Which of the following best describes an interval estimator?$\alpha$$\begin{array}{|l|l|} \hline A&\text { An interval estimator is the same as a point estimator.}\\ \hline B&\text {An interval estimator is an interval that draws inferences about a }\\ &\text {population based on a sample statistic. }\\ \hline C&\text { An interval estimator can only be done for the population mean.}\\ \hline D&\text { An interval estimator can only be done for the population proportion.}\\ \hline \end{array}$

The answer of Which of the following best describes an...

If the standard error of the sampling distribution of the sample proportion is 0.0337 for samples of size 200, then the population proportion must be: $\begin{array}{|l|l|} \hline\text { A. } & 0.25 . \\ \hline \text { B. } & 0.75 . \\ \hline \text { C. } & \text { either } 0.20 \text { or } 0.80 \\ \hline \text { D. } & \text { either } 0.35 \text { or } 0.65 \\ \hline \text { E. } & \text { either } 0.30 \text { or } 0.70 \\ \hline \end{array}$

The answer of If the standard error of the sampling...

Exam 11: Estimation: Describing a Single Population

Suppose that the amount of time teenagers spend on the Internet is normally distributed, with a standard deviation of 1.5 hours. A sample of 100 teenagers is selected at random, and the sample mean is computed as 6.5 hours. Determine the 95% confidence interval estimate of the population mean, changing the sample mean to 8.5 hours.

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Question 121

As the number of degrees of freedom for a t-distribution increases: A the dispersion of the distribution decreases. B the shape of the distribution becomes narrower and taller. C the t -distribution becomes more and more similar to the standard normal distribution. D All of these choices are correct.

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Question 122

Suppose that the amount of time teenagers spend on the Internet is normally distributed, with a standard deviation of 1.5 hours. A sample of 100 teenagers is selected at random, and the sample mean is computed as 6.5 hours. Determine the 95% confidence interval estimate of the population mean, changing the sample mean to 5.0 hours.

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Question 123

In developing an interval estimate for a population mean, the population standard deviation $\sigma$ was assumed to be 8. The interval estimate was 50.0 ± 2.50. Had $\sigma$ equalled 16, the interval estimate would have been 100 ± 5.0.

(True/False)

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Question 124

The sample proportion is a biased estimator of the population proportion.

(True/False)

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Question 125

Suppose that the amount of time teenagers spend on the Internet is normally distributed, with a standard deviation of 1.5 hours. A sample of 100 teenagers is selected at random, and the sample mean is computed as 6.5 hours. Determine the 95% confidence interval estimate of the population mean, changing the sample size to 36.

(Short Answer)

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Question 126

Which of the following best describes an interval estimator? $\alpha$ A An interval estimator is the same as a point estimator. B An interval estimator is an interval that draws inferences about a population based on a sample statistic. C An interval estimator can only be done for the population mean. D An interval estimator can only be done for the population proportion.

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Question 127

If the standard error of the sampling distribution of the sample proportion is 0.0337 for samples of size 200, then the population proportion must be: A. 0.25. B. 0.75. C. either 0.20 or 0.80 D. either 0.35 or 0.65 E. either 0.30 or 0.70

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Question 128

The degrees of freedom used to find the t_/2 for a confidence interval for the population mean? A The degrees of freedom are n-2 . B The degrees of freedom are n-3 . C The degrees of freedom are n-1 . D The degrees of freedom are n .

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Question 129

A random sample of size 20 taken from a normally distributed population resulted in a sample variance of 32. The lower limit of a 90% confidence interval for the population variance would be: A. 52.185. B. 20.375. C. 20.170. D. 54.931.

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Question 130

We cannot interpret the confidence interval estimate of $\mu$ as a probability statement about $\mu$ , simply because the population mean is a fixed but unknown quantity.

(True/False)

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Question 131

The sample proportion $\hat { p }$ is a consistent estimator of the population proportion p because it is unbiased and the variance of $\hat { p }$ is p(1 - p)/n, which grows smaller as n grows larger.

(True/False)

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Question 132

Showing 121 - 132 of 132

In developing an interval estimate for a population mean, the population standard deviation $\sigma$ was assumed to be 8. The interval estimate was 50.0 ± 2.50. Had $\sigma$ equalled 16, the interval estimate would have been 100 ± 5.0.

The sample proportion is a biased estimator of the population proportion.

