In order for the Central Limit Theorem to apply, what distribution must the underlying data have?...
In order for the Central Limit Theorem to apply, what distribution must the underlying data have? (assuming ? is large enough) A. Normal distribution B. Bernoulli distribution C. Binomial distribution D. Uniform distribution E. Any distribution
Homework Answers
according to clt
if x have a distribution with mean mu and standard deviation sigma then (xbar-mu)/ sigma follows standard normal distribution.(if n is large enough)
So from CLT defination we get that distribution can be any.
E. any distribution.
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In order for the Central Limit Theorem to apply, what
distribution must the underlying data have?...
1. Explain, in your own words, what the Central Limit Theorem says about sample means. In particular, discuss what the Central Limit Theorem says about the distribution of the sample mean, the...
1. Explain, in your own words, what the Central Limit Theorem says about sample means. In particular, discuss what the Central Limit Theorem says about the distribution of the sample mean, the mean of the sample mcan, and the standard deviation of the sample mean, as well as what effect (if any) the distribution of the underlying sample data has on the distribution of the sample mean. (You should consult my slides from class. Supplement with internet resources if you...
According to the central limit theorem, in order to assume a normal distribution for our sample...
According to the central limit theorem, in order to assume a normal distribution for our sample mean if σ is unknown, we must have a sample size greater than ____.
The Central Limit Theorem (CLT) implies that: A: the mean follows the same distribution as the...
The Central Limit Theorem (CLT) implies that: A: the mean follows the same distribution as the population B: repeated samples must be taken to obtain normality C: the population will be approximately normal if n ≥ 30 D: the distribution of the sample mean will be normal with large n
The Central Limit Theorem states that the sampling distribution will be normal as long as the...
The Central Limit Theorem states that the sampling distribution will be normal as long as the subgroup size is large enough. Explain what role the subgroup size has on the Variability of the means
please answer asap, urgent QUESTION 7 According to the Central Limit Theorem, the distribution of which...
please answer asap, urgent
QUESTION 7 According to the Central Limit Theorem, the distribution of which statistic can be approximately normal for any population distribution? What condition should the sample satisfy? 6. The Central Limit Theorem approximates the sample mean . It is applicable when the sample size n is sufficiently large. b. The Central Limit Theorem approximates the sample size n. It is applicable when the sample size is not large. The Central Limit Theorem approximates the population mean...
The Central Limit Theorem basically states that the sampling distribution will be normal as long as...
The Central Limit Theorem basically states that the sampling distribution will be normal as long as the subgroup size is large enough. Explain what role the subgroup size has on the: a. normality of the means. b. variability of the means
What is the answer? QUESTION 3 According to the central limit theorem, which of the following...
What is the answer?
QUESTION 3 According to the central limit theorem, which of the following distributions tend towards a normal distribution? (choose all that apply) a. Binomial distribution as number of events (number of total coin flips) increase U b.Sum of m independent samples from a normal distribution as m increases UC. Mean of n independent samples from a chi-squared distribution as n increases d. Sampling distribution of the mean from ANY population distribution as the sample size increases
The Central Limit Theorem is important in statistics because _. A for a large n, it...
The Central Limit Theorem is important in statistics because _. A for a large n, it says the population is approximately normal B for any population, it says the sampling distribution of the sample mean is approximately normal, regardless of the sample size C for a large n, it says the sampling distribution of the sample mean is approximately normal, regardless of the population D for any size sample, it says the sampling distribution of the sample mean is approximately...
The Central Limit Theorem tells us that the sampling distribution of the sample mean can be...
The Central Limit Theorem tells us that the sampling distribution of the sample mean can be approximated with a normal distribution for “large”n as n gets bigger, the sample data becomes more like the normal distribution if the data comes from an (approximately) normally distributed population, then the sample mean will also be (approximately) normally distributed the minimum variance unbiased estimator is the "best" estimator for a parameter
In the notes there is a Central Limit Theorem example in which a sampling distribution of means i...
R Programming codes for the above questions?
In the notes there is a Central Limit Theorem example in which a sampling distribution of means is created using a for loop, and then this distribution is plotted. This distribution should look approximately like a normal distribution. However, not all statistics have normal sampling distributions. For this problem, you'll create a sampling distribution of standard deviations rather than means. 3. Using a for loop, draw 10,000 samples of size n-30 from a...
1. Explain, in your own words, what the Central Limit Theorem says about sample means. In particular, discuss what the Central Limit Theorem says about the distribution of the sample mean, the mean of the sample mcan, and the standard deviation of the sample mean, as well as what effect (if any) the distribution of the underlying sample data has on the distribution of the sample mean. (You should consult my slides from class. Supplement with internet resources if you...
please answer asap, urgent
QUESTION 7 According to the Central Limit Theorem, the distribution of which statistic can be approximately normal for any population distribution? What condition should the sample satisfy? 6. The Central Limit Theorem approximates the sample mean . It is applicable when the sample size n is sufficiently large. b. The Central Limit Theorem approximates the sample size n. It is applicable when the sample size is not large. The Central Limit Theorem approximates the population mean...
What is the answer?
QUESTION 3 According to the central limit theorem, which of the following distributions tend towards a normal distribution? (choose all that apply) a. Binomial distribution as number of events (number of total coin flips) increase U b.Sum of m independent samples from a normal distribution as m increases UC. Mean of n independent samples from a chi-squared distribution as n increases d. Sampling distribution of the mean from ANY population distribution as the sample size increases
The Central Limit Theorem tells us that the sampling distribution of the sample mean can be approximated with a normal distribution for “large”n as n gets bigger, the sample data becomes more like the normal distribution if the data comes from an (approximately) normally distributed population, then the sample mean will also be (approximately) normally distributed the minimum variance unbiased estimator is the "best" estimator for a parameter
R Programming codes for the above questions?
In the notes there is a Central Limit Theorem example in which a sampling distribution of means is created using a for loop, and then this distribution is plotted. This distribution should look approximately like a normal distribution. However, not all statistics have normal sampling distributions. For this problem, you'll create a sampling distribution of standard deviations rather than means. 3. Using a for loop, draw 10,000 samples of size n-30 from a...
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