The computational formula for SS
... important distribution, to which we will return shortly.] The mean of the sampling distribution should equal the population parameter that one wants to estimate. So, if you wanted to estimate the parameter , the population mean, the sample mean would be an accurate statistic, because the average (m ...
... important distribution, to which we will return shortly.] The mean of the sampling distribution should equal the population parameter that one wants to estimate. So, if you wanted to estimate the parameter , the population mean, the sample mean would be an accurate statistic, because the average (m ...
Using Microsoft Excel for Probability and Statistics
... Descriptive statistics: this tool provides a collection of sample statistics for a given range of data, including sample mean, variance, median, quartiles, mode and a number of others. F-Test Two-Sample for Variances: tell it where to find the two samples and this procedure will give you a one-sided ...
... Descriptive statistics: this tool provides a collection of sample statistics for a given range of data, including sample mean, variance, median, quartiles, mode and a number of others. F-Test Two-Sample for Variances: tell it where to find the two samples and this procedure will give you a one-sided ...
... important distribution, to which we will return shortly.] The mean of the sampling distribution should equal the population parameter that one wants to estimate. So, if you wanted to estimate the parameter , the population mean, the sample mean would be an accurate statistic, because the average (m ...
German tank problem
In the statistical theory of estimation, the problem of estimating the maximum of a discrete uniform distribution from sampling without replacement is known in English as the German tank problem, due to its application in World War II to the estimation of the number of German tanks.The analyses illustrate the difference between frequentist inference and Bayesian inference.Estimating the population maximum based on a single sample yields divergent results, while the estimation based on multiple samples is an instructive practical estimation question whose answer is simple but not obvious.