Self-Entry-Level Test
... − µY . (d) Recall that the mean of Ȳ is µY and its variance is σȲ2 = σY2 /n. According to the central limit theorem, when n is large, the distribution of Ȳ is approximately N (µY , σȲ2 ). The distribution of Ȳ is exactly N (µY , σȲ2 ) when the sample is drawn from a population with the normal ...
... − µY . (d) Recall that the mean of Ȳ is µY and its variance is σȲ2 = σY2 /n. According to the central limit theorem, when n is large, the distribution of Ȳ is approximately N (µY , σȲ2 ). The distribution of Ȳ is exactly N (µY , σȲ2 ) when the sample is drawn from a population with the normal ...
Confidence intervals, missing data and imputation: a salutary
... measure of uncertainty when the cases involved are not randomly sampled, and why the utility of a CI is to be doubted even when the cases are randomly sampled. The paper then describes the methods and findings of a simple simulation, before summing up what can be learnt about the dangers of using co ...
... measure of uncertainty when the cases involved are not randomly sampled, and why the utility of a CI is to be doubted even when the cases are randomly sampled. The paper then describes the methods and findings of a simple simulation, before summing up what can be learnt about the dangers of using co ...
Rare Probability Estimation under Regularly Varying Heavy Tails
... concentration results for the missing mass to all of the rare probabilities, and then using the regular variation property to show multiplicative concentration. Additionally, we construct new families of estimators that address some of the other shortcomings of the Good-Turing estimator. For example ...
... concentration results for the missing mass to all of the rare probabilities, and then using the regular variation property to show multiplicative concentration. Additionally, we construct new families of estimators that address some of the other shortcomings of the Good-Turing estimator. For example ...
Chapter 21
... the true population proportion. • “We are ‘highly confident’ that the true population proportion is contained in the calculated interval.” • Statistically (for a 95% C.I.): in repeated samples, 95% of the calculated confidence intervals should contain the true proportion. Chapter 21 ...
... the true population proportion. • “We are ‘highly confident’ that the true population proportion is contained in the calculated interval.” • Statistically (for a 95% C.I.): in repeated samples, 95% of the calculated confidence intervals should contain the true proportion. Chapter 21 ...
Doc
... Reject H 0 : 1 0 if p-value is less than , (for example, =0.05 ) A 100(1- )% Confidence Interval for a 1parameter ...
... Reject H 0 : 1 0 if p-value is less than , (for example, =0.05 ) A 100(1- )% Confidence Interval for a 1parameter ...
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.