Respuesta :
Answer:
The proportion of U.S. households that owned two or more televisions is 83%.
Step-by-step explanation:
To determine whether the proportions of U.S. households that owned two or more televisions is less than 83% or not let us perform a hypothesis test for single proportion.
Assumptions:
The sample size (n) selected by the local cable company is 300 which is quite large. Then according to the Central limit theorem the sampling distribution of sample proportion follows a normal distribution with mean p and standard deviation [tex]\sqrt{\frac{p(1-p)}{n} }[/tex] .
Since the sampling distribution of sample proportions follows a normal distribution use the z-test for one proportion to perform the test.
The hypothesis is:
[tex]H_{0}[/tex] : The proportion of U.S. households that owned two or more televisions is 83%, i.e. [tex]p=0.83[/tex]
[tex]H_{1}[/tex] :The proportion of U.S. households that owned two or more televisions is less than 83%, i.e. [tex]p< 0.83[/tex]
Decision Rule:
At the level of significance α = 0.05 the critical region for a one-tailed z-test is:
[tex]\\ Z\leq -1.645\\[/tex]
**Use the z table for the critical values.
So, if [tex]\\ Z\leq -1.645\\[/tex] the null hypothesis will be rejected.
Test statistic value:
[tex]z=\frac{\hat{p}-p}{\sqrt{\frac{p(1-p)}{n}}}[/tex]
Here [tex]\hat{p}[/tex] is the sample proportion.
Compute the value of [tex]\hat{p}[/tex] as follows:
[tex]\hat{p}=\frac{X}{n} \\=\frac{240}{300}\\ =0.80[/tex]
Now compute the value of the test statistic as follows:
[tex]z=\frac{\hat{p}-p}{\sqrt{\frac{p(1-p)}{n}}}\\=\frac{0.80-0.83}{\sqrt{\frac{0.83*(1-0.83)}{300} } } \\=-1.383[/tex]
The test statistic is -1.383 which is more than -1.645.
Thus, the test statistic lies in the acceptance region.
Hence we fail to reject the null hypothesis.
Conclusion:
At 0.05 level of significance we fail to reject the null hypothesis stating that the proportion of U.S. households that owned two or more televisions is 83%.