The three differences between nonparametric tests and parametric tests.

Question

Want to see more full solutions like this?

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.

Want to see more full solutions like this?

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

Students have asked these similar questions

Consider the state space model X₁ = §Xt−1 + Wt, Yt = AX+Vt, where Xt Є R4 and Y E R². Suppose we know the covariance matrices for Wt and Vt. How many unknown parameters are there in the model?

Business Discuss

You want to obtain a sample to estimate the proportion of a population that possess a particular genetic marker. Based on previous evidence, you believe approximately p∗=11% of the population have the genetic marker. You would like to be 90% confident that your estimate is within 0.5% of the true population proportion. How large of a sample size is required?n = (Wrong: 10,603) Do not round mid-calculation. However, you may use a critical value accurate to three decimal places.

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.

Want to see more full solutions like this?

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

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.

Want to see more full solutions like this?

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

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.

Want to see more full solutions like this?

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

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.