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In a set of matched samples, each data value in one sample is related to or matched with a corresponding data value in the other sample.

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True

An appropriate sampling plan to determine if there is a difference in the speed of a wireless router from two different manufacturers, consists of a network manager drawing independent samples of wireless routers from the two manufacturers and comparing the difference in the sample means for the connection speed.

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True

If a 98% confidence interval for the difference in the two population means does not contain zero, then the null hypothesis of a zero difference between the two population means cannot be rejected at a 0.02 level of significance.

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False

Hypothesis tests conducted on sets of matched samples are sometimes referred to as correlated t tests.

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In order to construct an interval estimate of the difference in the means of two normally distributed populations with unknown but equal variances, using two independent samples of size n_{1} and n_{2}, we must use a t distribution with (n_{1 }+ n_{2 }− 2)degrees of freedom.

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If the sample sizes are small, but the populations are normally distributed and the population variances are known, the z-statistic can be used as the basis for statistical inferences about the difference in two population means using two independent random samples.

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When finding a confidence interval for the difference between two population proportions we use a pooled estimate of the proportion.

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If a 90% confidence interval for the difference in the two population means contains zero, then the null hypothesis of zero difference between the two population means cannot be rejected at a 0.10 level of significance.

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If the populations are normally distributed and the variances of the two populations are equal, it is appropriate to use the "pooled" formula to determine the t-statistic for the hypothesis test of the difference in the two population means.

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If the populations are normally distributed but the population variances are unknown the t-statistic can be used as the basis for statistical inferences about the difference in two population means using two independent random samples.

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If the populations are normally distributed and the variances of the two populations are not equal, it is appropriate to use the "unpooled" formula to determine the t-statistic for the hypothesis test of the difference in the two population means.

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The difference in two sample means is normally distributed for sample sizes ≥ 30, only if the populations are normally distributed.

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If the populations are normally distributed but the population variances are unknown the z-statistic can be used as the basis for statistical inferences about the difference in two population means using two independent random samples.

True False

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In conducting a matched-pairs hypothesis test, the null and alternative hypotheses always represent one-tailed tests.

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In order to construct an interval estimate of the difference in the means of two normally distributed populations with unknown but equal variances, using two independent samples of size n_{1} and n_{2}, we must use a t distribution with (n_{1 }+ n_{2}_{ }-1)degrees of freedom.

True False

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If the sample sizes are greater than 30 and the population variances are known, the basis for statistical inferences about the difference in two population means using two independent random samples is the z-statistic, regardless of the shapes of the population distributions.

True False

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Testing the difference in two population proportions is useful whenever the researcher is interested in comparing the proportion of one population that has certain characteristic with the proportion of the second population that has the same characteristic.

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When testing for the difference between two population proportions we use a pooled estimate of the proportion.

True False

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If the variances of the two populations are not equal, it is appropriate to use the "pooled" formula to determine the t-statistic for the hypothesis test of the difference in the two population means.

True False

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