The difference between Parametric and Non-parametric test primarily in terms of assumptions and data.

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Answer 1

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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 15, Problem 1P

To determine

To find: The difference between Parametric and Non-parametric test primarily in terms of assumptions and data.

Expert Solution & Answer

Explanation of Solution

In a nonparametric test data is not required to fit a normal distribution, in-fact, it assumes that the data does not follow any specific distribution. Whereas in case of parametric test, the data follows a normal distribution.

The information about population parameter is not available in nonparametric test.

Non-parametric test uses data that is often ordinal, meaning it does not rely on numbers, but rather a ranking or order of sorts. Parametric test uses data which is interval or ratio.

The nonparametric test is mainly based on differences in medians. Hence, it is alternately known as the distribution-free test. Whereas, parametric test uses mean as a measure of central tendency.

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A well-known company predominantly makes flat pack furniture for students. Variability with the automated machinery means the wood components are cut with a standard deviation in length of 0.45 mm. After they are cut the components are measured. If their length is more than 1.2 mm from the required length, the components are rejected. a) Calculate the percentage of components that get rejected. b) In a manufacturing run of 1000 units, how many are expected to be rejected? c) The company wishes to install more accurate equipment in order to reduce the rejection rate by one-half, using the same ±1.2mm rejection criterion. Calculate the maximum acceptable standard deviation of the new process.

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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 15, Problem 1P

To determine

To find: The difference between Parametric and Non-parametric test primarily in terms of assumptions and data.

Expert Solution & Answer

Explanation of Solution

In a nonparametric test data is not required to fit a normal distribution, in-fact, it assumes that the data does not follow any specific distribution. Whereas in case of parametric test, the data follows a normal distribution.

The information about population parameter is not available in nonparametric test.

Non-parametric test uses data that is often ordinal, meaning it does not rely on numbers, but rather a ranking or order of sorts. Parametric test uses data which is interval or ratio.

The nonparametric test is mainly based on differences in medians. Hence, it is alternately known as the distribution-free test. Whereas, parametric test uses mean as a measure of central tendency.

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Answer 3

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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 15, Problem 1P

To determine

To find: The difference between Parametric and Non-parametric test primarily in terms of assumptions and data.

Expert Solution & Answer

Explanation of Solution

In a nonparametric test data is not required to fit a normal distribution, in-fact, it assumes that the data does not follow any specific distribution. Whereas in case of parametric test, the data follows a normal distribution.

The information about population parameter is not available in nonparametric test.

Non-parametric test uses data that is often ordinal, meaning it does not rely on numbers, but rather a ranking or order of sorts. Parametric test uses data which is interval or ratio.

The nonparametric test is mainly based on differences in medians. Hence, it is alternately known as the distribution-free test. Whereas, parametric test uses mean as a measure of central tendency.

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Answer 4

Question

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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 15, Problem 1P

To determine

To find: The difference between Parametric and Non-parametric test primarily in terms of assumptions and data.

Expert Solution & Answer

Explanation of Solution

In a nonparametric test data is not required to fit a normal distribution, in-fact, it assumes that the data does not follow any specific distribution. Whereas in case of parametric test, the data follows a normal distribution.

The information about population parameter is not available in nonparametric test.

Non-parametric test uses data that is often ordinal, meaning it does not rely on numbers, but rather a ranking or order of sorts. Parametric test uses data which is interval or ratio.

The nonparametric test is mainly based on differences in medians. Hence, it is alternately known as the distribution-free test. Whereas, parametric test uses mean as a measure of central tendency.

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Answer 5

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Answer 6

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Answer 7

Chapter 15, Problem 1P

To determine

To find: The difference between Parametric and Non-parametric test primarily in terms of assumptions and data.

Expert Solution & Answer

Explanation of Solution

In a nonparametric test data is not required to fit a normal distribution, in-fact, it assumes that the data does not follow any specific distribution. Whereas in case of parametric test, the data follows a normal distribution.

The information about population parameter is not available in nonparametric test.

Non-parametric test uses data that is often ordinal, meaning it does not rely on numbers, but rather a ranking or order of sorts. Parametric test uses data which is interval or ratio.

The nonparametric test is mainly based on differences in medians. Hence, it is alternately known as the distribution-free test. Whereas, parametric test uses mean as a measure of central tendency.

Answer 8

Chapter 15, Problem 1P

To determine

To find: The difference between Parametric and Non-parametric test primarily in terms of assumptions and data.

Expert Solution & Answer

Explanation of Solution

In a nonparametric test data is not required to fit a normal distribution, in-fact, it assumes that the data does not follow any specific distribution. Whereas in case of parametric test, the data follows a normal distribution.

The information about population parameter is not available in nonparametric test.

Non-parametric test uses data that is often ordinal, meaning it does not rely on numbers, but rather a ranking or order of sorts. Parametric test uses data which is interval or ratio.

The nonparametric test is mainly based on differences in medians. Hence, it is alternately known as the distribution-free test. Whereas, parametric test uses mean as a measure of central tendency.

Answer 9

Chapter 15, Problem 1P

To determine

To find: The difference between Parametric and Non-parametric test primarily in terms of assumptions and data.

Answer 10

Expert Solution & Answer

Explanation of Solution

In a nonparametric test data is not required to fit a normal distribution, in-fact, it assumes that the data does not follow any specific distribution. Whereas in case of parametric test, the data follows a normal distribution.

The information about population parameter is not available in nonparametric test.

Non-parametric test uses data that is often ordinal, meaning it does not rely on numbers, but rather a ranking or order of sorts. Parametric test uses data which is interval or ratio.

The nonparametric test is mainly based on differences in medians. Hence, it is alternately known as the distribution-free test. Whereas, parametric test uses mean as a measure of central tendency.

Answer 11

Expert Solution & Answer

Answer 12

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Answer 13

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The difference between Parametric and Non-parametric test primarily in terms of assumptions and data.

To find: The difference between Parametric and Non-parametric test primarily in terms of assumptions and data.

Explanation of Solution

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