第1个回答 2007-07-03
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Probability density function A function used to compute probabilities for a continuous random variable. The area under the graph of a probability density function over an interval represents probability.
Uniform probability distribution A continuous probability distribution for which the probability that the random variable will assume a value in any interval is the same for each interval of equal length.
Normal probability distribution A continuous probability distribution. Its probability density function is bell shaped and determined by its mean µ and standard deviation Õ
Standard normal probability distribution A normal distribution with a mean of zero and a standard deviation of one
Exponential probability distribution a continuous probability distribution that is useful in computing probabilities for the time it takes to complete a task..
Parameter A numerical characteristic of a population, suck as a population mean µ, a population standard deviation Õ, a population proportion p, and so on.
Simple random sampling Finite population: a sample selected such that each possible sample of size n has the same probability of being selected. Infinite population: a sample selected suck that each element comes from the same population and the elements are selected independently.
Sampling without replacement Once an element has been included in the sample, it is removed from the population and cannot be selected a second time.
Sample statistic A sample characteristic, such as a sample mean x¯, a sample standard deviation s, a sample proportion p¯, and so on. The value of the sample statistic is used to estimate the value of the population parameter.
Point estimate A single numerical value used as an estimate of a population parameter.
Point estimator The sample statistic, such as x¯, s, or p¯, that provides the point estimate of the population parameter.
Sampling error The absolute value of the difference between an unbiased point estimator and the corresponding population parameter. For a sample mean, sample standard deviation, and sample proportion, the sampling errors are x¯-u, s- Õ, p¯-p, respectively
Sampling distribution A probability distribution consisting of all possible values of a sample statistic.
Finite population correction factor The term (N-n)/(N-1)开根号 that is used in the formulas for Õx and Õp whenever a finite population rather than an infinite population, is being sampled. The generally accepted rule of thumb is to ignore the finite population correction factor whenever n/N<0.5
Standard error The standard deviation of a point estimator.
Central limit theorem A theorem that enables one to use the normal probability distribution to approximate the sampling distribution of x¯ and p¯ whenever the sample size is large.
Unbiasedness A property of a point estimator when the expected value of the point estimator is equal to the population parameter it estimates.
Relative efficiency Given two unbiased point estimators of the same population parameter, the point estimator with the smaller standard deviation is more efficient.
Consistency A property of a point estimator that is present whenever larger sample sizes tend to provide point estimates closer to the population parameter.
Stratified random sampling A probability sampling method in which the population is first divided into strata and a simple random sample is then taken from each stratum.
Cluster sampling A probability sampling method in which the population is fist divided into clusters and then a simple random sample of the clusters is taken.
Systematic sampling A probability sampling method in which we randomly select one of the first k elements and then select every kth element thereafter.
Convenience sampling A nonprobability method of sampling whereby elements are selected for the sample on the basis of convenience.
Judgment sampling A nonprobability method of sampling whereby elements are selected for the sample based on the judgment of the person doing the study.
Sampling with replacement Once an element has been included in the sample, it is returned to the population. A previously selected element can be selected again and therefore may appear in the sample more than once.
Interval estimate An estimate of a population parameter that provides an interval believed to contain the value of the parameter. It has the form point estimate +- margin of error
Margin of error The +- value added to and subtracted from a point estimate in order to develop an interval estimate of a population parameter.
Sampling error The absolute value of the difference between an unbiased point estimator, such as the sample mean x¯, and the value of the population parameter it estimates, such as the population mean u. In the case of a population mean, the sampling error is x¯-u. In the case of a population proportion, the sampling error is p¯-p
Precision statement A probability statement about the sampling error.
Confidence level The confidence associated with an interval estimate. For example, if an interval estimation procedure provides intervals such that 95% of the intervals formed using the procedure will include the population parameter, the interval estimate is said to be constructed at the 95% confidence level.
Confidence Coefficient The confidence level expressed as a decimal value. For example, .96\5 is the confidence coefficient for a 95% confidence level.
T Distribution A family of probability distributions that can be used to develop an interval estimate of a population mean whenever the population standard deviation Õ is estimated by the sample standard deviation s and the population has a normal or near-normal probability distribution.
Degrees of freedom A parameter of the t distribution. When the t distribution is used in the computation of an interval estimate of a population mean, the appropriate t distribution has n-1 degrees of freedom, where n is the size of the simple random sample.
Null hypothesis The hypothesis tentatively assumed true in the hypothesis testing procedure.
Alternative hypothesis The hypothesis concluded to be true if the null hypothesis is rejected.
Type Ⅰerror The error of rejecting H0 when it is true
Type Ⅱerror The error of accepting H0 when it is false.
Level of significance The maximum allowable probability of making a TypeⅠerror
Rejection region The range of values that will lead to the rejection fo a null hypothesis.
Test statistic A statistic whose value is used to determine whether a null hypothesis can be rejected.
Critical value A value that is compared with the test statistic to determine whether h0 should be rejected.
One-tailed test A hypothesis test in which rejection of the null hypothesis occurs for values of the test statistic in one tail of the sampling distribution.
Two-tailed test A hypothesis test in which rejection of the null hypothesis occurs for values of the test statistic in either tail of the sampling distribution.
p-value The probability, when the null hypothesis is true, of obtaining a sample result that is at least as unlikely as what is observed. It is often called the observed level of significance.
Power The probability of correctly rejecting H0 when it is false.
Power curve A graph of the probability of rejecting H0 for all possible values of the population parameter not satisfying the null hypothesis. The power curve provides the probability of correctly rejecting the null hypothesis.
SAMPLE error=STANDARD DEVIATION OF SAMPLE MEAN=S=s/根号N = 根号p*(1-p)/n
N=Z-SCORE平方*S平方/MARGIN OF ERROR平方
MARGIN OF ERROR=Z-SCORE*STANDARD ERROR（S） （CONFIDENCE）