Specify the differences between a large-sample and small-sample test of hypothesis about a population mean α Focus on the assumptions and test statistics.

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Answer 1

Textbook Question

Chapter 7, Problem 7.108LM

Specify the differences between a large-sample and small-sample test of hypothesis about a population mean α Focus on the assumptions and test statistics.

Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.

Expert Solution

To determine

To explain: The difference between the large samples and small samples for the hypothesis about a population mean.

Explanation of Solution

The hypothesis test about a population mean for a larger sample does not require any assumption because the central limit theorem states that the test statistic approximately has a normal distribution. For a small sample, assume that the population being sampled is normal.

For a large sample, the test statistic is the z-statistic.
For a small sample, the test statistic is the t statistic.

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I just need to know why this is wrong below: What is the test statistic W? W=5 (incorrect) and What is the p-value of this test? (p-value < 0.001-- incorrect) Use the Wilcoxon signed rank test to test the hypothesis that the median number of pages in the statistics books in the library from which the sample was taken is 400. A sample of 12 statistics books have the following numbers of pages pages 127 217 486 132 397 297 396 327 292 256 358 272 What is the sum of the negative ranks (W-)? 75 What is the sum of the positive ranks (W+)? 5What type of test is this? two tailedWhat is the test statistic W? 5 These are the critical values for a 1-tailed Wilcoxon Signed Rank test for n=12 Alpha Level 0.001 0.005 0.01 0.025 0.05 0.1 0.2 Critical Value 75 70 68 64 60 56 50 What is the p-value for this test? p-value < 0.001

Answer 2

Textbook Question

Chapter 7, Problem 7.108LM

Specify the differences between a large-sample and small-sample test of hypothesis about a population mean α Focus on the assumptions and test statistics.

Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.

Expert Solution

To determine

To explain: The difference between the large samples and small samples for the hypothesis about a population mean.

Explanation of Solution

The hypothesis test about a population mean for a larger sample does not require any assumption because the central limit theorem states that the test statistic approximately has a normal distribution. For a small sample, assume that the population being sampled is normal.

For a large sample, the test statistic is the z-statistic.
For a small sample, the test statistic is the t statistic.

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Answer 3

Textbook Question

Chapter 7, Problem 7.108LM

Specify the differences between a large-sample and small-sample test of hypothesis about a population mean α Focus on the assumptions and test statistics.

Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.

Expert Solution

To determine

To explain: The difference between the large samples and small samples for the hypothesis about a population mean.

Explanation of Solution

The hypothesis test about a population mean for a larger sample does not require any assumption because the central limit theorem states that the test statistic approximately has a normal distribution. For a small sample, assume that the population being sampled is normal.

For a large sample, the test statistic is the z-statistic.
For a small sample, the test statistic is the t statistic.

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Answer 4

Textbook Question

Chapter 7, Problem 7.108LM

Specify the differences between a large-sample and small-sample test of hypothesis about a population mean α Focus on the assumptions and test statistics.

Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.

Expert Solution

To determine

To explain: The difference between the large samples and small samples for the hypothesis about a population mean.

Explanation of Solution

The hypothesis test about a population mean for a larger sample does not require any assumption because the central limit theorem states that the test statistic approximately has a normal distribution. For a small sample, assume that the population being sampled is normal.

For a large sample, the test statistic is the z-statistic.
For a small sample, the test statistic is the t statistic.

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Subscribe now to access step-by-step solutions to millions of textbook problems written by subject matter experts!

Answer 5

Textbook Question

Answer 6

Textbook Question

Answer 7

Expert Solution

To determine

To explain: The difference between the large samples and small samples for the hypothesis about a population mean.

Explanation of Solution

The hypothesis test about a population mean for a larger sample does not require any assumption because the central limit theorem states that the test statistic approximately has a normal distribution. For a small sample, assume that the population being sampled is normal.

For a large sample, the test statistic is the z-statistic.
For a small sample, the test statistic is the t statistic.

Answer 8

Expert Solution

To determine

To explain: The difference between the large samples and small samples for the hypothesis about a population mean.

Explanation of Solution

The hypothesis test about a population mean for a larger sample does not require any assumption because the central limit theorem states that the test statistic approximately has a normal distribution. For a small sample, assume that the population being sampled is normal.

For a large sample, the test statistic is the z-statistic.
For a small sample, the test statistic is the t statistic.

Answer 9

Expert Solution

Answer 10

Expert Solution

Answer 11

Expert Solution

Answer 12

To determine

To explain: The difference between the large samples and small samples for the hypothesis about a population mean.

Answer 13

Explanation of Solution

The hypothesis test about a population mean for a larger sample does not require any assumption because the central limit theorem states that the test statistic approximately has a normal distribution. For a small sample, assume that the population being sampled is normal.

For a large sample, the test statistic is the z-statistic.
For a small sample, the test statistic is the t statistic.

Answer 14

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Specify the differences between a large-sample and small-sample test of hypothesis about a population mean α Focus on the assumptions and test statistics.

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