The three differences between nonparametric tests and parametric tests.

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Answer 1

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 14, Problem 1FP

To determine

To explain: The three differences between nonparametric tests and parametric tests.

Expert Solution & Answer

Explanation of Solution

The three ways that nonparametric tests differ from parametric tests are shown below

Nonparametric tests are used when the test of hypothesis does not make inferences about the population parameter, although they can be used to test hypothesized relationships in a population whereas parametric tests hypothesis make inferences about the population parameters.
In Nonparametric test, it does not require the population to be normally distributed and does not make any assumptions. These tests are considered to be distribution free tests as the data can have of any type of distribution, whereas parametric tests make assumptions and assume that population distribution is approximately normal. Parametric tests depend on the distribution of Population.
Nonparametric tests can be used to analyze the data on a nominal or ordinal scale of measurement without any assumptions. Though parametric tests can be used for nominal or ordinal data, it requires many assumptions to be made and many conditions to be met.

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Answer 2

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 14, Problem 1FP

To determine

To explain: The three differences between nonparametric tests and parametric tests.

Expert Solution & Answer

Explanation of Solution

The three ways that nonparametric tests differ from parametric tests are shown below

Nonparametric tests are used when the test of hypothesis does not make inferences about the population parameter, although they can be used to test hypothesized relationships in a population whereas parametric tests hypothesis make inferences about the population parameters.
In Nonparametric test, it does not require the population to be normally distributed and does not make any assumptions. These tests are considered to be distribution free tests as the data can have of any type of distribution, whereas parametric tests make assumptions and assume that population distribution is approximately normal. Parametric tests depend on the distribution of Population.
Nonparametric tests can be used to analyze the data on a nominal or ordinal scale of measurement without any assumptions. Though parametric tests can be used for nominal or ordinal data, it requires many assumptions to be made and many conditions to be met.

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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 14, Problem 1FP

To determine

To explain: The three differences between nonparametric tests and parametric tests.

Expert Solution & Answer

Explanation of Solution

The three ways that nonparametric tests differ from parametric tests are shown below

Nonparametric tests are used when the test of hypothesis does not make inferences about the population parameter, although they can be used to test hypothesized relationships in a population whereas parametric tests hypothesis make inferences about the population parameters.
In Nonparametric test, it does not require the population to be normally distributed and does not make any assumptions. These tests are considered to be distribution free tests as the data can have of any type of distribution, whereas parametric tests make assumptions and assume that population distribution is approximately normal. Parametric tests depend on the distribution of Population.
Nonparametric tests can be used to analyze the data on a nominal or ordinal scale of measurement without any assumptions. Though parametric tests can be used for nominal or ordinal data, it requires many assumptions to be made and many conditions to be met.

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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 14, Problem 1FP

To determine

To explain: The three differences between nonparametric tests and parametric tests.

Expert Solution & Answer

Explanation of Solution

The three ways that nonparametric tests differ from parametric tests are shown below

Nonparametric tests are used when the test of hypothesis does not make inferences about the population parameter, although they can be used to test hypothesized relationships in a population whereas parametric tests hypothesis make inferences about the population parameters.
In Nonparametric test, it does not require the population to be normally distributed and does not make any assumptions. These tests are considered to be distribution free tests as the data can have of any type of distribution, whereas parametric tests make assumptions and assume that population distribution is approximately normal. Parametric tests depend on the distribution of Population.
Nonparametric tests can be used to analyze the data on a nominal or ordinal scale of measurement without any assumptions. Though parametric tests can be used for nominal or ordinal data, it requires many assumptions to be made and many conditions to be met.

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Answer 5

Chapter 14, Problem 1FP

To determine

To explain: The three differences between nonparametric tests and parametric tests.

Expert Solution & Answer

Explanation of Solution

The three ways that nonparametric tests differ from parametric tests are shown below

Nonparametric tests are used when the test of hypothesis does not make inferences about the population parameter, although they can be used to test hypothesized relationships in a population whereas parametric tests hypothesis make inferences about the population parameters.
In Nonparametric test, it does not require the population to be normally distributed and does not make any assumptions. These tests are considered to be distribution free tests as the data can have of any type of distribution, whereas parametric tests make assumptions and assume that population distribution is approximately normal. Parametric tests depend on the distribution of Population.
Nonparametric tests can be used to analyze the data on a nominal or ordinal scale of measurement without any assumptions. Though parametric tests can be used for nominal or ordinal data, it requires many assumptions to be made and many conditions to be met.

Answer 6

Chapter 14, Problem 1FP

To determine

To explain: The three differences between nonparametric tests and parametric tests.

Expert Solution & Answer

Explanation of Solution

The three ways that nonparametric tests differ from parametric tests are shown below

Nonparametric tests are used when the test of hypothesis does not make inferences about the population parameter, although they can be used to test hypothesized relationships in a population whereas parametric tests hypothesis make inferences about the population parameters.
In Nonparametric test, it does not require the population to be normally distributed and does not make any assumptions. These tests are considered to be distribution free tests as the data can have of any type of distribution, whereas parametric tests make assumptions and assume that population distribution is approximately normal. Parametric tests depend on the distribution of Population.
Nonparametric tests can be used to analyze the data on a nominal or ordinal scale of measurement without any assumptions. Though parametric tests can be used for nominal or ordinal data, it requires many assumptions to be made and many conditions to be met.

Answer 7

Chapter 14, Problem 1FP

To determine

To explain: The three differences between nonparametric tests and parametric tests.

Answer 8

Expert Solution & Answer

Explanation of Solution

The three ways that nonparametric tests differ from parametric tests are shown below

Nonparametric tests are used when the test of hypothesis does not make inferences about the population parameter, although they can be used to test hypothesized relationships in a population whereas parametric tests hypothesis make inferences about the population parameters.
In Nonparametric test, it does not require the population to be normally distributed and does not make any assumptions. These tests are considered to be distribution free tests as the data can have of any type of distribution, whereas parametric tests make assumptions and assume that population distribution is approximately normal. Parametric tests depend on the distribution of Population.
Nonparametric tests can be used to analyze the data on a nominal or ordinal scale of measurement without any assumptions. Though parametric tests can be used for nominal or ordinal data, it requires many assumptions to be made and many conditions to be met.