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The Central Limit Theorem (CLT) says O Samples of size 15 are large enough for the CLT to apply for any populations. O As the sample size increases, the sampling distribution of the mean will get closer and closer to a normal distribution. O Regardless of the sample size, the sampling distribution of the mean is always close to a normal distribution. O Only for symmetric population distributions, the sampling distribution of the mean will get close to a normal distribution as the sample size increas

The Central Limit Theorem (CLT) says O Samples of size 15 are large enough for the CLT to apply for any populations. O As the sample size increases, the sampling distribution of the mean will get closer and closer to a normal distribution. O Regardless of the sample size, the sampling distribution of the mean is always close to a normal distribution. O Only for symmetric population distributions, the sampling distribution of the mean will get close to a normal distribution as the sample size increas

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section10.4: Distributions Of Data
Problem 22PFA
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The Central Limit Theorem (CLT) says O Samples of size 15 are large enough for the CLT to apply for any populations. O As the sample size increases, the sampling distribution of the mean will get closer and closer to a normal distribution. O Regardless of the sample size, the sampling distribution of the mean is always close to a normal distribution. O Only for symmetric population distributions, the sampling distribution of the mean will get close to a normal distribution as the sample size increases.
Transcribed Image Text:The Central Limit Theorem (CLT) says O Samples of size 15 are large enough for the CLT to apply for any populations. O As the sample size increases, the sampling distribution of the mean will get closer and closer to a normal distribution. O Regardless of the sample size, the sampling distribution of the mean is always close to a normal distribution. O Only for symmetric population distributions, the sampling distribution of the mean will get close to a normal distribution as the sample size increases.
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