When they send out theirfundraising letter​, a philanthropic organization typically gets a return from about 5​% of the people on their mailing list. To see what the response rate might be in the​ future, they did a simulation using samples of size​ 20, 50,​ 100, and 200. For each sample​ size, they simulated 1000 mailings with success rate p=0.05 and constructed the histogram of the 1000 sample​ proportions, as shown in the accopmanying table. Explain how these histograms demonstrate what the Central Limit Theorem says about the sampling distribution model for sample proportions. Be sure to talk about​ center, shape, and spread. Fill in the blanks As the sample size​ increases, the center of the histogram ▼ (moves to the left), (moves to the right), (stays approximattly the same)  the shape of the distribution ▼ (becomes more normal), (becomes less normal), (stays approximatley the same) and the variability in the sample proportions ▼ (decreases), (increases), (stays aproxamtley the same) These simulations ▼(appear), (do not appear) to demonstrate what the Central Limit Theorem says about the sampling distribution model for sample proportions.

MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
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When they send out theirfundraising letter​, a philanthropic organization typically gets a return from about 5​% of the people on their mailing list. To see what the response rate might be in the​ future, they did a simulation using samples of size​ 20, 50,​ 100, and 200. For each sample​ size, they simulated 1000 mailings with success rate p=0.05 and constructed the histogram of the 1000 sample​ proportions, as shown in the accopmanying table. Explain how these histograms demonstrate what the Central Limit Theorem says about the sampling distribution model for sample proportions. Be sure to talk about​ center, shape, and spread.

Fill in the blanks

As the sample size​ increases, the center of the histogram ▼ (moves to the left), (moves to the right), (stays approximattly the same) 

the shape of the distribution ▼ (becomes more normal), (becomes less normal), (stays approximatley the same)

and the variability in the sample proportions ▼ (decreases), (increases), (stays aproxamtley the same)

These simulations ▼(appear), (do not appear)

to demonstrate what the Central Limit Theorem says about the sampling distribution model for sample proportions.

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