If the standard error of the sampling distribution of the sample proportion is 0.0337 for samples of size 200, then the population proportion must be: A. 0.25. B. 0.75. C. either 0.20 or 0.80 D. either 0.35 or 0.65 E. either 0.30 or 0.70

The degrees of freedom used to find the t_/2 for a confidence interval for the population mean? A The degrees of freedom are n-2 . B The degrees of freedom are n-3 . C The degrees of freedom are n-1 . D The degrees of freedom are n .

A random sample of size 20 taken from a normally distributed population resulted in a sample variance of 32. The lower limit of a 90% confidence interval for the population variance would be: A. 52.185. B. 20.375. C. 20.170. D. 54.931.

We cannot interpret the confidence interval estimate of $\mu$ as a probability statement about $\mu$ , simply because the population mean is a fixed but unknown quantity.

The sample proportion $\hat { p }$ is a consistent estimator of the population proportion p because it is unbiased and the variance of $\hat { p }$ is p(1 - p)/n, which grows smaller as n grows larger.

What Is Statistics

Types of Data, Data Collection and Sampling

Graphical Descriptive Methods Nominal Data

Graphical Descriptive Techniques Numerical Data

Numerical Descriptive Measures

Probability

Random Variables and Discrete Probability Distributions

Continuous Probability Distributions

Statistical Inference: Introduction

Sampling Distributions

Estimation: Comparing Two Populations

Hypothesis Testing: Describing a Single Population

Hypothesis Testing: Comparing Two Populations

Inference About Population Variances

Analysis of Variance

Additional Tests for Nominal Data: Chi-Squared Tests

Simple Linear Regression and Correlation

Multiple Regression

Model Building

Nonparametric Techniques

Statistical Inference: Conclusion

Time-Series Analysis and Forecasting

Index Numbers

Decision Analysis

Filters

Exam 11: Estimation: Describing a Single Population

In developing an interval estimate for a population mean, the population standard deviation σ\sigmaσ was assumed to be 8. The interval estimate was 50.0 ± 2.50. Had σ\sigmaσ equalled 16, the interval estimate would have been 100 ± 5.0.

The sample proportion is a biased estimator of the population proportion.

If the standard error of the sampling distribution of the sample proportion is 0.0337 for samples of size 200, then the population proportion must be: A. 0.25. B. 0.75. C. either 0.20 or 0.80 D. either 0.35 or 0.65 E. either 0.30 or 0.70

The degrees of freedom used to find the t/2 for a confidence interval for the population mean? A The degrees of freedom are n-2 . B The degrees of freedom are n-3 . C The degrees of freedom are n-1 . D The degrees of freedom are n .

A random sample of size 20 taken from a normally distributed population resulted in a sample variance of 32. The lower limit of a 90% confidence interval for the population variance would be: A. 52.185. B. 20.375. C. 20.170. D. 54.931.

We cannot interpret the confidence interval estimate of μ\muμ as a probability statement about μ\muμ , simply because the population mean is a fixed but unknown quantity.

The sample proportion p^\hat { p }p^​ is a consistent estimator of the population proportion p because it is unbiased and the variance of p^\hat { p }p^​ is p(1 - p)/n, which grows smaller as n grows larger.

What Is Statistics

Types of Data, Data Collection and Sampling

Graphical Descriptive Methods Nominal Data

Graphical Descriptive Techniques Numerical Data

Numerical Descriptive Measures

Probability

Random Variables and Discrete Probability Distributions

Continuous Probability Distributions

Statistical Inference: Introduction

Sampling Distributions

Estimation: Comparing Two Populations

Hypothesis Testing: Describing a Single Population

Hypothesis Testing: Comparing Two Populations

Inference About Population Variances

Analysis of Variance

Additional Tests for Nominal Data: Chi-Squared Tests

Simple Linear Regression and Correlation

Multiple Regression

Model Building

Nonparametric Techniques

Statistical Inference: Conclusion

Time-Series Analysis and Forecasting

Index Numbers

Decision Analysis

Filters

In developing an interval estimate for a population mean, the population standard deviation $\sigma$ was assumed to be 8. The interval estimate was 50.0 ± 2.50. Had $\sigma$ equalled 16, the interval estimate would have been 100 ± 5.0.

The degrees of freedom used to find the t_/2 for a confidence interval for the population mean? A The degrees of freedom are n-2 . B The degrees of freedom are n-3 . C The degrees of freedom are n-1 . D The degrees of freedom are n .

We cannot interpret the confidence interval estimate of $\mu$ as a probability statement about $\mu$ , simply because the population mean is a fixed but unknown quantity.

The sample proportion $\hat { p }$ is a consistent estimator of the population proportion p because it is unbiased and the variance of $\hat { p }$ is p(1 - p)/n, which grows smaller as n grows larger.