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Alex777 [14]
3 years ago
7

Determine the percentage of all samples of three men that have mean brain weights within 0.1 kg of the population mean brain wei

ght of kg. Interpret your answer in terms of sampling error
Mathematics
1 answer:
yawa3891 [41]3 years ago
3 0

Answer:

The result indicates that the percentage of all samples of three men that have mean brain weights within (1.24 * sampling error) of the mean is 78.50%.

Step-by-step explanation:

Note: This question is not complete. The complete question is therefore provided before answering the question as follows:

According to one study, brain weights of men are normally distributed with mean = 1.20 kg and a standard deviation = 0.14 kg.

Determine the percentage of all samples of three men that have mean brain weights within 0.1 kg of the population mean brain weight of 1.20 kg. Interpret your answer in terms of sampling error.

The explanation of the answers is now provided as follows:

Based on the Central limit theorem, it possible to say that the mean of sampling distribution (μₓ) is approximately equal to the population mean (μ) as follows:

μₓ = μ = 1.20 kg …………………………. (1)

Also, the standard deviation of the sampling distribution can be written as follows:

σₓ = (σ/√N) ……………………….. (2)

Where:

σ = population standard deviation = 0.14 kg

N = Sample size = 3

Substituting the values into equation (2), we have:

σₓ = 0.14 / √3 = 0.0808

Since we are to determine the percentage of all samples of three men that have mean brain weights within 0.1 kg of the population mean brain weight of 1.20 kg, this implies that we have:

P(1.10 ≤ x ≤ 1.30)

Therefore, 1.10 and 1.30 have to be first normalized or standardized as follows:

For 1.10 kg

z = (x - μₓ) / σₓ = (1.10 - 1.20) / 0.0808 = -1.24

For 1.30 kg

z = (x - μₓ)/σₓ = (1.30 - 1.20) / 0.0808 = 1.24

The required probability can be determined when P(1.10 ≤ x ≤ 1.30) = P(-1.24 ≤ z ≤ 1.24).

From the normal distribution table, the following can be obtained for these probabilities:

P(1.10 ≤ x ≤ 1.30) = P(-1.24 ≤ z ≤ 1.24) = P(z ≤ 1.24) - P(z ≤ -1.24) = 0.89251 - 0.10749 = 0.7850, or 78.50%

Therefore, the sampling error is equal to 0.0808 which is the standard deviation of the sampling distribution.

In terms of the sampling error, the result indicates that the percentage of all samples of three men that have mean brain weights within (1.24 * sampling error) of the mean is 78.50%.

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Step-by-step explanation:

We are given that a certain geneticist is interested in the proportion of males and females in the population who have a minor blood disorder.

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<em>Here for constructing 95% confidence interval we have used Two-sample z proportion statistics.</em>

<u>So, 95% confidence interval for the difference between the population proportions, </u><u>(</u>p_1-p_2<u>)</u><u> is ;</u>

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P(-1.96 < \frac{(\hat p_1-\hat p_2)-(p_1-p_2)}{\sqrt{\frac{\hat p_1(1-\hat p_1)}{n_1}+ \frac{\hat p_2(1-\hat p_2)}{n_2}} } < 1.96) = 0.95

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P( (\hat p_1-\hat p_2)-1.96 \times {\sqrt{\frac{\hat p_1(1-\hat p_1)}{n_1}+ \frac{\hat p_2(1-\hat p_2)}{n_2}} } < (p_1-p_2) < (\hat p_1-\hat p_2)+1.96 \times {\sqrt{\frac{\hat p_1(1-\hat p_1)}{n_1}+ \frac{\hat p_2(1-\hat p_2)}{n_2}} } ) = 0.95

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