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Leona [35]
4 years ago
13

Assume that you plan to use a significance level of alpha α equals =0.05 to test the claim that p 1 p1 equals = p 2 p2. Use the

given sample sizes and numbers of successes to find the pooled estimate p. Round your answer to the nearest thousandth. n 1 n1 equals =​677, n 2 n2 equals =3377 x 1 x1 equals =​172, x 2 x2 equals =654
Mathematics
1 answer:
stiks02 [169]4 years ago
6 0

Answer:

z=3.536

\hat p=0.204

p_v =2*P(Z>3.54)\approx 0.0004  

Step-by-step explanation:

1) Data given and notation  

X_{1}=172 represent the number of people with the characteristic 1

X_{2}=356 represent the number of people with the characteristic 2

n_{1}=677 sample 1 selected  

n_{2}=3377 sample 2 selected  

p_{1}=\frac{172}{677}=0.254 represent the proportion estimated for the sample 1

p_{2}=\frac{654}{3377}=0.194 represent the proportion estimated for the sample 2

\hat p represent the pooled estimate of p

z would represent the statistic (variable of interest)  

p_v represent the value for the test (variable of interest)  

\alpha=0.05 significance level given

2) Concepts and formulas to use  

We need to conduct a hypothesis in order to check if is there is a difference between the two proportions, the system of hypothesis would be:  

Null hypothesis:p_{1} = p_{2}  

Alternative hypothesis:p_{1} \neq p_{2}  

We need to apply a z test to compare proportions, and the statistic is given by:  

z=\frac{p_{1}-p_{2}}{\sqrt{\hat p (1-\hat p)(\frac{1}{n_{1}}+\frac{1}{n_{2}})}}   (1)  

Where \hat p=\frac{X_{1}+X_{2}}{n_{1}+n_{2}}=\frac{172+654}{677+3377}=0.204  

z-test: Is used to compare group means. Is one of the most common tests and is used to determine whether the means of two groups are equal to each other.  

3) Calculate the statistic  

Replacing in formula (1) the values obtained we got this:  

z=\frac{0.254-0.194}{\sqrt{0.204(1-0.204)(\frac{1}{677}+\frac{1}{3377})}}=3.536    

4) Statistical decision

Since is a two side test the p value would be:  

p_v =2*P(Z>3.54)\approx 0.0004  

Comparing the p value with the significance level given \alpha=0.05 we see that p_v so we can conclude that we have enough evidence to to reject the null hypothesis, and we can say that the proportion analyzed is significantly different between the two groups.  

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z=\frac{0.775 -0.73}{\sqrt{\frac{0.73(1-0.73)}{200}}}=1.433  

Now we can find the p value. Since we have a bilateral test the p value would be:  

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Step-by-step explanation:

Information provided

n=200 represent the sample size slected

X=155 represent the cell phone owners used text messaging

\hat p=\frac{155}{200}=0.775 estimated proportion of cell phone owners used text messaging

p_o=0.73 is the value to verify

\alpha=0.1 represent the significance level

We need to conduct a z test for a proportion

z would represent the statistic

p_v represent the p value

System of hypothesis

We want to verify if the true proportion of cell phone owners used text messaging is different from 0.73 so then the system of hypothesis are:

Null hypothesis:p=0.73  

Alternative hypothesis:p \neq 0.73  

The statistic to check this hypothesis is given by:

z=\frac{\hat p -p_o}{\sqrt{\frac{p_o (1-p_o)}{n}}} (1)  

Replacing the data given we got:

z=\frac{0.775 -0.73}{\sqrt{\frac{0.73(1-0.73)}{200}}}=1.433  

Now we can find the p value. Since we have a bilateral test the p value would be:  

p_v =2*P(z>1.433)=0.152  

Since the p value is higher than the significance level of 0.1 we have enough evidence to FAIL to reject the null hypothesis and the best conclusion for this case would be:

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