Determine why power is greater when a non-parametric test is used for data that are not normally distributed .

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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.

Chapter 18, Problem 29PR

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Determine why power is greater when a non-parametric test is used for data that are not normally distributed.

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Answer 2

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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.

Chapter 18, Problem 29PR

To determine

Determine why power is greater when a non-parametric test is used for data that are not normally distributed.

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Answer 3

Question

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.

Chapter 18, Problem 29PR

To determine

Determine why power is greater when a non-parametric test is used for data that are not normally distributed.

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Answer 4

Question

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.

Chapter 18, Problem 29PR

To determine

Determine why power is greater when a non-parametric test is used for data that are not normally distributed.

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Answer 5

Chapter 18, Problem 29PR

To determine

Determine why power is greater when a non-parametric test is used for data that are not normally distributed.

Answer 6

Chapter 18, Problem 29PR

To determine

Determine why power is greater when a non-parametric test is used for data that are not normally distributed.

Answer 7

Chapter 18, Problem 29PR

To determine

Determine why power is greater when a non-parametric test is used for data that are not normally distributed.

Answer 8

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Answer 11

Ch. 18.1 - Prob. 1.1LC Ch. 18.1 - Prob. 1.2LC Ch. 18.1 - Prob. 1.3LC Ch. 18.3 - Prob. 2.1LC Ch. 18.3 - Prob. 2.2LC Ch. 18.3 - Prob. 2.3LC Ch. 18.3 - Prob. 2.4LC Ch. 18.5 - Prob. 3.1LC Ch. 18.5 - Prob. 3.2LC Ch. 18.5 - Prob. 3.3LC

Determine why power is greater when a non-parametric test is used for data that are not normally distributed .

Solution Summary: The author explains why power is greater when a non-parametric test is used for data that are not normally distributed because variability in the data is reduced.

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Determine why power is greater when a non-parametric test is used for data that are not normally distributed.

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