Answer this plese I NEED IT NOW OR I WILL FAIL

I got 27.5 for the mean
mean: you add up all the #'s and divide them my their quantity( in other words by how many numbers there are)
In your case you have 8 numbers, you add them and divide them by 8!

Hope it helps ;)

4 0

3 years ago

The International Air Transport Association surveys business travelers to develop quality ratings for transatlantic gateway airp

yKpoI14uk [10]

Answer:

Confidence Interval in 95% confidence level for the quality rating is (6.06,7.46)

Step-by-step explanation:

Confidence Interval can be calculated using the formula M±ME where

M is the mean of the sample
ME is the margin of error in a given confidence level

Using the sample obtained from 50 business travelers we get

Mean of the sample is 6.76
standard deviation of the sample is 2.526

Margin of error (ME) around the mean using the formula

ME= $\frac{z*s}{\sqrt{N} }$ where

z is the corresponding statistic in 95% confidence level (1.96)

s is the standard deviation of the sample (2.526)

N is the sample size (50)

Using the numbers in the formula we get:

ME= $\frac{1.96*2.526}{\sqrt{50} }$ ≈ 0.70

Then the confidence interval becomes 6.76±0.70

6 0

3 years ago

According to a Yale program on climate change communication survey, 71% of Americans think global warming is happening.† (a) For

SpyIntel [72]

Answer:

a) 0.2741 = 27.41% probability that at least 13 believe global warming is occurring

b) 0.7611 = 76.11% probability that at least 110 believe global warming is occurring

Step-by-step explanation:

Binomial probability distribution

The binomial probability is the probability of exactly x successes on n repeated trials, and X can only have two outcomes.

$P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}$

In which $C_{n,x}$ is the number of different combinations of x objects from a set of n elements, given by the following formula.

$C_{n,x} = \frac{n!}{x!(n-x)!}$

And p is the probability of X happening.

The expected value of the binomial distribution is:

$E(X) = np$

The standard deviation of the binomial distribution is:

$\sqrt{V(X)} = \sqrt{np(1-p)}$

Normal probability distribution

Problems of normally distributed samples can be solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the zscore of a measure X is given by:

$Z = \frac{X - \mu}{\sigma}$

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.

When we are approximating a binomial distribution to a normal one, we have that $\mu = E(X)$ , $\sigma = \sqrt{V(X)}$ .