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Statistics2
Stats Durham College
| Question | Answer |
|---|---|
| Reason to Sample | The results of a sample may adequately estimate the value of the population parameter, thus saving time and money |
| Reason to Sample | It may be too time consuming to contact all members of the population |
| Reason to Sample | It may be impossible to check or locate all the members of the population |
| Reason to Sample | The cost of studying all the items in the population may be prohibitive |
| Reason to Sample | Often, testing destroys the sampled item and it cannot be returned to the population |
| Simple Random Sample | A sample selected so that each item or person in the population has the same chance of being included |
| Systematic Random Sampling | A random starting point is selected, and then every kth member of the population is selected |
| Stratified Random Sampling | A population is divided into subgroups, called strata, and a sample is randomly selected from each stratum |
| Cluster Sampling | A population is divided into clusters using naturally occurring geographic or other boundaries. Then, clusters are randomly selected and a sample is collected by randomly selecting from each cluster |
| Sampling Error | The difference between a sample statistic and its corresponding population parameter |
| Sampling Distribution of the Sample Mean | A probability distribution of all possible sample means of a given sample size |
| Central Limit Theorem | If all samples of a particular size are selected from any population, the sampling distribution of the sample mean is approximately a normal distribution. This approximation improves with larger samples |
| Proportion | The fraction, ratio, or percent indicating the part of the sample or the population having a particular trait of interest |
| Sampling Distribution of the Sampling Proportion | A probability distribution of all possible sample proportions of a given sample size |
| Sampling Distribution of the Sample Mean | For a given sample size, the mean of all possible sample means selected from a population is equal to the population mean |
| Sampling Distribution of the Sample Mean | There is less variation in the distribution of the sample mean than in the population distribution |
| Sampling Distribution of the Sample Mean | The standard error of the mean measures the variation in the sampling distribution of the sample mean. The sample error is found by: stdev(sub x-bar)=stdev/sqrt(n) |
| Sampling Distribution of the Sample Mean | If the population follows a normal distribution, the sampling distribution of the sample mean will also follow a normal distribution for samples of any size |
| Sampling Distribution of the Sample Mean | Assume the population standard deviation is known. To determine the probability that a sample mean falls in a particular region, use the following formula: z=x-bar - mu/(stdev/sqrt(n)) |
| Sampling Distribution of the Sample Mean | The sampling distribution of the proportion follows a normal distribution if np and n(1-p) >5. 1. The sample proportion is p-bar=x/n |
| To determine the probability that a sample proportion falls in a particular region, use the following formula: | z=p-bar-p/(sqrt(p(1-p))/n) |
| mu sub x bar | mean of the sampling distribution of the sample mean |
| sigma sub x bar | population standard error of the sample mean |
| s sub x bar | estimate of the standard error of the sample mean |
| sigma sub p bar | population standard error of the sample proportion |
Created by:
clisterwa
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