Respuesta :
Answer:
For this case the statistic is given [tex] z_{calc}= 2.4[/tex]
Since is a bilateral test the p value would be:
[tex]p_v =2*P(z>2.4)=0.0164[/tex]
Step-by-step explanation:
Data given and notation
n represent the random sample taken
[tex]\hat p[/tex] estimated proportion of interest
[tex]p_o=0.25[/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 is 0.25 or no.:
Null hypothesis:[tex]p=0.25[/tex]
Alternative hypothesis:[tex]p \neq 0.25[/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
For this case the statistic is given [tex] z_{calc}= 2.4[/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.
Since is a bilateral test the p value would be:
[tex]p_v =2*P(z>2.4)=0.0164[/tex]
