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hodyreva [135]
3 years ago
7

Determine the standard deviation of the data below. (1, 2, 3, 4, 5)

Mathematics
1 answer:
Burka [1]3 years ago
4 0

Answer:

\sqrt{2} or 1.414

Step-by-step explanation:

1) Find the mean. 1+2+3+4+5 = 15. 15/5= 3

2) For each data point, find the square of its distance to the mean. (4, 1, 0, 1, 4)

3) Sum the values from Step 2. 10

4) Divide by the number of data points. 10/5= 2

5) Take the square root. \sqrt{2}

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Answer:

The null and alternative hypothesis can be written as:

H_0: \pi_1-\pi_2=0\\\\H_a:\pi_1-\pi_2< 0

Step-by-step explanation:

This is a hypothesis test for the difference between proportions.

The claim is that the proportion of 18- to 34-year-old Americans that own a cell phone is less than the proportion of 35- to 49-year-old Americans.

This claim will be reflected in the alternnative hypothesis, that will state that the population proportion 1 (18 to 34) is significantly smaller than the population proportion 2 (35 to 49).

On the contrary, the null hypothesis will state that the population proportion 1 is ot significantly smaller than the population proportion 2.

Then, the null and alternative hypothesis can be written as:

H_0: \pi_1-\pi_2=0\\\\H_a:\pi_1-\pi_2< 0

The significance level is assumed to be 0.05.

The sample 1, of size n1=200 has a proportion of p1=0.63.

p_1=X_1/n_1=126/200=0.63

The sample 2, of size n2=175 has a proportion of p2=0.68.

p_2=X_2/n_2=119/175=0.68

The difference between proportions is (p1-p2)=-0.05.

p_d=p_1-p_2=0.63-0.68=-0.05

The pooled proportion, needed to calculate the standard error, is:

p=\dfrac{X_1+X_2}{n_1+n_2}=\dfrac{126+119}{200+175}=\dfrac{245}{375}=0.653

The estimated standard error of the difference between means is computed using the formula:

s_{p1-p2}=\sqrt{\dfrac{p(1-p)}{n_1}+\dfrac{p(1-p)}{n_2}}=\sqrt{\dfrac{0.653*0.347}{200}+\dfrac{0.653*0.347}{175}}\\\\\\s_{p1-p2}=\sqrt{0.001132+0.001294}=\sqrt{0.002427}=0.049

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z=\dfrac{p_d-(\pi_1-\pi_2)}{s_{p1-p2}}=\dfrac{-0.05-0}{0.049}=\dfrac{-0.05}{0.049}=-1.01

This test is a left-tailed test, so the P-value for this test is calculated as (using a z-table):

\text{P-value}=P(z

As the P-value (0.1554) is bigger than the significance level (0.05), the effect is not significant.

The null hypothesis failed to be rejected.

There is not enough evidence to support the claim that the proportion of 18- to 34-year-old Americans that own a cell phone is less than the proportion of 35- to 49-year-old Americans.

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