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(c) The standard error (SE) describes how the observed proportion of heads varies from trial to trial. What is the general expression for SE? SE =- (d) Consider the general expression for SE in (c). What does this tell us about the role of sample size, n? Will our different tosses yield more or less consistent results as we increase the number of pennies in our sample? Explain. (e) According to the Central Limit Theorem, what should we expect for the shape of the sample proportions from many different tosses?

(c) The standard error (SE) describes how the observed proportion of heads varies from trial to trial. What is the general expression for SE? SE =- (d) Consider the general expression for SE in (c). What does this tell us about the role of sample size, n? Will our different tosses yield more or less consistent results as we increase the number of pennies in our sample? Explain. (e) According to the Central Limit Theorem, what should we expect for the shape of the sample proportions from many different tosses?

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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C, D, E
Activity 13: Sampling Distribution of Proportions Objective: The purpose of this activity is to obtain a better understanding of the sampling distribution In this section we will construct empirical sampling distributions for the probability of heads in a coin 59 5.4 for the sample proportion. Topics covered: 1. Simple random sampling 2. Standard errors 3. Sampling distributions ott on toss. To begin, each student should take twenty pennies from the container. 1. First, let's think about what we expect to see in this experiment. e we were to toss a handful of n pennies, what proportion would you expect to land on heads? That is, what is the value of p= theoretical probability of heads? p= _50 (b) Are you guaranteed to observe the proportion of heads is exactly p when you toss the handful of coins? If we toss the same handful of n pennies several times, will we observe the same proportion of heads each toss? Explain. No, we are not quaranteed to observe the proportion o+ heads is exactly p when we toss the hadful of coins. bsta oy uoi (c) The standard error (SE) describes how the observed proportion of heads varies from trial to trial. What is the general expression for SE? SE =. (d) Consider the general expression for SE in (c). What does this tell us about the role of sample size, n? Will our different tosses yield more or less consistent results as we increase the number of pennies in our sample? Explain. (e) According to the Central Limit Theorem, what should we expect for the shape of the sample proportions from many different tosses?
Transcribed Image Text:Activity 13: Sampling Distribution of Proportions Objective: The purpose of this activity is to obtain a better understanding of the sampling distribution In this section we will construct empirical sampling distributions for the probability of heads in a coin 59 5.4 for the sample proportion. Topics covered: 1. Simple random sampling 2. Standard errors 3. Sampling distributions ott on toss. To begin, each student should take twenty pennies from the container. 1. First, let's think about what we expect to see in this experiment. e we were to toss a handful of n pennies, what proportion would you expect to land on heads? That is, what is the value of p= theoretical probability of heads? p= _50 (b) Are you guaranteed to observe the proportion of heads is exactly p when you toss the handful of coins? If we toss the same handful of n pennies several times, will we observe the same proportion of heads each toss? Explain. No, we are not quaranteed to observe the proportion o+ heads is exactly p when we toss the hadful of coins. bsta oy uoi (c) The standard error (SE) describes how the observed proportion of heads varies from trial to trial. What is the general expression for SE? SE =. (d) Consider the general expression for SE in (c). What does this tell us about the role of sample size, n? Will our different tosses yield more or less consistent results as we increase the number of pennies in our sample? Explain. (e) According to the Central Limit Theorem, what should we expect for the shape of the sample proportions from many different tosses?
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