Respuesta :
Answer:
[tex]z=\frac{0.537-0.531}{\sqrt{0.534(1-0.534)(\frac{1}{147}+\frac{1}{147})}}=0.103[/tex]
Now we can calculate the p value with the following probability taking in count the alternative hypothesis:
[tex]p_v =2*P(Z>0.103) =0.918 [/tex]
For this case since the p value is large enough we have enough evidence to FAIL to reject the null hypothesis and we can't conclude that we have significant differences between the two proportions analyzed.
Step-by-step explanation:
Information provided
[tex]X_{1}=79[/tex] represent the number of New Yorkers familiar with the brand
[tex]X_{2}=78[/tex] represent the number of Californians familiar with the brand
[tex]n_{1}=147[/tex] sample of New Yorkers
[tex]n_{2}=147[/tex] sample of Californians
[tex]p_{1}=\frac{79}{147}=0.537[/tex] represent the proportion New Yorkers familiar with the brand
[tex]p_{2}=\frac{78}{147}=0.531[/tex] represent the proportion of Californians familiar with the brand
[tex]\hat p[/tex] represent the pooled estimate of p
z would represent the statistic
[tex]p_v[/tex] represent the value
System of hypothesis
We want to verify if the two proportions of interest for this case are equal, the system of hypothesis would be:
Null hypothesis:[tex]p_{1} = p_{2}[/tex]
Alternative hypothesis:[tex]p_{1} \neq p_{2}[/tex]
The statistic would be given by:
[tex]z=\frac{p_{1}-p_{2}}{\sqrt{\hat p (1-\hat p)(\frac{1}{n_{1}}+\frac{1}{n_{2}})}}[/tex] (1)
Where [tex]\hat p=\frac{X_{1}+X_{2}}{n_{1}+n_{2}}=\frac{79+78}{147+147}=0.534[/tex]
Repalcing the info given we got:
[tex]z=\frac{0.537-0.531}{\sqrt{0.534(1-0.534)(\frac{1}{147}+\frac{1}{147})}}=0.103[/tex]
Now we can calculate the p value with the following probability taking in count the alternative hypothesis:
[tex]p_v =2*P(Z>0.103) =0.918 [/tex]
For this case since the p value is large enough we have enough evidence to FAIL to reject the null hypothesis and we can't conclude that we have significant differences between the two proportions analyzed.