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Underlying distribution is a concept that describes the density for the value of the measurement. It is a theoretical concept.
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What is the application of normal distribution curve in quality control?
If the underlying distribution of the product is normally distributed then (and only then) the normal distribution can be used to identify specimens that are outside the acceptable range.
What affects the standard error of the mean?
The standard error of the underlying distribution, the method of selecting the sample from which the mean is derived, the size of the sample.
What is the The approximate probability of a value occurring that is greater than one standard deviation?
The answer depends on the underlying distribution. For example, if you have a random variable X, with a symmetric distribution with mean = 20 and sd = 1, then prob(X > 1) = 1, to at least 10 decimal places.
How does sample size affect validity of results in a research?
A large sample reduces the variability of the estimate. The extent to which variability is reduced depends on the quality of the sample, what variable is being estimated and the underlying distribution for that variable.
What does it mean to have a normal distribution of data?
The Normal or Gaussian distribution is a probability distribution which depends on two parameters: the mean and the variance (or standard deviation). In may real life situations measurements are found to be approximately normal. Furthermore, even if the underlying distribution of a variable is not normal, the mean of a number of repeated observations of the variable will approximate the normal distribution.The term "approximate" is important because, although the heights of adult males (for example) appear to be normally distributed, the true normal distribution must allow negative heights whereas that is not physically possible!
What is an 0001 probability level?
The answer depends on the underlying distribution.
When the population standard deviation is unknown the sampling distribution is equal to what?
The answer will depend on the underlying distribution for the variable. You may not simply assume that the distribution is normal.
What does distributions free mean in statistics?
It means independent of the underlying distribution.
What is the probability of -1.71 and -0.88?
The answer depends on the underlying distribution. And since you have not bothered to share that crucial bit of information, I cannot provide a more useful answer.Furthermore, if the distribution is continuous, the probability of any specific value is 0.The answer depends on the underlying distribution. And since you have not bothered to share that crucial bit of information, I cannot provide a more useful answer.Furthermore, if the distribution is continuous, the probability of any specific value is 0.The answer depends on the underlying distribution. And since you have not bothered to share that crucial bit of information, I cannot provide a more useful answer.Furthermore, if the distribution is continuous, the probability of any specific value is 0.The answer depends on the underlying distribution. And since you have not bothered to share that crucial bit of information, I cannot provide a more useful answer.Furthermore, if the distribution is continuous, the probability of any specific value is 0.
What is the application of normal distribution curve in quality control?
If the underlying distribution of the product is normally distributed then (and only then) the normal distribution can be used to identify specimens that are outside the acceptable range.
The mean of a sampling distribution is equal to the mean of the underlying population?
This is the Central Limit Theorem.
What do the clusters and gaps in a stem and leaf graph tell you?
They tell you that the underlying distribution is very erratic.
Find the percent of the area between the mean and 0.83 deviations from the mean?
It depends on the underlying distribution.
Is 1.4 unusual in z-score?
No. If the underlying distribution is approximately Normal then 1.4 is not at all unusual.
Which statistics are used to construct a confidence interval?
The parameters of the underlying distribution, plus the standard error of observation.
Why is the normal probability distribution called a family of normal probability distribution?
Because very many variables tend to have the Gaussian distribution. Furthermore, even if the underlying distribution is non-Gaussian, the distribution of the means of repeated samples will be Gaussian. As a result, the Gaussian distributions are also referred to as Normal.
How did the normal distribution get its name?
According to the Central Limit Theorem the sum of [a sufficiently large number of] independent, identically distributed random variables has a Gaussian distribution. This is true irrespective of the underlying distribution of each individual random variable.As a result, many of the measurable variables that we come across have a Gaussian distribution and consequently, it is also called the normal distribution.According to the Central Limit Theorem the sum of [a sufficiently large number of] independent, identically distributed random variables has a Gaussian distribution. This is true irrespective of the underlying distribution of each individual random variable.As a result, many of the measurable variables that we come across have a Gaussian distribution and consequently, it is also called the normal distribution.According to the Central Limit Theorem the sum of [a sufficiently large number of] independent, identically distributed random variables has a Gaussian distribution. This is true irrespective of the underlying distribution of each individual random variable.As a result, many of the measurable variables that we come across have a Gaussian distribution and consequently, it is also called the normal distribution.According to the Central Limit Theorem the sum of [a sufficiently large number of] independent, identically distributed random variables has a Gaussian distribution. This is true irrespective of the underlying distribution of each individual random variable.As a result, many of the measurable variables that we come across have a Gaussian distribution and consequently, it is also called the normal distribution.
