The Variance Estimation Experiment

]> The Variance Estimation Experiment

Description

The experiment is to select a random sample of size $n$ from a specified distribution, and then to construct an approximate confidence interval for the standard deviation at a specified confidence level.

The distribution can be chosen with a list box; the options are

In each case, the appropriate parameters and the sample size can be varied with scroll bars. The probability density function, mean, and standard deviation of the selected distribution are shown in blue in the first graph.

The confidence level can be selected from a list box, as can the type of interval--two sided, upper bound, or lower bound. The interval can be constructed assuming either that the distribution mean is known or unknown. In the first case the pivot variable $V$ has the chi-square distribution with $n$ degrees of freedom; in the second case the pivot variable $V$ has the chi-square distribution with $n 1$ degrees of freedom. The density and the critical values of $V$ are shown in blue in the second graph.

Random variables $L$ and $R$ denote the left and right endpoints of the confidence interval and $I$ indicates the event that the confidence interval contains the distribution mean. The theoretical probability density function of $I$ is shown in blue in the third graph.

On each update, the sample density and the confidence interval are shown in red in the first graph, and the value of $V$ is shown in red in the second graph. Note that the confidence interval contains the mean in the first graph if and only if $V$ falls between the critical values in the second graph. The third graph shows the proportion of successes and failures in red. The first table records $L$ , $R$ , $V$ , and $I$ . The second table shows the current sample. Finally, the third table gives the theoretical and empirical probability density functions of $I$ .