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Anton [14]
2 years ago
11

A pew research Center project on the state of news media showed that the clearest pattern of news audience growth in 2012 came o

n digital platforms. According to pew research data, 39% of Americans get news online or from a mobile device in a typical day. a. Suppose that you take a sample of 100 Americans. If the population proportion of Americans who get news online or from a mobile device in a typical day is 0.39, what is the probability that fewer than 30% in your sample will get news online or from a mobile device in a typical day? b. Suppose that you take a sample of 400 Americans. If the population proportion of Americans who get news online or from a mobile device in a typical day is 0.39, what is the probability that fewer than 30% in your sample will get news online or from a mobile device in a typical day? c. Discuss the effect of sample size on the sampling distribution of the proportion in general and the effect on the probabilities in (a) and (b).
Mathematics
1 answer:
KengaRu [80]2 years ago
6 0

Answer:

Step-by-step explanation:

Given that:

the sample proportion p = 0.39

sample size = 100

Then np = 39

Using normal approximation

The sampling distribution from the sample proportion is approximately normal.

Thus, mean \mu _{\hat p} = p = 0.39

The standard deviation;

\sigma = \sqrt{\dfrac{p(1-p)}{n} }

\sigma = \sqrt{\dfrac{0.39(1-0.39)}{100} }

\sigma = 0.048

The test statistics can be computed as:

Z = \dfrac{{\hat _{p}} - \mu_{_ {\hat p}} }{\sigma_{\hat p}}

Z = \dfrac{0.3 - 0.39 }{0.0488}

Z = -1. 8 4

From the z - tables;

P (\hat p \le 0.3 ) = P(z \le -1.84)

\mathbf{P (\hat p \le 0.3 ) = 0.0329}

(b)

Here;

the sample proportion = 0.39

the sample size n = 400

Since np = 400 * 0.39 = 156

Thus, using normal approximation.

From the sample proportion, the sampling distribution is approximate to the mean \mu_{\hat p} =  p = 0.39

the standard deviation \sigma_{\hat p} = \sqrt{\dfrac{p(1-p)}{n} }

\sigma_{\hat p} = \sqrt{\dfrac{0.39 (1-0.39)}{400} }

\sigma_{\hat p} =0.0244

The test statistics can be computed as:

Z = \dfrac{{\hat _{p}} - \mu_{_ {\hat p}} }{\sigma_{\hat p}}

Z = \dfrac{0.3 - 0.39 }{0.0244}

Z = -3.69

From the z - tables;

P (\hat p \le 0.3 ) = P(z \le -3.69)

\mathbf{P (\hat p \le 0.3 ) = 0.0001}

(c) The effect of the sample size on the sampling distribution is that:

As sample size builds up, the standard deviation of the sampling distribution decreases.

In addition to that, reduction in the standard deviation resulted in increases in the Z score, and the probability of having a sample proportion  that is less than 30% also decreases.

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