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Alex
2 years ago
6

If a hurricane was headed your way, would you evacuate? The headline of a press release states, "Thirty-four Percent of People o

n High-Risk Coast Will Refuse Evacuation Order, Survey of Hurricane Preparedness Finds." This headline was based on a survey of 6138 adults who live within 20 miles of the coast in high hurricane risk counties of eight southern states. In selecting the sample, care was taken to ensure that the sample would be representative of the population of coastal residents in these states.
(a) Use this information to estimate the proportion of coastal residents who would evacuate using a 98% confidence interval. (Round your answers to three decimal places.)
Mathematics
1 answer:
IgorC [24]2 years ago
8 0

Answer:

The 98% confidence interval would be given by (0.326;0.354)

Step-by-step explanation:

1) Notation and definitions

X=63 number of people who live within 20 miles of the coast in high hurricane risk counties of eight southern states

n=6138 random sample taken

\hat p=0.34 estimated proportion of people who live within 20 miles of the coast in high hurricane risk counties of eight southern states

p true population proportion of people who live within 20 miles of the coast in high hurricane risk counties of eight southern states

A confidence interval is "a range of values that’s likely to include a population value with a certain degree of confidence. It is often expressed a % whereby a population means lies between an upper and lower interval".  

The margin of error is the range of values below and above the sample statistic in a confidence interval.  

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".  

The population proportion have the following distribution

p \sim N(p,\sqrt{\frac{p(1-p)}{n}})

2) Confidence interval

In order to find the critical value we need to take in count that we are finding the interval for a proportion, so on this case we need to use the z distribution. Since our interval is at 98% of confidence, our significance level would be given by \alpha=1-0.98=0.02 and \alpha/2 =0.01. And the critical value would be given by:

z_{\alpha/2}=-2.33, z_{1-\alpha/2}=2.33

The confidence interval for the mean is given by the following formula:  

\hat p \pm z_{\alpha/2}\sqrt{\frac{\hat p (1-\hat p)}{n}}

If we replace the values obtained we got:

0.34 - 2.33\sqrt{\frac{0.34(1-0.34)}{6138}}=0.326

0.34 + 2.33\sqrt{\frac{0.34(1-0.34)}{6138}}=0.354

The 98% confidence interval would be given by (0.326;0.354)

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2 years ago
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Answer:

$43.75

Step-by-step explanation:

So you have: 35% discount

Total without discount: $125.00

Amount discounted: ?

So you have to make 35% into a number that you can multiply with $125. When you have a percentage you move the decimal two times to the front so you have 0.35 then you multiply;

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43.75 is the amount discounted, so you only have to pay 81.25. The equation for that would look like;

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Hope that made sense!

7 0
2 years ago
Use Pythagorean identities to prove whether ΔLMN is a right, acute, or obtuse triangle. Show all work for full credit.
diamong [38]

Answer:  The given triangle LMN is an obtuse-angled triangle.

Step-by-step explanation:  We are given to use Pythagorean identities to prove whether ΔLMN is a right, acute, or obtuse triangle.

From the figure, we note that

in ΔLMN, LM = 5 units, MN = 13 units  and  LN = 14 units.

We know that a triangle with sides a units, b units and c units (a  > b, c) is said to be

(i) Right-angled triangle if b^2+c^2=a^2,

(ii) Acute-angled triangle if b^2+c^2>a^2,

(iii) Obtuse-angled triangle if b^2+c^2

For the given triangle LMN, we have

a = 14, b = 13 and c = 5.

So,

b^2+c^2=13^2+5^2=169+25=194,\\\\a^2=14^2=196.

Therefore,  b^2+c^2

Thus, the given triangle LMN is an obtuse-angled triangle.

5 0
2 years ago
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7 0
2 years ago
"Immediately after a ban on using hand-held cell phones while driving was implemented, compliance with the law was measured. A r
sergiy2304 [10]

Answer:

(a) Null Hypothesis, H_0 : p_1-p_2=0  or  p_1= p_2  

    Alternate Hypothesis, H_A : p_1-p_2\neq 0  or  p_1\neq p_2

(b) We conclude that there is a statistical difference in these two proportions measured initially and then one year later.

Step-by-step explanation:

We are given that a random sample of 1,250 drivers found that 98.9% were in compliance. A year after the implementation, compliance was again measured to see if compliance was the same (or not) as previously measured.

A different random sample of 1,100 drivers found 96.9% compliance."

<em />

<em>Let </em>p_1<em> = proportion of drivers that were in compliance initially</em>

p_2<em> = proportion of drivers that were in compliance one year later</em>

(a) <u>Null Hypothesis</u>, H_0 : p_1-p_2=0  or  p_1= p_2      {means that there is not any statistical difference in these two proportions measured initially and then one year later}

<u>Alternate Hypothesis</u>, H_A : p_1-p_2\neq 0  or  p_1\neq p_2     {means that there is a statistical difference in these two proportions measured initially and then one year later}

The test statistics that will be used here is <u>Two-sample z proportion statistics</u>;

                     T.S.  = \frac{(\hat p_1-\hat p_2)-(p_1-p_2)}{\sqrt{ \frac{\hat p_1(1-\hat p_1)}{n_1} + \frac{\hat p_2(1-\hat p_2)}{n_2}} }  ~ N(0,1)

where, \hat p_1 = sample proportion of drivers in compliance initially = 98.9%

\hat p_2 = sample proportion of drivers in compliance one year later = 96.9%

n_1 = sample of drivers initially = 1,250

n_2 = sample of drivers one year later = 1,100

(b) So, <u><em>the test statistics</em></u>  =  \frac{(0.989-0.969)-(0)}{\sqrt{ \frac{0.989(1-0.989)}{1,250} + \frac{0.969(1-0.969)}{1,100}} }  

                                           =  3.33

<u>Now, P-value of the test statistics is given by;</u>

         P-value = P(Z > 3.33) = 1 - P(Z \leq 3.33)

                                            = 1 - 0.99957 = <u>0.00043</u>

Since in the question we are not given with the level of significance so we assume it to be 5%. Now at 5% significance level, the z table gives critical values between -1.96 and 1.96 for two-tailed test.

<em>Since our test statistics does not lies within the range of critical values of z, so we have sufficient evidence to reject our null hypothesis as it will fall in the rejection region due to which </em><u><em>we reject our null hypothesis.</em></u>

Therefore, we conclude that there is a statistical difference in these two proportions measured initially and then one year later.

7 0
2 years ago
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