Confidence Intervals
... We just saw that the interval X ± 2 √σn contains all values µ that are “compatible” with the observed data X. (If we were to test that the mean of the box is any of these values, the P -value would be at least 2.5%.) Another way to interpret this is to imagine the interval as random. If we were to c ...
... We just saw that the interval X ± 2 √σn contains all values µ that are “compatible” with the observed data X. (If we were to test that the mean of the box is any of these values, the P -value would be at least 2.5%.) Another way to interpret this is to imagine the interval as random. If we were to c ...
Gamma Function for Different Negative Numbers
... function for different numbers is shown in figure 1 so that for helping to develop and obtain the gamma function for different negative numbers. Also( Abramowitz,1965) and( Tuma, 1970) viewed and dealt with calculation of the gamma function (Γn) for positive numbers and some negative numbers.(Buck,1 ...
... function for different numbers is shown in figure 1 so that for helping to develop and obtain the gamma function for different negative numbers. Also( Abramowitz,1965) and( Tuma, 1970) viewed and dealt with calculation of the gamma function (Γn) for positive numbers and some negative numbers.(Buck,1 ...
Credits: Three
... One week before launching the new zPhone, the cell phone company takes another survey of their current customers to find out how many are planning on buying the zPhone. They randomly sampled 100 current customers and found that the standard error for the estimate was twice the size of the standard e ...
... One week before launching the new zPhone, the cell phone company takes another survey of their current customers to find out how many are planning on buying the zPhone. They randomly sampled 100 current customers and found that the standard error for the estimate was twice the size of the standard e ...
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.