To explain what is sampling distribution.

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Answer 1

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Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

Chapter 6, Problem 1FP

To determine

To explain what is sampling distribution.

Expert Solution & Answer

Explanation of Solution

The sampling distribution is a distribution of a sample statistic. When using a procedure that repeatedly samples from a population and each time computes the same sample statistic, the resulting distribution of sample statistics is a sampling distribution of that statistic. Thus, simply we can say that, sampling distribution is a graph of a statistic for your sample data. For example, if we calculate sample mean then our sampling distribution would be the sampling distribution of the sample mean, as it is for median,variance,standard deviation,range etc. Every statistic has a sampling distribution.

The sampling distribution has a mean and standard deviation. The standard deviation of the sampling distribution is called as standard error. The key points for identifying the sampling distribution are that, if many samples are repeatedly taken for the same subjects in the population, then the distribution of the samples represent sampling distribution and if only one sample is taken for the same subjects in the population, then the distribution of the sample is not a sampling distribution.

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Answer 2

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Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

Chapter 6, Problem 1FP

To determine

To explain what is sampling distribution.

Expert Solution & Answer

Explanation of Solution

The sampling distribution is a distribution of a sample statistic. When using a procedure that repeatedly samples from a population and each time computes the same sample statistic, the resulting distribution of sample statistics is a sampling distribution of that statistic. Thus, simply we can say that, sampling distribution is a graph of a statistic for your sample data. For example, if we calculate sample mean then our sampling distribution would be the sampling distribution of the sample mean, as it is for median,variance,standard deviation,range etc. Every statistic has a sampling distribution.

The sampling distribution has a mean and standard deviation. The standard deviation of the sampling distribution is called as standard error. The key points for identifying the sampling distribution are that, if many samples are repeatedly taken for the same subjects in the population, then the distribution of the samples represent sampling distribution and if only one sample is taken for the same subjects in the population, then the distribution of the sample is not a sampling distribution.

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Answer 3

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Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

Chapter 6, Problem 1FP

To determine

To explain what is sampling distribution.

Expert Solution & Answer

Explanation of Solution

The sampling distribution is a distribution of a sample statistic. When using a procedure that repeatedly samples from a population and each time computes the same sample statistic, the resulting distribution of sample statistics is a sampling distribution of that statistic. Thus, simply we can say that, sampling distribution is a graph of a statistic for your sample data. For example, if we calculate sample mean then our sampling distribution would be the sampling distribution of the sample mean, as it is for median,variance,standard deviation,range etc. Every statistic has a sampling distribution.

The sampling distribution has a mean and standard deviation. The standard deviation of the sampling distribution is called as standard error. The key points for identifying the sampling distribution are that, if many samples are repeatedly taken for the same subjects in the population, then the distribution of the samples represent sampling distribution and if only one sample is taken for the same subjects in the population, then the distribution of the sample is not a sampling distribution.

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Answer 4

Question

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

Chapter 6, Problem 1FP

To determine

To explain what is sampling distribution.

Expert Solution & Answer

Explanation of Solution

The sampling distribution is a distribution of a sample statistic. When using a procedure that repeatedly samples from a population and each time computes the same sample statistic, the resulting distribution of sample statistics is a sampling distribution of that statistic. Thus, simply we can say that, sampling distribution is a graph of a statistic for your sample data. For example, if we calculate sample mean then our sampling distribution would be the sampling distribution of the sample mean, as it is for median,variance,standard deviation,range etc. Every statistic has a sampling distribution.

The sampling distribution has a mean and standard deviation. The standard deviation of the sampling distribution is called as standard error. The key points for identifying the sampling distribution are that, if many samples are repeatedly taken for the same subjects in the population, then the distribution of the samples represent sampling distribution and if only one sample is taken for the same subjects in the population, then the distribution of the sample is not a sampling distribution.

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Answer 5

Chapter 6, Problem 1FP

To determine

To explain what is sampling distribution.

Expert Solution & Answer

Explanation of Solution

The sampling distribution is a distribution of a sample statistic. When using a procedure that repeatedly samples from a population and each time computes the same sample statistic, the resulting distribution of sample statistics is a sampling distribution of that statistic. Thus, simply we can say that, sampling distribution is a graph of a statistic for your sample data. For example, if we calculate sample mean then our sampling distribution would be the sampling distribution of the sample mean, as it is for median,variance,standard deviation,range etc. Every statistic has a sampling distribution.

The sampling distribution has a mean and standard deviation. The standard deviation of the sampling distribution is called as standard error. The key points for identifying the sampling distribution are that, if many samples are repeatedly taken for the same subjects in the population, then the distribution of the samples represent sampling distribution and if only one sample is taken for the same subjects in the population, then the distribution of the sample is not a sampling distribution.

Answer 6

Chapter 6, Problem 1FP

To determine

To explain what is sampling distribution.

Expert Solution & Answer

Explanation of Solution

The sampling distribution is a distribution of a sample statistic. When using a procedure that repeatedly samples from a population and each time computes the same sample statistic, the resulting distribution of sample statistics is a sampling distribution of that statistic. Thus, simply we can say that, sampling distribution is a graph of a statistic for your sample data. For example, if we calculate sample mean then our sampling distribution would be the sampling distribution of the sample mean, as it is for median,variance,standard deviation,range etc. Every statistic has a sampling distribution.

The sampling distribution has a mean and standard deviation. The standard deviation of the sampling distribution is called as standard error. The key points for identifying the sampling distribution are that, if many samples are repeatedly taken for the same subjects in the population, then the distribution of the samples represent sampling distribution and if only one sample is taken for the same subjects in the population, then the distribution of the sample is not a sampling distribution.

Answer 7

Chapter 6, Problem 1FP

To determine

To explain what is sampling distribution.

Answer 8

Expert Solution & Answer

Explanation of Solution

The sampling distribution is a distribution of a sample statistic. When using a procedure that repeatedly samples from a population and each time computes the same sample statistic, the resulting distribution of sample statistics is a sampling distribution of that statistic. Thus, simply we can say that, sampling distribution is a graph of a statistic for your sample data. For example, if we calculate sample mean then our sampling distribution would be the sampling distribution of the sample mean, as it is for median,variance,standard deviation,range etc. Every statistic has a sampling distribution.

The sampling distribution has a mean and standard deviation. The standard deviation of the sampling distribution is called as standard error. The key points for identifying the sampling distribution are that, if many samples are repeatedly taken for the same subjects in the population, then the distribution of the samples represent sampling distribution and if only one sample is taken for the same subjects in the population, then the distribution of the sample is not a sampling distribution.