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KATRIN_1 [288]
3 years ago
7

Data on the blood cholesterol levels of 6 rats give mean = 85, s= 12. A 95% confidence interval for the mean blood cholesterol o

f rats under this condition is
a)72.4 to 97.6
b)73.0 to 97.0
c)75.4 to 94.6
d)72.4 to 94.6
Mathematics
2 answers:
shusha [124]3 years ago
5 0
The equation of this would be

true \ mean=mean \ +/- \  z\frac{s}{ \sqrt{n} }

The z-value for a 95% confidence level is equal to 1.96.

Then the lower limit would be:

85-1.96 \frac{12}{ \sqrt{6} }=75.398

And the higher limit would be:

85+1.96 \frac{12}{ \sqrt{6} }=94.60

Therefore, the answer is

<span>c)75.4 to 94.6

I hope I was able to explain it clearly. Have a good day :)
</span>
lana [24]3 years ago
4 0

The <em><u>correct answer</u></em> is:

c)75.4 to 94.6

Explanation:

The formula for a confidence interval is:

\mu \pm z*(\frac{\sigma}{\sqrt{n}}),

where μ is the mean, z is the z-score associated with the level of confidence we want, σ is the standard deviation, and n is the sample size.

Our mean is 85, our standard deviation is 12, our sample size is 6, and since we want 95% confidence, our z-score is 1.96:

85\pm 1.96(\frac{12}{\sqrt{6}})=85\pm 9.6=85-9.6, 85+9.6=75.4, 94.6

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Suppose that in a casino game the payout is a random variable X . If X is positive, you gain money, if negative, you lose.
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Answer:

the conditional probability that X = 1 , X = 2 and X = 3 is  0.7333 (73.33%) , 0.25 (25%) and 0.0167 (1.67%) respectively

Step-by-step explanation:

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P(X=1/W)= P(X=1 ∩ W)/P(W)

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then

P(X=1/W)= P(X=1 ∩ W)/P(W) = P(X=1) / P(W) = p(1) /[p(1)+p(2)+p(3)] = 11/40 /(11/40+3/32+1/160 ) = 0.7333 (73.33%)

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P(X=2/W)=p(2) /[p(1)+p(2)+p(3)] = 3/32 /(11/40+3/32+1/160 ) = 0.25 (25%)

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(a) 99% confidence interval for the percentage of girls born is [0.804 , 0.896].

(b) Yes​, the proportion of girls is significantly different from 0.50.

Step-by-step explanation:

We are given that a clinical trial tests a method designed to increase the probability of conceiving a girl.

In the study 400 babies were​ born, and 340 of them were girls.

(a) Firstly, the pivotal quantity for 99% confidence interval for the population proportion is given by;

                    P.Q. =  \frac{\hat p-p}{\sqrt{\frac{\hat p(1-\hat p)}{n} } }  ~ N(0,1)

where, \hat p = sample proportion of girls born = \frac{340}{400} = 0.85

             n = sample of babies = 400

             p = population percentage of girls born

<em>Here for constructing 99% confidence interval we have used One-sample z proportion statistics.</em>

<u>So, 99% confidence interval for the population proportion, p is ;</u>

P(-2.58 < N(0,1) < 2.58) = 0.99  {As the critical value of z at 0.5% level

                                                    of significance are -2.58 & 2.58}  

P(-2.58 < \frac{\hat p-p}{\sqrt{\frac{\hat p(1-\hat p)}{n} } } < 2.58) = 0.99

P( -2.58 \times {\sqrt{\frac{\hat p(1-\hat p)}{n} } } < {\hat p-p} < 2.58 \times {\sqrt{\frac{\hat p(1-\hat p)}{n} } } ) = 0.99

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= [ 0.85-2.58 \times {\sqrt{\frac{0.85(1-0.85)}{400} } } , 0.85+2.58 \times {\sqrt{\frac{0.85(1-0.85)}{400} } } ]

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(b) <em>Let p = population proportion of girls born.</em>

So, Null Hypothesis, H_0 : p = 0.50      {means that the proportion of girls is equal to 0.50}

Alternate Hypothesis, H_A : p \neq 0.50      {means that the proportion of girls is significantly different from 0.50}

The test statistics that will be used here is <u>One-sample z proportion test</u> <u>statistics</u>;

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So, <u><em>the test statistics</em></u>  =  \frac{0.85-0.50}{\sqrt{\frac{0.85(1-0.85)}{400} } }

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Now, at 0.01 significance level, the z table gives critical value of 2.3263 for right tailed test. Since our test statistics is way more than the critical value of z as 19.604 > 2.3263, so we have sufficient evidence to reject our null hypothesis due to which <u>we reject our null hypothesis</u>.

Therefore, we conclude that the proportion of girls is significantly different from 0.50.

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