Question

Demonstrate Central Limit Theorem(CLT) of the sample mean by sampling a 100 uniform distribution data with 50 variables. Verify the result by computing the sample mean, sample variance and sketch the...

Demonstrate Central Limit Theorem(CLT) of the sample mean by sampling a 100 uniform distribution data with 50 variables. Verify the result by computing the sample mean, sample variance and sketch the histogram on Excel/Megastat.

Hint: Generate 100 datasets of 50 variables and calculate 50 sample means to determine the distribution of X̅ and SX̅. It should converge to a model that we’ve learned in class.

0 0
Add a comment Improve this question Transcribed image text
Answer #1

solution:

given that

Histogram of C51 Normal Mean 0.4901 25 StDev 0.04516 100 20 15 10 0.60 0.450.50 C51 0.55 0.40 0.35

Above histogram of sample mean is based mean of 100 data set each contains 50 values .

By central limit theorem

Sample mean is normally distributed with mean 0.5 and standard deviation 0.2987.

Here on basis of simulation we get sample mean 0.4901 and standard deviation 0.045

thank you

Add a comment
Know the answer?
Add Answer to:
Demonstrate Central Limit Theorem(CLT) of the sample mean by sampling a 100 uniform distribution data with 50 variables. Verify the result by computing the sample mean, sample variance and sketch the...
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
  • Part III. Exponential Rvs & the Central Limit Theorem c) Imagine sampling 50 values from Exp(5),...

    Part III. Exponential Rvs & the Central Limit Theorem c) Imagine sampling 50 values from Exp(5), an exponential distribution with rate 5. According to the CLT (central limit theorem), what should be the expected value (mean) of this sample? You should not need to do any coding to answer this. Answer: Check Part III. Exponential Rvs & the Central Limit Theorem d) Imagine sampling 50 values from Exp(5), an exponential distribution with rate 5. What should be the variance of...

  • 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

  • True or False: the central limit theorem states that the sampling distribution of the sample mean...

    True or False: the central limit theorem states that the sampling distribution of the sample mean is approximately normal whenever the population from which we are sampling is normally distributed Assume that 14% of the items produced in an assembly line operation are defective, but that the firm’s production manager is not aware of this situation. Assume firtber that the wuality assurance department to determine the quality of the assembly operation tests 50 parts. What is the probability that the...

  • 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...

  • Central Limit Theorem Data is drawn from a normal distribution with a mean of 50 and...

    Central Limit Theorem Data is drawn from a normal distribution with a mean of 50 and a standard deviation of is. A sample of 100 is taken and the sample meen computed of what is the mean of the sample mean of 100 of the b) what is sample the standard deviation mean of 100 what is the probability that the Sample mean is less than 48 CUMULATIVE I X NORM, DIST Case I

  • 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...

  • R commands 2) Illustrating the central limit theorem. X, X, X, a sequence of independent random variables with the same distribution as X. Define the sample mean X by X = A + A 2 be a random va...

    R commands 2) Illustrating the central limit theorem. X, X, X, a sequence of independent random variables with the same distribution as X. Define the sample mean X by X = A + A 2 be a random variable having the exponential distribution with A -2. Denote by -..- The central limit theorem applied to this particular case implices that the probability distribution of converges to the standard normal distribution for certain values of u and o (a) For what...

  • Question 1, 2 or similar to demonstrate the central limit theorem. sample mean when the population...

    Question 1, 2 or similar to demonstrate the central limit theorem. sample mean when the population distribution is expo- pling distribution changes as n, the sample size increases. In this homework assignment you will use R (or python or similar) to demo . First we will explore the sampling distribution of a sample mean when the nential (X, 1 exp(1)). We will show how the sampling distribution changes a - X, id exp(1) and n=1 * In a document (word...

  • Using R, Exercise 4 (CLT Simulation) For this exercise we will simulate from the exponential distribution....

    Using R, Exercise 4 (CLT Simulation) For this exercise we will simulate from the exponential distribution. If a random variable X has an exponential distribution with rate parameter A, the pdf of X can be written for z 2 0 Also recall, (a) This exercise relies heavily on generating random observations. To make this reproducible we will set a seed for the randomization. Alter the following code to make birthday store your birthday in the format yyyymmdd. For example, William...

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