Introduction to Estimation
... The model requires information about the mean demand during delivery lead time between a central manufacturing facility and local warehouses. Past experience indicates that lead-time demand is normally distributed with a standard deviation of 75 computers per lead time (which is also random). Constr ...
... The model requires information about the mean demand during delivery lead time between a central manufacturing facility and local warehouses. Past experience indicates that lead-time demand is normally distributed with a standard deviation of 75 computers per lead time (which is also random). Constr ...
Lecture 3: Statistical sampling uncertainty
... If we are trying to estimate some X which we have reason to believe will be approximately n(x, σm [x]), then the normal distribution tells us the probability X lies within any given range (Fig. 2). About (2/3, 95%, 99.7%) of the time, X will lie within (1, 2, 3)σm [x] of x. These estimates need to b ...
... If we are trying to estimate some X which we have reason to believe will be approximately n(x, σm [x]), then the normal distribution tells us the probability X lies within any given range (Fig. 2). About (2/3, 95%, 99.7%) of the time, X will lie within (1, 2, 3)σm [x] of x. These estimates need to b ...
1 Reminder of Definitions 2 Unknown Population Standard
... Now suppose that the sample size is at least 2 but less than 30. Because we can not use the central limit theorem here, we must assume that the population data set is normally distributed in order to conclude that x̄ − µσx̄ follows a standard normal distribution. However, now it is no longer true th ...
... Now suppose that the sample size is at least 2 but less than 30. Because we can not use the central limit theorem here, we must assume that the population data set is normally distributed in order to conclude that x̄ − µσx̄ follows a standard normal distribution. However, now it is no longer true th ...
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.