now, ever since news of a single European Economic Union came first surfaced nearly fifteen years ago. The idea was simple, and focused on allowing multi-national European countries greater ease, and cost effective benefits when trading between countries. In a sense, the EEC was trying to implement an economic model similar to that of the United States, where amongst all fifty of the states there existed a single currency under a central federal bank that controlled the national interest rate level and other currency issues. Thus trade between the states was eased, promoting companies both with nation-wide interests, and those wishing to build from regional to nation wide platforms. However, since the official launch of the â€Å"Euro† in January of 1999, Britain, along with Sweden and the Dutch population, have chosen to remain isolated from this conglomerate, creating what many term a â€Å"two-speed† European economy. But why d oes the Britain business sector choose to remain isolated from this currency? This essay will attempt to examine both the positive and negative aspects of joining the single currency, while analyzing the forces behind Britain’s involvement.   Ã‚  Ã‚  Ã‚  Ã‚  So what exactly are the benefits of a single currency for Britain’s business sector? First of all, firms that export a lot to other countries within the euro zone don't have to bear the costs of exchanging profits into their home currency anymore. Multinationals also save a lot of money if all their subsidiaries trade in the same currency. Smaller firms suddenly are finding customers in regions they thought they could never be bothered to export to. The disappearance of these transaction costs is bound to boost economic growth, and will make goods cheaper for consumers. And even the weak euro has been a boon for the euro zone, as its exports to the United States and the UK have become more competitive. The Financial Times noted, while the value of the euro has been decreasing, exports have risen from 50 billion euros, to now 75 billion euros annually.   Ã‚  Ã‚  Ã‚  Ã‚  Furthermore, one currency across Europe increases the urge for companies to do business across the continent. For a start, it is easier to raise the cash to do a deal. Secondly, the fact of the single currency makes it easier to do business in other European countries, encouraging companies already lured by the prospect of boosting their revenues by entering new markets.

Hostel Management

Hostel Management Abstract For the past few years the number of educational institutions is increasing rapidly. Thereby the number of hostels is also increasing for the accommodation of the students studying in this institution. And hence there is a lot of strain on the person who are running the hostel and software's are not usually used in this context. This particular project deals with the problems on managing a hostel and avoids the problems which occur when carried manually . Hence to reduce the load on the person handling this.It includes some of the following features. RULES AND REGULATIONS: In this the rules and regulations of the hostels are given in details. HOSTEL FACILITES: In this the details of facilities provided in the hostels are given in details. ACCOMODITY: This deals with the total no. of rooms available with the details of total no. of students accommodating in a room. Here the details of equipment provided are also listed. WARDENS AND TEACHING STAFF DETAILS: It deals with the details of the wardens.Here the details of the teaching staff are also mentioned with details of their mess calculations ,etc. STAFF DETAILS: The staff working in the hostel and their salary calculation,leave,etc can be attained from this section. The existing system of hostel management is done manually which is an ineffective manner. This way of managing the hostels has many limitations. The chances of occurring errors are more. So the records must be accurate, informative and dynamically updated. †¢ TIME CONSUMING †¢ HUMAN ERROR SLOW PROCESS †¢ BACKUP †¢ POOR QUALITY †¢ DATA INCONSISTENCY This is a software created for the purpose of managing all the works of a hostel in a most efficient manner. The project includes all the basic events carried out in a hostel like mess bill calculation, daily voucher etc. The software keeps a close track on the number of inmates, teaching staff, non -teaching staff. Room details and student registration a re also included. The project is a complete software package for hostel management.

Determining Outliers in Statistics

Outliers are data values that differ greatly from the majority of a set of data. These values fall outside of an overall trend that is present in the data.  A careful examination of a set of data to look for outliers causes some difficulty. Although it is easy to see, possibly by use of a stemplot, that some values differ from the rest of the data, how much different does the value have to be to be considered an outlier?  We will look at a specific measurement that will give us an objective standard of what constitutes an outlier. Interquartile Range The interquartile range is what we can use to determine if an extreme value is indeed an outlier. The interquartile range is based upon part of the five-number summary of a data set, namely the first quartile and the third quartile. The calculation of the interquartile range involves a single arithmetic operation. All that we have to do to find the interquartile range is to subtract the first quartile from the third quartile. The resulting difference tells us how spread out the middle half of our data is. Determining Outliers Multiplying the interquartile range (IQR) by 1.5 will give us a way to determine whether a certain value is an outlier. If we subtract 1.5 x IQR from the first quartile, any data values that are less than this number are considered outliers. Similarly, if we add 1.5 x IQR to the third quartile, any data values that are greater than this number are considered outliers. Strong Outliers Some outliers show extreme deviation from the rest of a data set. In these cases we can take the steps from above, changing only the number that we multiply the IQR by, and define a certain type of outlier. If we subtract 3.0 x IQR from the first quartile, any point that is below this number is called a strong outlier. In the same way, the addition of 3.0 x IQR to the third quartile allows us to define strong outliers by looking at points which are greater than this number. Weak Outliers Besides strong outliers, there is another category for outliers. If a data value is an outlier, but not a strong outlier, then we say that the value is a weak outlier. We will look at these concepts by exploring a few examples. Example 1 First, suppose that we have the data set {1, 2, 2, 3, 3, 4, 5, 5, 9}. The number 9 certainly looks like it could be an outlier. It is much greater than any other value from the rest of the set. To objectively determine if 9 is an outlier, we use the above methods. The first quartile is 2 and the third quartile is 5, which means that the interquartile range is 3. We multiply the interquartile range by 1.5, obtaining 4.5, and then add this number to the third quartile. The result, 9.5, is greater than any of our data values. Therefore there are no outliers. Example 2 Now we look at the same data set as before, with the exception that the largest value is 10 rather than 9: {1, 2, 2, 3, 3, 4, 5, 5, 10}. The first quartile, third quartile, and interquartile range are identical to example 1. When we add 1.5 x IQR 4.5 to the third quartile, the sum is 9.5. Since 10 is greater than 9.5 it is considered an outlier. Is 10 a strong or weak outlier? For this, we need to look at 3 x IQR 9. When we add 9 to the third quartile, we end up with a sum of 14. Since 10 is not greater than 14, it is not a strong outlier. Thus we conclude that 10 is a weak outlier. Reasons for Identifying Outliers We always need to be on the lookout for outliers. Sometimes they are caused by an error. Other times outliers indicate the presence of a previously unknown phenomenon. Another reason that we need to be diligent about checking for outliers is because of all the descriptive statistics that are sensitive to outliers. The mean, standard deviation and correlation coefficient for paired data are just a few of these types of statistics.