The Central Limit Theorem is one of the most important and useful concepts of statistics. It forms a foundation for estimating population parameters and hypothesis testing and states thatas the sample size increases, the sampling distribution of sample means approaches an FF distribution, regardless of the distribution of the original population.as the sample size increases, the sampling distribution of sample means approaches a Binomial distribution, regardless of the distribution of the original population.as the sample size increases, the sampling distribution of sample means approaches a Student (tt) distribution, regardless of the distribution of the original population.as the sample size increases, the sampling distribution of sample means approaches a Chi-square (χ2χ2) distribution, regardless of the distribution of the original population.as the sample size increases, the sampling distribution of sample means approaches a Normal (zz) distribution, regardless of the distribution of the original population.

Answered: The Central Limit Theorem is one of the…

The Central Limit Theorem is one of the most important and useful concepts of statistics. It forms a foundation for estimating population parameters and hypothesis testing and states that

Holt Mcdougal Larson Pre-algebra: Student Edition 2012

1st Edition

ISBN:9780547587776

Author:HOLT MCDOUGAL

Publisher:HOLT MCDOUGAL

Chapter11: Data Analysis And Probability

Section11.4: Collecting Data

Problem 2E

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Concept explainers

Estimation

A new framework of data analysis where the new statistical methods are used for interpreting the results and analyzing the data is known as estimation in statistics.

Question

The Central Limit Theorem is one of the most important and useful concepts of statistics. It forms a foundation for estimating population parameters and hypothesis testing and states that

as the sample size increases, the sampling distribution of sample means approaches an FF distribution, regardless of the distribution of the original population.
as the sample size increases, the sampling distribution of sample means approaches a Binomial distribution, regardless of the distribution of the original population.
as the sample size increases, the sampling distribution of sample means approaches a Student (tt) distribution, regardless of the distribution of the original population.
as the sample size increases, the sampling distribution of sample means approaches a Chi-square (χ2χ2) distribution, regardless of the distribution of the original population.
as the sample size increases, the sampling distribution of sample means approaches a Normal (zz) distribution, regardless of the distribution of the original population.

Definition Definition Number of subjects or observations included in a study. A large sample size typically provides more reliable results and better representation of the population. As sample size and width of confidence interval are inversely related, if the sample size is increased, the width of the confidence interval decreases.

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