sampling distribution

sampling distribution : In statistics, a sampling distribution is the probability distribution of a given statistic (a numerical quantity calculated from the data values in a sample), based on a random sample. The sampling distribution depends on the distribution of the population, the statistic being considered, and the sample size used. The sampling distribution may be considered as the distribution of the statistic for all possible samples of a given size. For example, consider a very large normal population (one that follows the so-called bell curve). Assume we repeatedly take samples of a given size from the population and calculate the sample mean ($\backslash bar\; x$, the arithmetic mean of the data values) for each sample. Different samples will lead to different sample means. The distribution of these means is the "sampling distribution of the sample mean" (for the given sample size). This distribution will be normal since the population is normal. (According to the central lim....   More from Wikipedia

sampling distribution : In statistics, importance sampling is a general technique for estimating the properties of a particular distribution, while only having samples generated from a different distribution rather than the distribution of interest. Depending on the application, the term may refer to the process of sampling..   More from Wikipedia

  A summary of the logic behind the sampling distribution

  This is a sample problem using the normal sampling distribution.

Question : Hi, I am an undergraduate student doing a business degree. I have a piece of tutorial work on sampling at a lower level than I am sure most of you are used to. I have a question, I have done an NPV calculation using excel with costs and profits (a0, a1, etc.) each of uniform distribution. When the NPV's are analysed they show something near to a normal distribution on the histogram. Why does combing several uniform distributions in a calculation produce a normal distribution? Thanks in adv..

Answer : That is the central limit theorem. Each distribution can be uniform, but the distributions themselves are normally distributed in their range, their value, their frequency. Read the attached link for more details...   More from Yahoo Answers

Question : The mean time to run a race by all of the runners is 60.75 mins and the standard deviation is 4.5 minutes. What is the estimated mean for a sampling distribution of the sample if the sample size is is 30? Also does anyone know what formula to use and if possible can you outline the steps to follow so.

Answer : x-bar (sample mean) would equal the population mean the equation for that is on the bottom of the second page of the AP Stat reference table (if that's the course you're asking this for)..   More from Yahoo Answers

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