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GuDViN [60]
3 years ago
11

A 90% confidence interval for the mean height of students

Mathematics
1 answer:
Westkost [7]3 years ago
4 0

Answer:

ME= \frac{69.397-60.128}{2}= 4.6345 \approx 4.635

And the best answer on this case would be:

b) m = 4.635

Step-by-step explanation:

Let X the random variable of interest and we know that the confidence interval for the population mean \mu is given by this formula:

\bar X \pm t_{\alpha/2} \frac{s}{\sqrt{n}}

The confidence level on this case is 0.9 and the significance \alpha=1-0.9=0.1

The confidence interval calculated on this case is 60.128 \leq \mu \leq 69.397

The margin of error for this confidence interval is given by:

ME =t_{\alpha/2} \frac{s}{\sqrt{n}}

Since the confidence interval is symmetrical we can estimate the margin of error with the following formula:

ME = \frac{Upper -Lower}{2}

Where Upper and Lower represent the bounds for the confidence interval calculated and replacing we got:

ME= \frac{69.397-60.128}{2}= 4.6345 \approx 4.635

And the best answer on this case would be:

b) m = 4.635

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An assembly plant orders a large shipment of electronic circuits each month. The supplier claims that the population proportion
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Answer:

p_v =P(z>2.652)=0.0040  

So the p value obtained was a very low value and using the significance level assumed for example \alpha=0.05 we have p_v so we can conclude that we have enough evidence to reject the null hypothesis, and we can said that at 5% of significance the proportion of defectives is significantly higher than 0.04.  

Step-by-step explanation:

1) Data given and notation  

n=300 represent the random sample taken

X=21 represent the number of defectives (value assumed)

\hat p=\frac{21}{300}=0.07 estimated proportion of defectives

p_o=0.04 is the value that we want to test

\alpha represent the significance level

z would represent the statistic (variable of interest)

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2) Concepts and formulas to use  

We need to conduct a hypothesis in order to test the claim that the proportion of defectives it's higher than 0.04.:  

Null hypothesis:p\leq 0.04  

Alternative hypothesis:p > 0.04  

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{0.07 -0.04}{\sqrt{\frac{0.04(1-0.04)}{300}}}=2.652  

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 next step would be calculate the p value for this test.  

Since is a right tailed test the p value would be:  

p_v =P(z>2.652)=0.0040  

So the p value obtained was a very low value and using the significance level assumed for example \alpha=0.05 we have p_v so we can conclude that we have enough evidence to reject the null hypothesis, and we can said that at 5% of significance the proportion of defectives is significantly higher than 0.04.  

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