Homework Answers
According to central limit theoram ,for random samples the shape of the sampling distribution of x-bar is approximately normal if the sample size is large.
Option c is correct.
The probability that best matches the following statement is
0.05
Option( b) is correct
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31. According to the Central Limit Theorem, for random samples, what is the approximate shape of the sampling distr...
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...
According to the central limit theorem, for samples of size 64 drawn from a population with...
According to the central limit theorem, for samples of size 64 drawn from a population with μ = 800 and σ = 56, the standard deviation of the sampling distribution of sample means would equal 7 8 100 800 80
Which of the following statements concerning sampling is false? (1) The Central Limit Theorem is very...
Which of the following statements concerning sampling is false? (1) The Central Limit Theorem is very important for statistical inference. (2) The standard error of an estimator is the standard deviation of a statistic. (3) Regardless of the sample size n, if the population distribution is normal then the sampling distribution of ī will be exactly normal. (4) If the sampled population is uniform then the sampling distribution of ī is also approximately uniform.
Which of the following statements concerning sampling is false? (1) The Central Limit Theorem is very...
Which of the following statements concerning sampling is false? (1) The Central Limit Theorem is very important for statistical inference. (2) The standard error of an estimator is the standard deviation of a statistic. (3) Regardless of the sample size n, if the population distribution is normal then the sampling distribution of ī will be exactly normal. (4) If the sampled population is uniform then the sampling distribution of ī is also approximately uniform.
Question (1) According to the Central Limit theorem, what is the standard deviation of the sampling...
Question (1) According to the Central Limit theorem, what is the standard deviation of the sampling distribution of the sample mean? (02 marks) ► The standard deviation of the population The standard deviation of the sample ► The standard deviation of the population divided by the square root of the sample size. The standard deviation of the sample divided by the square root of the sample size.
For each of the following give the name of the sampling method The Central Limit Theorem...
For
each of the following give the name of the sampling method
The Central Limit Theorem (CLT) is one of the most important theorems in Statistics. Determine if each of the following statements about the Central Limit Theorem is Valid or Invalid. Write a sentence to explain your answer. a) The average (center) of all the random sample means will be a good (3pts) b) The distribution of random sample means is normally distributed for (3pts) c) The CLT only...
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 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.
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 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
Which of the following statements concerning sampling is false? (1) The Central Limit Theorem is very important for statistical inference. (2) The standard error of an estimator is the standard deviation of a statistic. (3) Regardless of the sample size n, if the population distribution is normal then the sampling distribution of ī will be exactly normal. (4) If the sampled population is uniform then the sampling distribution of ī is also approximately uniform.
Which of the following statements concerning sampling is false? (1) The Central Limit Theorem is very important for statistical inference. (2) The standard error of an estimator is the standard deviation of a statistic. (3) Regardless of the sample size n, if the population distribution is normal then the sampling distribution of ī will be exactly normal. (4) If the sampled population is uniform then the sampling distribution of ī is also approximately uniform.
Question (1) According to the Central Limit theorem, what is the standard deviation of the sampling distribution of the sample mean? (02 marks) ► The standard deviation of the population The standard deviation of the sample ► The standard deviation of the population divided by the square root of the sample size. The standard deviation of the sample divided by the square root of the sample size.
For
each of the following give the name of the sampling method
The Central Limit Theorem (CLT) is one of the most important theorems in Statistics. Determine if each of the following statements about the Central Limit Theorem is Valid or Invalid. Write a sentence to explain your answer. a) The average (center) of all the random sample means will be a good (3pts) b) The distribution of random sample means is normally distributed for (3pts) c) The CLT only...
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
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 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
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