Confidence Interval
... o Appendix B.1, page 784 o (Student’s t distribution) o Appendix B.2, page 785 o Binomial Probability Distribution o Appendix B.9, pages 794,798 n x p( x) P( X x) p (1 p) n x x ...
... o Appendix B.1, page 784 o (Student’s t distribution) o Appendix B.2, page 785 o Binomial Probability Distribution o Appendix B.9, pages 794,798 n x p( x) P( X x) p (1 p) n x x ...
Sampling distributions and estimation
... If we choose n items from a population, we say that the size of the sample is n. If we take many samples, the means of these samples will themselves have a distribution which may be different from the population from which the samples were chosen. Much of the practical application of sampling theory ...
... If we choose n items from a population, we say that the size of the sample is n. If we take many samples, the means of these samples will themselves have a distribution which may be different from the population from which the samples were chosen. Much of the practical application of sampling theory ...
Exam 3 Study Guide
... 1. First, see if the outliers are due to an error. Try to correct them (if they are errors) or justify their removal (if possible) before computing the interval. If the outliers cannot be removed, you should not use the confidence interval formula given in section 6.1. (See p.393.) 2. The margin of ...
... 1. First, see if the outliers are due to an error. Try to correct them (if they are errors) or justify their removal (if possible) before computing the interval. If the outliers cannot be removed, you should not use the confidence interval formula given in section 6.1. (See p.393.) 2. The margin of ...
Grading of Oct 22 homework: If the student made some
... Substituting this formula for the formula above it (used when we have a sample but don’t have information about the whole population) would be easy to do except that you are only just learning the terminology. Consequently, this slightly different formula uses terms that you are less familiar with ...
... Substituting this formula for the formula above it (used when we have a sample but don’t have information about the whole population) would be easy to do except that you are only just learning the terminology. Consequently, this slightly different formula uses terms that you are less familiar with ...
Behboodian, J.; (1971)Bayesian estimation for the proportions in a mixture of distribution."
... and we can eliminate such non-informative ...
... and we can eliminate such non-informative ...
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.