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What is the proportion of observations from a standard Normal distribution that take values greater than 0.84?
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How does the standard normal distribution differ from the t-distribution?
The normal distribution and the t-distribution are both symmetric bell-shaped continuous probability distribution functions. The t-distribution has heavier tails: the probability of observations further from the mean is greater than for the normal distribution. There are other differences in terms of when it is appropriate to use them. Finally, the standard normal distribution is a special case of a normal distribution such that the mean is 0 and the standard deviation is 1.
Can a standard deviation be greater than its mean?
Yes - but the distribution is not a normal distribution - this can happen with a distribution that has a very long tail.
Statistic question help?
When using Chebyshev's Theorem the minimum percentage of sample observations that will fall within two standard deviations of the mean will be __________ the percentage within two standard deviations if a normal distribution is assumed Empirical Rule smaller than greater than the same as
Using the standard normal distribution find the probability that z is greater than 1.78?
0.0375
What is the difference between a general normal curve and a standard normal curve?
A standard normal distribution has a mean of zero and a standard deviation of 1. A normal distribution can have any real number as a mean and the standard deviation must be greater than zero.
What does standard deviation tell about distribution and varity?
It is a measure of the spread of the distribution. The greater the standard deviation the more variety there is in the observations.
How does the standard normal distribution differ from the t-distribution?
The normal distribution and the t-distribution are both symmetric bell-shaped continuous probability distribution functions. The t-distribution has heavier tails: the probability of observations further from the mean is greater than for the normal distribution. There are other differences in terms of when it is appropriate to use them. Finally, the standard normal distribution is a special case of a normal distribution such that the mean is 0 and the standard deviation is 1.
Why does a Uniform distribution have a greater standard deviation?
The answer depends on greater standard deviation that WHAT!
What proportion of sample proportions have a value greater than the population in any normal sample proportion distribution?
A half.
Can a standard deviation be greater than its mean?
Yes - but the distribution is not a normal distribution - this can happen with a distribution that has a very long tail.
Statistic question help?
When using Chebyshev's Theorem the minimum percentage of sample observations that will fall within two standard deviations of the mean will be __________ the percentage within two standard deviations if a normal distribution is assumed Empirical Rule smaller than greater than the same as
Fora standard normal distribution what is the probability that z is greater than 1.75?
.0401
What is the probability that z (the standard normal distribution variable) is greater than 1?
It is 0.1587
A normal distribution has a mean of µ = 50 with σ = 10. What proportion of the scores in this distribution are greater than X = 65?
Scores on the SAT form a normal distribution with a mean of µ = 500 with σ = 100. What is the probability that a randomly selected college applicant will have a score greater than 640?
Using the standard normal distribution find the probability that z is greater than 1.78?
0.0375
What is the difference between a general normal curve and a standard normal curve?
A standard normal distribution has a mean of zero and a standard deviation of 1. A normal distribution can have any real number as a mean and the standard deviation must be greater than zero.
What is chebychev's rule?
Chebyshev's rule, also known as Chebyshev's inequality, is a statistical theorem that describes the proportion of values that fall within a certain number of standard deviations from the mean in any distribution. It states that for any set of data, regardless of the shape of the distribution, at least (1 - 1/k^2) where k is greater than 1, of the data values will fall within k standard deviations of the mean.
