Answer:
a) [tex]z=\frac{0.684 -0.58}{\sqrt{\frac{0.58(1-0.58)}{114}}}=2.250[/tex]
b) [tex]p_v =2*P(z>2.250)=0.0244[/tex]
If we compare the p value and the significance level given we see that [tex]p_v <\alpha[/tex] we have enough evidence to reject the null hypothesis at 5% of significance.
Step-by-step explanation:
Data given and notation
n=114 represent the random sample taken
[tex]\hat p=0.684[/tex] estimated proportion of people that their approval rating might have changed
[tex]p_o=0.58[/tex] is the value that we want to test
[tex]\alpha=0.05[/tex] represent the significance level
Confidence=95% or 0.95
z would represent the statistic (variable of interest)
[tex]p_v[/tex] represent the p value (variable of interest)
Hypothesis
We need to conduct a hypothesis in order to test the claim that true proportion of people that their approval rating might have changed is 0.58 or no.:
Null hypothesis:[tex]p=0.58[/tex]
Alternative hypothesis:[tex]p \neq 0.58[/tex]
Part a
[tex]z=\frac{\hat p -p_o}{\sqrt{\frac{p_o (1-p_o)}{n}}}[/tex] (1)
Calculate the statistic
Since we have all the info requires we can replace in formula (1) like this:
[tex]z=\frac{0.684 -0.58}{\sqrt{\frac{0.58(1-0.58)}{114}}}=2.250[/tex]
Part b: Statistical decision
The significance level provided [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>2.250)=0.0244[/tex]
If we compare the p value and the significance level given we see that [tex]p_v <\alpha[/tex] we have enough evidence to reject the null hypothesis at 5% of significance.