We hope you noticed several important things while working through this activity. First, no matter whatthe sample size was, our sampling distributions were always centered at the claimed populationproportion of 0.50. As the sample size got bigger, there was less variability among our sample statistics.Further, all of the sampling distributions in this case had a shape that we would consider to beapproximately Normal. When the sample size is large enough (and we'll say more about what "largeenough" is in a future lecture), the sampling distribution will have a Normal shape.Let's look again at our distributions of sample proportions for the three different sample sizes, from left toright below (n = 25, n = 50, and n = 100). Please answer the following questions based on what you seein the graphs below.n = 25Mean = 0.50n= 50Mean = 0.50n = 100Mean = 0.50Standard deviation = 0.1Standard deviation:0.071Standard deviation = 0.050.12 0.24 0.36 0.48 0.60 0.72 0.84 0.960.30 0.36 0.42 0.48 0.54 0.60 0.66 0.720.160.24 0.32 0.400.48 0.560.64 0.720.800.88Proportion of orangeProportion of orangeProportion of orange2000 4000 6000 800100081000710009000912000 18000 Again, without performing any calculations,' what would be less likely to occur: Obtaining asample proportion of 0.60 or larger when randomly selecting a sample of size n = 25, or obtaininga sample proportion of 0.60 or larger when randomly selecting a sample of size n =50?Pleaseexplain

Again, without performing any calculations, what would be less likely to occur: Obtaining a sample proportion of 0.60 or larger when randomly selecting a sample of size n = 25, or obtaining a sample proportion of 0.60 or larger when randomly selecting a sample of size n = 50? Please

Again, without performing any calculations, what would be less likely to occur: Obtaining a sample proportion of 0.60 or larger when randomly selecting a sample of size n = 25, or obtaining a sample proportion of 0.60 or larger when randomly selecting a sample of size n = 50? Please

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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Concept explainers

Confidence Intervals

When we calculate intervals in probability and statistics, it can be simply termed as finding out a confidence interval. The confidence interval is usually calculated from the data set which is observed. The value of the parameter that needs to be calculated is present here only.

Question

We hope you noticed several important things while working through this activity. First, no matter what
the sample size was, our sampling distributions were always centered at the claimed population
proportion of 0.50. As the sample size got bigger, there was less variability among our sample statistics.
Further, all of the sampling distributions in this case had a shape that we would consider to be
approximately Normal. When the sample size is large enough (and we'll say more about what "large
enough" is in a future lecture), the sampling distribution will have a Normal shape.
Let's look again at our distributions of sample proportions for the three different sample sizes, from left to
right below (n = 25, n = 50, and n = 100). Please answer the following questions based on what you see
in the graphs below.
n = 25
Mean = 0.50
n= 50
Mean = 0.50
n = 100
Mean = 0.50
Standard deviation = 0.1
Standard deviation:
0.071
Standard deviation = 0.05
0.12 0.24 0.36 0.48 0.60 0.72 0.84 0.96
0.30 0.36 0.42 0.48 0.54 0.60 0.66 0.72
0.160.24 0.32 0.400.48 0.560.64 0.720.800.88
Proportion of orange
Proportion of orange
Proportion of orange
2000 4000 6000 8001
00081
00071
0009
0009
12000 18000

Again, without performing any calculations,' what would be less likely to occur: Obtaining a
sample proportion of 0.60 or larger when randomly selecting a sample of size n = 25, or obtaining
a sample proportion of 0.60 or larger when randomly selecting a sample of size n =
50?
Please
explain

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