Question

Asked what the central limit theorem says, a student replies, "As you take larger and larger...

Asked what the central limit theorem says, a student replies, "As you take larger and larger samples from a population, the histogram of the sample values looks more and more Normal."

  • No. The central limit theorem says nothing about the histogram of the sample values. It deals only with the distribution of the sample's mean.
  • Yes. This is exactly what the theorem says.
  • No. As you take larger and larger samples the histogram of the sample values looks less Normal.
0 0
Add a comment Improve this question Transcribed image text
Answer #1

Anscier: Option I is correct (NO. The Central limit theorem says nothing about the histogram of the sample values. It deals

Add a comment
Know the answer?
Add Answer to:
Asked what the central limit theorem says, a student replies, "As you take larger and larger...
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for? Ask your own homework help question. Our experts will answer your question WITHIN MINUTES for Free.
Similar Homework Help Questions
  • The central limit theorem says that when a simple random sample of size n is drawn...

    The central limit theorem says that when a simple random sample of size n is drawn from any population with mean μ and standard deviation σ, then when n is sufficiently large the distribution of the sample mean is approximately Normal. the standard deviation of the sample mean is σ2nσ2n. the distribution of the sample mean is exactly Normal. the distribution of the population is approximately Normal.

  • 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...

  • Law of Large Numbers, Central Limit Theorem, and Confidence Intervals 1. (15 points) In an exercise,...

    Law of Large Numbers, Central Limit Theorem, and Confidence Intervals 1. (15 points) In an exercise, your Professor generated random numbers in Excel. The mean is supposed to be 0.5 because the numbers are supposed to be spread at randonm between 0 and 1. I asked the software to generate samples of 100 random numbers repeatedly. Here are the sample means x for 50 samples of size 100: 0.532 0.450 0.481 0.508 0.510 0.530 0.4990.4610.5430.490 0.497 0.5520.473 0.425 0.4490.507 0.472...

  • 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 says A) When n<30 , the sampling distribution of x¯¯¯ will be...

    The Central Limit Theorem says A) When n<30 , the sampling distribution of x¯¯¯ will be approximately a normal distribution. B) When n<30 , the original population will be approximately a normal distribution. C) When n>30 , the original population will be approximately a normal distribution. D) When n>30 , the sampling distribution of x¯¯¯ will be approximately a normal distribution.

  • L.9) Central Limit Theorem Central Limit Theorem Version 1 says Go with independent random variables (Xi,...

    L.9) Central Limit Theorem Central Limit Theorem Version 1 says Go with independent random variables (Xi, X2, X3, ..., Xs, ...] all with the same cumulative distribution function so that μ-Expect[X] = Expect[X] and σ. varpKJ-Var[X] for all i and j Put As n gets large, the cumulative distribution function of S[n] is well approximated by the Normal[0, 1] cumulative distribution function. Another version of the Central Limit Theorem used often in statistics says Go with independent random variables (Xi....

  • The Central Limit Theorem says A. When ?<30n<30, the original population will be approximately a normal...

    The Central Limit Theorem says A. When ?<30n<30, the original population will be approximately a normal distribution. B. When ?<30n<30, the sampling distribution of ?⎯⎯⎯x¯ will be approximately a normal distribution. C. When ?>30n>30, the original population will be approximately a normal distribution. D. When ?>30n>30, the sampling distribution of ?⎯⎯⎯x¯ will be approximately a normal distribution. E. None of the above

  • 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

  • 1. In this problem, you are going to numerically verify that the Central Limit Theorem is valid e...

    1. In this problem, you are going to numerically verify that the Central Limit Theorem is valid even when sampling from non-normal distributions. Suppose that a component has a probability of failure described by a Weibull distri- bution. Let X be the random variable that denotes time until failure; its probability density is: for a 2 0, and zero elsewhere. In this problem, assume k 1.5, 100 a) Simulate drawing a set of N-20 sample values, repeated over M 200...

  • Central Limit Theorem (CLT) 1. The CLT states: draw all possible samples of size _____________ from...

    Central Limit Theorem (CLT) 1. The CLT states: draw all possible samples of size _____________ from a population. The result will be the sampling distribution of the means will approach the ___________________- as the sample size, n, increases. 2. The CLT tells us we can make probability statements about the mean using the normal distribution even though we know nothing about the ______________-

ADVERTISEMENT
Free Homework Help App
Download From Google Play
Scan Your Homework
to Get Instant Free Answers
Need Online Homework Help?
Ask a Question
Get Answers For Free
Most questions answered within 3 hours.
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT