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Gnesinka [82]
2 years ago
8

Please help me with question 1.

Mathematics
2 answers:
muminat2 years ago
4 0

Answer:

C =66

Step-by-step explanation:

Amiraneli [1.4K]2 years ago
4 0
Opposite angles in a cyclic quadrilateral sum up to 180

m114° + mmm
Answer : 66°
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I need help with number 4 if you know it
Svetradugi [14.3K]

Answer:

the answer is 5500 but if not put 6000$

3 0
2 years ago
An article reports that 1 in 500 people carry the defective gene that causes inherited colon cancer. In a sample of 2500 individ
Ne4ueva [31]

Answer:

The approximate distribution of the number who carry this gene is approximately normal with mean \mu = 5 and standard deviation \sigma = 2.23

Step-by-step explanation:

For each person, there are only two possible outcomes. Either they carry the defective gene that causes inherited colon cancer, or they do not. The probability of a person carrying this gene is independent from other people. So the binomial probability distribution is used to solve this question.

A sample of 2500 individuals is quite large, so we use the binomial approximation to the normal to solve this question.

Binomial probability distribution

Probability of exactly x sucesses on n repeated trials, with p probability.

Can be approximated to a normal distribution, using the expected value and the standard deviation.

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)}.

An article reports that 1 in 500 people carry the defective gene that causes inherited colon cancer.

This means that p = \frac{1}{500} = 0.002

In a sample of 2500 individuals, what is the approximate distribution of the number who carry this gene?

n = 2500

So

\mu = E(X) = np = 2500*0.002 = 5

\sigma = \sqrt{V(X)} = \sqrt{np(1-p)} = \sqrt{2500*0.002*0.998} = 2.23

So the approximate distribution of the number who carry this gene is approximately normal with mean \mu = 5 and standard deviation \sigma = 2.23

4 0
3 years ago
Mited
Natasha_Volkova [10]

9514 1404 393

Answer:

  • relative minimum -6√3 at x = -√3
  • relative maximum 6√3 at x = √3
  • decreasing on x < -√3 and x > √3
  • increasing on -√3 < x < √3
  • see below for a graph

Step-by-step explanation:

I find it convenient to draw the graph first when looking for relative extrema.

The function can be differentiated to get ...

  f'(x) = -3x^2 +9

This is zero when ...

  -3x^2 +9 = 0

  x^2 = 3

  x = ±√3 . . . . . x-values of relative extrema

Then the extreme values are ...

  f(±√3) = x(9 -x^2) = (±√3)(9 -3) = ±6√3

The lower extreme (minimum) corresponds to the lower value of x (-√3), so the extrema are ...

 (x, y) = (-√3, -6√3) and (√3, 6√3)

__

Since the leading coefficient is negative and the degree is odd, the function is decreasing for values of x below the minimum and above the maximum. It is increasing for values of  x between the minimum and the maximum.

  decreasing: x < -√3, and √3 < x

  increasing: -√3 < x < √3

7 0
3 years ago
What is the measure of angle x?
velikii [3]
Whatever 180 - 109 =. I ain't doing all the word bro XD
7 0
2 years ago
Read 2 more answers
Find the unit price. 14 ounces of canned corn for $1.96​
Genrish500 [490]

The unit price of canned corn is $0.14

14 ounces = $1.96

1 ounce = 1.96 / 14

= 0.14

3 0
1 year ago
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