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Viktor [21]
3 years ago
9

In a study researchers reported the mean BMI for men 60 and older to be 24.7 with a standard deviation of 3.3 and the mean BMI f

or women 60 and older to be 23.1 with a standard deviation of 3.7. Using these values as the populations mean and standard deviation for men and women respectively, find the probability that the difference in the mean BMI (men-women) for 45 women and 50 men selected independently and at random will exceed 2.1.
Mathematics
1 answer:
erica [24]3 years ago
6 0

Answer:

Probability that the difference in the mean BMI (men-women) for 45 women and 50 men selected independently and at random will exceed 2.1 = 0.2451

Step-by-step explanation:

The central limit theorem helps us to obtain the mean and the standard deviation of any sampling distribution.

Given that the sample was obtained from a normal distribution or an approximately normal distribution & it was obtained using random sampling techniques with each variable independent of one another and with each sample with adequate sample size,

Mean of sampling distribution (μₓ) = Population mean (μ)

Standard deviation of the sampling distribution = σₓ = (σ/√N)

where σ = population mean

N = Sample size

For the 45 women

μₓ = μ = 23.1

σₓ = (σ/√N) = (3.7/√45) = 0.552

For the 50 men

μₓ = μ = 24.7

σₓ = (σ/√N) = (3.3/√50) = 0.467

To find the probability that the difference in the mean BMI (men-women) for 45 women and 50 men selected independently and at random will exceed 2.1, we need to combine the distributions.

New distribution = (BMI of men) - (BMI of women) = x = X₁ - X₂

When independent distributions are combined, the combined mean and combined variance are given through the relation

Combined mean = Σ λᵢμᵢ

(summing all of the distributions in the manner that they are combined)

Combined variance = Σ λᵢ²σᵢ²

(summing all of the distributions in the manner that they are combined)

λ₁ = 1, λ₂ = -1

μ₁ = 24.7, μ₂ = 23.1

σ₁ = 0.467, σ₂ = 0.552

Combined mean = (Mean of men) - (Mean of women) = 24.7 - 23.1 = 1.6

Combined Variance = (1²×0.467²) + [(-1)²×(0.552²)] = 0.522793

Combined standard deviation = √0.522793 = 0.723

Probability that the difference in the mean BMI (men-women) for 45 women and 50 men selected independently and at random will exceed 2.1 = P(x > 2.1)

Note that the resulting distribution from the combination of distributions is still a normal distribution since the distributions combined were normal distributions too.

Hence, we first normalize or standardize 2.1

The standardized score for any value is the value minus the mean then divided by the standard deviation.

z = (x - μ)/σ = (2.1 - 1.6)/0.723 = 0.69

To determine the required probability

P(x > 2.1) = P(z > 0.69)

We'll use data from the normal distribution table for these probabilities

P(x > 2.1) = P(z > 0.69) = 1 - P(z ≤ 0.69)

= 1 - 0.7549

= 0.2451

Hope this Helps!!!

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Answer:

z=\frac{0.0003214 -0.00034}{\sqrt{\frac{0.00034(1-0.00034)}{420095}}}=-0.654  

p_v =2*P(Z  

So the p value obtained was a very high value and using the significance level given \alpha=0.005 we have p_v>\alpha so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and we can say that the true proportion not differs significantly from the specified value of 0.00034 or 0.034%.

Step-by-step explanation:

1) Data given and notation  

n=420095 represent the random sample taken

X=135 represent the subjects developed cancer of the brain or nervous system (based on data from the Journal of the National Cancer Institute as reported in USA Today)

\hat p=\frac{135}{420095}=0.0003214 estimated proportion of subjects developed cancer of the brain or nervous system (based on data from the Journal of the National Cancer Institute as reported in USA Today)

p_o=0.00034 is the value that we want to test

\alpha=0.005 represent the significance level

Confidence=99.5% or 0.995

z would represent the statistic (variable of interest)

p_v represent the p value (variable of interest)  

2) Concepts and formulas to use  

We need to conduct a hypothesis in order to test the claim that the brain or nervous system at a rate that is different from the rate of 0.0340% :  

Null hypothesis:p=0.00034  

Alternative hypothesis:p \neq 0.00034  

When we conduct a proportion test we need to use the z statistic, and the is given by:  

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

The One-Sample Proportion Test is used to assess whether a population proportion \hat p is significantly different from a hypothesized value p_o.

3) Calculate the statistic  

Since we have all the info requires we can replace in formula (1) like this:  

z=\frac{0.0003214 -0.00034}{\sqrt{\frac{0.00034(1-0.00034)}{420095}}}=-0.654  

4) Statistical decision  

It's important to refresh the p value method or p value approach . "This method is about determining "likely" or "unlikely" by determining the probability assuming the null hypothesis were true of observing a more extreme test statistic in the direction of the alternative hypothesis than the one observed". Or in other words is just a method to have an statistical decision to fail to reject or reject the null hypothesis.  

The significance level provided \alpha=0.005. The next step would be calculate the p value for this test.  

Since is a bilateral test the p value would be:  

p_v =2*P(Z  

So the p value obtained was a very high value and using the significance level given \alpha=0.005 we have p_v>\alpha so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and we can say that the true proportion not differs significantly from the specified value of 0.00034 or 0.034%.  

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