Respuesta :
Answer:
[tex]z=\frac{0.70 -0.73}{\sqrt{\frac{0.73(1-0.73)}{1100}}}=-2.241[/tex]
[tex]p_v =2*P(z>2.241)=0.025[/tex]
So the p value obtained was a very low value and using the significance level given [tex]\alpha=0.05[/tex] we have [tex]p_v<\alpha[/tex] 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 chips do not fail in the first 1000 hours of their use is different from 0.73 at 5% of significance.
Step-by-step explanation:
Data given and notation
n=1100 represent the random sample taken
[tex]\hat p=0.7[/tex] estimated proportion of chips do not fail in the first 1000 hours of their use
[tex]p_o=0.73[/tex] is the value that we want to test
[tex]\alpha[/tex] represent the significance level
z would represent the statistic (variable of interest)
[tex]p_v[/tex] represent the p value (variable of interest)
Concepts and formulas to use
We need to conduct a hypothesis in order to test the claim that the true proportion of interest is different from 0.73.:
Null hypothesis:[tex]p=0.73[/tex]
Alternative hypothesis:[tex]p \neq 0.73[/tex]
When we conduct a proportion test we need to use the z statistic, and the is given by:
[tex]z=\frac{\hat p -p_o}{\sqrt{\frac{p_o (1-p_o)}{n}}}[/tex] (1)
The One-Sample Proportion Test is used to assess whether a population proportion [tex]\hat p[/tex] is significantly different from a hypothesized value [tex]p_o[/tex].
Calculate the statistic
Since we have all the info requires we can replace in formula (1) like this:
[tex]z=\frac{0.70 -0.73}{\sqrt{\frac{0.73(1-0.73)}{1100}}}=-2.241[/tex]
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 assumed for this case is[tex]\alpha=0.05[/tex]. The next step would be calculate the p value for this test.
Since is a bilateral test the p value would be:
[tex]p_v =2*P(z>2.241)=0.025[/tex]
So the p value obtained was a very low value and using the significance level given [tex]\alpha=0.05[/tex] we have [tex]p_v<\alpha[/tex] 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 chips do not fail in the first 1000 hours of their use is different from 0.73 at 5% of significance.
