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What else can I help you with?
When a population distribution is right skewed is the sampling distribution normal?
No, as you said it is right skewed.
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.
The Central Limit Theorem is important in statistics because?
According to the central limit theorem, as the sample size gets larger, the sampling distribution becomes closer to the Gaussian (Normal) regardless of the distribution of the original population. Equivalently, the sampling distribution of the means of a number of samples also becomes closer to the Gaussian distribution. This is the justification for using the Gaussian distribution for statistical procedures such as estimation and hypothesis testing.
What is the difference of a normal distribution and a stardard normal distribution?
The standard normal distribution is a special case of the normal distribution. The standard normal has mean 0 and variance 1.
What proportion of the scores in a normal distribution is approximately between z -1.16 and z 1.16?
Between z = -1.16 and z = 1.16 is approx 0.7540 (or 75.40 %). Which means ¾ (0.75 or 75%) of the normal distribution lies between approximately -1.16 and 1.16 standard deviations from the mean.
The shape of the sampling distribution is always approximately normal?
welll its quiet simple to be honest 1st ASK YOUR TEACHER! that's what they are there for
When the population standard deviation is known the sampling distribution is a?
normal distribution
When the population standard deviation is known the sampling distribution is known as what?
normal distribution
In an SRS of size n what is true about the sampling distributions of p when the sample size n increases?
As n increases the sampling distribution of pˆ (p hat) becomes approximately normal.
When a population distribution is right skewed is the sampling distribution normal?
No, as you said it is right skewed.
When population distribution is right skewed is the sampling also with right skewed distribution?
If the population distribution is roughly normal, the sampling distribution should also show a roughly normal distribution regardless of whether it is a large or small sample size. If a population distribution shows skew (in this case skewed right), the Central Limit Theorem states that if the sample size is large enough, the sampling distribution should show little skew and should be roughly normal. However, if the sampling distribution is too small, the sampling distribution will likely also show skew and will not be normal. Although it is difficult to say for sure "how big must a sample size be to eliminate any population skew", the 15/40 rule gives a good idea of whether a sample size is big enough. If the population is skewed and you have fewer that 15 samples, you will likely also have a skewed sampling distribution. If the population is skewed and you have more that 40 samples, your sampling distribution will likely be roughly normal.
We have a population with mean of 100 and standard deviation of 28 take repeated samples of size 49 and calculate the mean of each sample to form a sampling distribution Is it a Normal Distribution?
a) T or F The sampling distribution will be normal. Explain your answer. b) Find the mean and standard deviation of the sampling distribution. c) We pick one of our samples from the sampling distribution what is the probability that this sample has a mean that is greater than 109 ? Is this a usual or unusual event? these are the rest of the question.
How does the number of repetitions effect the shape of the normal distribution?
When we discuss a sample drawn from a population, the larger the sample, or the large the number of repetitions of the event, the more certain we are of the mean value. So, when the normal distribution is considered the sampling distribution of the mean, then more repetitions lead to smaller values of the variance of the distribution.
When can you say the distribution is considered normal?
When its probability distribution the standard normal 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.
How is the t distribution similar to the standard z distribution?
Z is the standard normal distribution. T is the standard normal distribution revised to reflect the results of sampling. This is the first step in targeted sales developed through distribution trends.
What is sampling distribution of the mean?
Thanks to the Central Limit Theorem, the sampling distribution of the mean is Gaussian (normal) whose mean is the population mean and whose standard deviation is the sample standard error.
