Auburn Lake Trails

The Auburn Lake Trails union is a golf course as well as a recreation center situated in the outskirts of Philadelphia, Pennsylvania. Jimmy K. Smith designed the 9-hole course by the river. The golf course was formed due to its strategic location of serenity. Major Banks manage and maintain it and it uses it for recreational activities for the staff in order to avoid burn-outs in the office.

The club offers five memberships plans:

Golf Membership Plan: this plan comprises golf members in addition to their immediate family.
Perquisites for this plan are golf, tennis, access to the swimming pool and clubhouse access where they can catch upon the TV or just play board games. Junior Membership Plan: this plan is inclusive of young folks in the workforce and their families. Sports Membership Plan: consists of sports members as the name suggests. It is for the athletic type and their families.

Concluding Remarks

Finally yet importantly in the list of membership plans there is Social Membership Plan, which includes social members and their wife and children. Auburn Lake Trails provides them with access to the guesthouse only.
There are people who are interested in joining the club but cannot afford it due to other financial commitment. Auburn Lake Trails designed a plan just for them and this is the Non-Resident Membership Plan.

A look at Dulles Int'l Airport, Washington DC

Washington Dulles International Airport (IAD) is in Virginia, on a large piece of land – thousands of acres – in the outskirts of Washington, DC. The airport was opened five decades ago and was meticulously designed by James Smith. He was a well-known architect in the 1960s. Travelers all over the world tend to regard Dulles Int’l Airport as world class and they do not mind visiting Washington DC for a stopover. In fact, the airport is one of the most important for global travelers, both US residents and foreigners. It has a blend of heritage and affordable air tickets that offer sky services all the way through their various destinations.

There are a number of airports in Washington, DC. The major one is Washington Dulles International Airport, which has been in existence since the early 1960s. A few financial institutions operate in the airport are there to complement the wants of tourists and citizens of United States of America using this airfield. Depositories of local and foreign money are within the vicinity of travelers when they alight from their respective flights. Other financial services are conveniently and conspicuously nearby such as the exchange of foreign money to the local legal tender. In fact, there are large banners announcing their services.

Departures are maneuvered beginning from the runway linked to the key terminal where passengers can board their flight. Dulles International Airport has devoted its communications and runway network by constructing feeder roads within the airport, which incorporates an extra hunger, an up-to-the-minute airfield interchange control tower. There are sick and elderly people within the airport and moving around for these folks is another headache. Therefore, they lodged a complaint to the management of Dulles International Airport and they responded with a people mover around the airport. A small automated train is capable of moving many people at once within the airport.

Dulles International Airport has a momentous capability for potential expansion. In the midst of slight development, the existing amenities may possibly contain about 50 million passengers per year. The airport will have a capacity to handle even more passengers in the near future. A recent publication by The Economist suggests that Dulles International Airport could handle 100 million passengers by the year 2040.

1. Bank / Organization name: Bank of New York and JP Morgan Chase
2. Type: a full-fledged banking operation; it’s a branch / office / ATM / currency exchange
3. Location (Departures and Arrivals / Terminal B/ground floor/
4. Operating hours: 24 hours
5. Services available: deposit/withdrawals and currency exchange

Types of non-probability sampling

The first type is convenience sampling. The subjects are selected just because they are easiest to recruit for a study and the researcher does not consider selecting subjects that are representative of the entire population. In all forms of research, it would be ideal to test the entire population, but in most cases, the population is just too large that it is impossible to include every individual. This is the reason why most researchers rely on sampling techniques like convenience sampling, the most common of all sampling techniques. Many researchers prefer this sampling technique because it is fast, inexpensive, easy and the subjects are readily available.

Judgmental sampling or Purpose sampling - The researcher chooses the sample based on who they think would be appropriate for the study. This is used primarily when there are a limited number of people that have expertise in the area being researched. This type of sampling technique is also known as purposive sampling and authoritative sampling. Purpose sampling is used in cases where the specialty of an authority can select a more representative sample that can bring more accurate results than by using other probability sampling techniques. The process involves nothing but purposely handpicking individuals from the population based on the authorities or the researcher’s knowledge and judgment.

In this technique of sampling the first respondent refers a friend; the friend also refers a friend, (Castillo 2009). Snowball sampling is a non-probability sampling technique that is used by researchers to identify potential subjects in studies where subjects are hard to locate. Researchers use this sampling method if the sample for the study is very rare or is limited to a very small subgroup of the population. This type of sampling technique works like chain referral. After observing the initial subject, the researcher asks for assistance from the subject to help identify people with a similar trait of interest.

Probabilistic sampling techniques

The first statistical sampling method is simple random sampling. In this method, each item in the population has the same probability of being selected as part of the sample as any other item. For example, a tester could randomly select 5 inputs to a test case from the population of all possible valid inputs within a range of 1-100 to use during test execution. To do this the tester could use a random number generator or simply put each number from 1-100 on a slip of paper in a hat. Mixing them up and drawing out five numbers. Random sampling is possible with or without replacement. If it is without replacement, an item is discarded after it is selected and thus can only occur once in the sample (Black, 1999).

Babbie (2001) studied systematic sampling as another statistical sampling method whereby every nth element from the list is selected as the sample, starting with a sample element n randomly selected from the first k elements. For example, if the population has 1000 elements and a sample size of 100 is needed, then k would be 1000/100 = 10. If number 7 is randomly selected from the first ten elements on the list, the sample would continue down the list selecting the 7^th element from each group of ten elements. Care must be taken when using systematic sampling to ensure that the original population list has not been ordered in a way that introduces any non-random factors into the sampling. An example of systematic sampling would be if the auditor of the acceptance test process selected the 14^th acceptance test case out of the first 20 test cases in a random list of all acceptance test cases to retest during the audit process. The auditor would then keep adding twenty and select the 34^th test case, 54^th test case, 74^th test case and so on to retest until the end of the list is reached.

The statistical sampling method called stratified sampling is used when representatives from each sub-group within a population need to be represented in a sample (Babbie 2001). The first step in stratified sampling is to divide the population into sub-groups (strata) based on mutually exclusive criteria. Random or systematic samples are then taken from each subgroup. The sampling fraction for each sub-group may be taken in the same proportion as the sub-group has in the population. For example, if the person conducting a customer satisfaction survey selected random customers from each customer type in proportion to the number of customers of that type in the population. For example, if 40 samples are to be selected, and 10% of the customers are managers, 60% are users, 25% are operators and 5% are database administrators then 4 managers, 24 uses, 10 operators and 2 administrators would be randomly selected. Stratified sampling can also sample an equal number of items from each subgroup. For example, a development lead randomly selected three modules out of each programming language used to examine against the coding standard (Castillo, 2009).

The fourth and final of the probabilistic sampling techniques is statistical sampling method is called cluster sampling, also called block sampling. In cluster sampling, the population that is being sampled is divided into groups called clusters (Castillo 2009). Instead of these subgroups being homogeneous based on selected criteria as in stratified sampling, a cluster is as heterogeneous as possible to matching the population. A random sample is then taken from within one or more selected clusters. For example, if an organization has 30 small projects currently under development, an auditor looking for compliance to the coding standard might use cluster sampling to randomly select 4 of those projects as representatives for the audit and then randomly sample code modules for auditing from just those 4 projects. Cluster sampling can tell a lot about a particular cluster, but unless the clusters are selected randomly and a lot of clusters are sampled, generalizations cannot always be made about the entire population. For example, random sampling from all the source code modules written during the previous week, or all the modules in a particular sub-system, or all modules written in a particular language may cause biases to enter the sample that would not allow statistically valid generalization (Black, 2004).