Chapter 9: Problem 465
Discuss and distinguish between discrete and continuous values.
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Chapter 9: Problem 465
Discuss and distinguish between discrete and continuous values.
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Consider the joint distribution of \(\mathrm{X}\) and \(\mathrm{Y}\) given in the form of a table below. The cell (i,j) corresponds to the joint probability that \(\mathrm{X}=\mathrm{i}, \mathrm{Y}=\mathrm{j}\), for \(\mathrm{i}=1,2,3, \mathrm{j}=1,2,3\) $$ \begin{array}{|c|c|c|c|} \hline \mathrm{Y}^{\mathrm{X}} & 1 & 2 & 3 \\ \hline 1 & 0 & 1 / 6 & 1 / 6 \\ \hline 2 & 1 / 6 & 0 & 1 / 6 \\ \hline 3 & 1 / 6 & 1 / 6 & 0 \\ \hline \end{array} $$ Check that this is a proper probability distribution. What is the marginal distribution of \(\mathrm{X} ?\) What is the marginal distribution of \(\mathrm{Y}\) ?
Often frequency data are tabulated according to two criteria, with a view toward testing whether the criteria are associated. Consider the following analysis of the 157 machine breakdowns during a given quarter. Number of Breakdowns We are interested in whether the same percentage of breakdown occurs on each machine during each shift or whether there is some difference due perhaps to untrained operators or other factors peculiar to a given shift.
A die was tossed 120 times and the results are listed below. $$ \begin{array}{|l|l|l|l|l|l|l|} \hline \text { Upturned face } & 1 & 2 & 3 & 4 & 5 & 6 \\ \hline \text { Frequency } & 18 & 23 & 16 & 21 & 18 & 24 \\ \hline \end{array} $$ Compute the \(\mathrm{X}^{2}\) statistic for this 1 by 6 contingency table under the hypothesis that the die was fair.
Given a normal population with \(\mu=25\) and \(\sigma=5\), find the probability that an assumed value of the variable will fall in the interval 20 to 30 .
Show that if \((\mathrm{X}, \mathrm{Y})\) has a bivariate normal distribution, then the marginal distributions of \(\mathrm{X}\) and \(\mathrm{Y}\) are univariate normal distributions; that is, \(\mathrm{X}\) is normally distributed with mean \(\mu_{\mathrm{x}}\) and variance \(\sigma^{2} \mathrm{x}\) and \(\mathrm{Y}\) is normally distributed with mean \(\mu_{\mathrm{y}}\) and variance \(\sigma^{2}{ }_{\mathrm{y}}\).
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