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kogti [31]
3 years ago
11

Researchers wondered if there was a difference between males and females in regard to some common annoyances. They asked a rando

m sample of males and​ females, the following​ question: "Are you annoyed by people who repeatedly check their mobile phones while having an​ in-person conversation?" Among the 530 males​ surveyed, 175 responded​ "Yes"; among the 575 females​ surveyed, 218 responded​ "Yes." Does the evidence suggest a higher proportion of females are annoyed by this​ behavior? Complete parts​ below.
(a) Determine the sample proportion for each sample.

The proportions of the females and males who took the survey who are annoyed by the behavior in question are ____and _____ respectively. (Round to four decimal places as needed.)

(b) Explan why this study can be analyzed using the methods for conducting a hypothesis test regarding two independent proportions. Select all that apply.

A. The sample size is more than 5% of the population size for each sample
B. The sample size is less than 5% of the population size for each sample
C. The samples are dependent.
D. The data come from a population that is nornally distributed.
E. np (1-) 10 and n2p2 (1-2) 210
F. The samples are independent.

(c) What are the null and alternative hypotheses? Let p1 represent the population proportion of females who are annoyed by the behavior in question and p2 represent the population proportion of males who are annoyed by the behavior in question.
Mathematics
1 answer:
inysia [295]3 years ago
4 0

Answer:

a)

The proportions of the females and males who took the survey who are annoyed by the behavior in question are<u> 0.3301 </u>and_<u>0.3791 </u>respectively

b)

<em>D) The data come from a Population that is Normally distributed</em>

<em>F) the samples are independent</em>

<em>c) </em>

<em>Null hypothesis: H₀: </em>p_{m} ^{-} - p^{-} _{fm} \leq  0

<em>Alternative Hypothesis H₁</em>: p_{m} ^{-} - p^{-} _{fm} \geq  0

Step-by-step explanation:

Given data Among the 530 males​ surveyed, 175 responded​ "Yes

The sample proportion of males

p_{m} ^{-}  = \frac{x}{n} = \frac{175}{530} = 0.3301

The sample proportion of 575 Females surveyed, 218 responded​ "Yes

p_{fem} ^{-}  = \frac{x}{n} = \frac{218}{575} = 0.3791

a)

The proportions of the females and males who took the survey who are annoyed by the behavior in question are<u> 0.3301 </u>and_<u>0.3791 </u>respectively

b)

<em>The data come from a Population that is normally distributed.</em>

<em>The two samples are independent</em>

c)

<em>Null hypothesis: H₀: </em>p_{m} ^{-} - p^{-} _{fm} \geq 0

<em>Alternative Hypothesis H₁</em>: p_{m} ^{-} - p^{-} _{fm} \leq  0

We will use two samples Z - hypothesis test

Z = \frac{p^{-} _{m} - p^{-} _{fem} }{se(p^{-} _{m}-p^{-} _{fem} ) }

Se(p_{m} -p_{fem}) = \sqrt{\frac{p_{m}(1-p_{m})  }{n_{m} }+\frac{p_{fem} (1-p_{fem} )}{n_{fem} }  }

Se(p_{m} -p_{fem}) = \sqrt{\frac{0.3301(1-0.3301)  }{530 }+\frac{0.3791(1-0.3791)}{575}  }

 Se(p_{m} ^{-} - p^{-} _{fem} ) = \sqrt{0.000826} = 0.0287

The test statistic

Z = \frac{0.3301-0.3791}{0.02875}

Z = -1.7043

|Z| = |-1.7043|

The tabulated value Z₀.₉₅ = 1.96

The calculated Z= 1.7043 < 1.96 at 0.05 level of significance

The null hypothesis is accepted

<u><em>Conclusion:-</em></u>

The evidence suggest not a higher proportion of females are annoyed by this​ behavior

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Let X denote the length of human pregnancies from conception to birth, where X has a normal distribution with mean of 264 days a
Kaylis [27]

Answer:

Step-by-step explanation:

Hello!

X: length of human pregnancies from conception to birth.

X~N(μ;σ²)

μ= 264 day

σ= 16 day

If the variable of interest has a normal distribution, it's the sample mean, that it is also a variable on its own, has a normal distribution with parameters:

X[bar] ~N(μ;σ²/n)

When calculating a probability of a value of "X" happening it corresponds to use the standard normal: Z= (X[bar]-μ)/σ

When calculating the probability of the sample mean taking a given value, the variance is divided by the sample size. The standard normal distribution to use is Z= (X[bar]-μ)/(σ/√n)

a. You need to calculate the probability that the sample mean will be less than 260 for a random sample of 15 women.

P(X[bar]<260)= P(Z<(260-264)/(16/√15))= P(Z<-0.97)= 0.16602

b. P(X[bar]>b)= 0.05

You need to find the value of X[bar] that has above it 5% of the distribution and 95% below.

P(X[bar]≤b)= 0.95

P(Z≤(b-μ)/(σ/√n))= 0.95

The value of Z that accumulates 0.95 of probability is Z= 1.648

Now we reverse the standardization to reach the value of pregnancy length:

1.648= (b-264)/(16/√15)

1.648*(16/√15)= b-264

b= [1.648*(16/√15)]+264

b= 270.81 days

c. Now the sample taken is of 7 women and you need to calculate the probability of the sample mean of the length of pregnancy lies between 1800 and 1900 days.

Symbolically:

P(1800≤X[bar]≤1900) = P(X[bar]≤1900) - P(X[bar]≤1800)

P(Z≤(1900-264)/(16/√7)) - P(Z≤(1800-264)/(16/√7))

P(Z≤270.53) - P(Z≤253.99)= 1 - 1 = 0

d. P(X[bar]>270)= 0.1151

P(Z>(270-264)/(16/√n))= 0.1151

P(Z≤(270-264)/(16/√n))= 1 - 0.1151

P(Z≤6/(16/√n))= 0.8849

With the information of the cumulated probability you can reach the value of Z and clear the sample size needed:

P(Z≤1.200)= 0.8849

Z= \frac{X[bar]-Mu}{Sigma/\sqrt{n} }

Z*(Sigma/\sqrt{n} )= (X[bar]-Mu)

(Sigma/\sqrt{n} )= \frac{(X[bar]-Mu)}{Z}

Sigma= \frac{(X[bar]-Mu)}{Z}*\sqrt{n}

Sigma*(\frac{Z}{(X[bar]-Mu)})= \sqrt{n}

n = (Sigma*(\frac{Z}{(X[bar]-Mu)}))^2

n = (16*(\frac{1.2}{(270-264)}))^2

n= 10.24 ≅ 11 pregnant women.

I hope it helps!

6 0
3 years ago
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