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Applied Statistics Statistica

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Applied Statistics: Random Sampling

Statistical techniques are primarily used to manage data. For example, statistical studies might try to make sound generalizations about the characteristics of a given population. Rather than analyze the entire group, however, such conclusions are typically drawn from more manageable population samples. In order for the generalizations to be valid, these samples must be random.

Random sampling can be defined as "a method of selecting a sample in such a way that every possible sample has the same probability of being selected." Populations may be either finite or infinite. Finite populations consist of a fixed number of elements; whereas, infinite populations have a limitless number of values. With finite populations, random samples can be determined through the use of random numbers. Such numbers may be either obtained from tables or generated by computer. Individual items within a population can then be selected from numbered lists. With infinite populations, a sample is considered random if it has a corresponding distribution of independent random variables. This ensures that the sample represents the particular infinite population's overall characteristics.

Most any survey or poll will employ some form of random sampling. As an example, the popularity of television programs is based on "ratings." These ratings represent a survey of what shows people prefer to watch. The survey is based on a random sample of viewers

. . .
portant than others. When averaging such groups, it is sometimes necessary to assign increased significance to such values. The mathematical technique for creating a weighted average involves various steps. First, weights must be assigned to each value based on its relative importance. Only then can the weighted mean be calculated. For example, the weighted mean, xw, of a set of numbers x1, x2, x3, . . . xn, whose relative importance is expressed numerically by a corresponding set of numbers w1, w2, w3, . . . wn, may be given by the following formula: xw = w1x1 + w2x2 + . . . wnxn = S w(x w1 + w2 + . . . wn S w In this equation, S w(x may be defined as the "sum of the products obtained by multiplying each x by the corresponding weight" and S w is the "sum of the weights." Perhaps the most widely used and quoted weighted average is the Dow Jones industrial average. This financial average is comprised of 30 of the biggest and best-known American stocks. Each stock's contribution is weighted according to its per-share closing price. Thus, a company whose stock sells for $100 per share has twice as much potential influence over the Dow Jones industrial average than one whose stock sells
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Some common words found in the essay are:
Standard Deviation, Mean Statistical, Statistics Range, Normal Distribution, Statistics Probability, Sampling Statistical, Distribution Frequency, Simon Freund, Weighted Mean, Fish Department, applied statistics, standard deviation, normal distribution, los angeles times, angeles times, los angeles, frequency distributions, set data, march 1995, weighted mean, arithmetic mean, angeles times 7, jones industrial average, dow jones industrial, means event occur,
Approximate Word count = 2534
Approximate Pages = 10 (250 words per page)

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