By using proportions, We expect 150 defects out of the 10,000 cars if the 3 defects out of the 200 cars sampled is representative of all 10,000 cars.
Option A is correct.
First, let's set up the proportions.
A proportion is an equation in which two ratios are set equal to each other.
We have 3/200 cars with defects and we want to know X for X/10000.
So 3/200 = X/10000
Now solve for X.
3/200 = X/10000
X = 
X = 150
So the answer is: We expect 150 defects out of the 10,000 cars if the 3 defects out of the 200 cars sampled is representative of all 10,000 cars.
Option A is correct.
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Answer
∴ The true statement is Answer:
The true statement is BD ≅ CE ⇒ 3rd answer
Step-by-step explanation:
- There is a line contained points B , C , D , E
- All points are equal distance from each other
- That means the distance of BC equal the distance of CD and equal
the distance of DE
∴ BC = CD = DE
- That means the line id divided into 3 equal parts, each part is one
third the line
∴ BC = 1/3 BE
∴ CD = 1/3 BE
∴ DE = 1/3 BE
∵ BC = CD
∴ C is the mid-point of BD
∴ BC = 1/2 BD
∵ CD = DE
∴ D is the mid-point of DE
∴ CD = 1/2 CE
- Lets check the answers
* BD = one half BC is not true because BC = one half BD
* BC = one half BE is not true because BC = one third BE
* BD ≅ CE is true because
BD = BC + CD
CE = CD + DE
BC ≅ DE and CD is common
then BD ≅ CE
* BC ≅ BD is not true because BC is one half BD
∴ The true statement is BD ≅ CE
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Answer:
16/45
Step-by-step explanation:
4/9 x 4/5=16/45 you just multiply straight across
Answer:
The probability is 1/2
Step-by-step explanation:
The time a person is given corresponds to a uniform distribution with values between 0 and 100. The mean of this distribution is 0+100/2 = 50 and the variance is (100-0)²/12 = 833.3.
When we take 100 players we are taking 100 independent samples from this same random variable. The mean sample, lets call it X, has equal mean but the variance is equal to the variance divided by the length of the sample, hence it is 833.3/100 = 8.333.
As a consecuence of the Central Limit Theorem, the mean sample (taken from independant identically distributed random variables) has distribution Normal with parameters μ = 50, σ= 8.333. We take the standarization of X, calling it W, whose distribution is Normal Standard, in other words
The values of the cummulative distribution of the Standard Normal distribution, lets denote it 
, are tabulated and they can be found in the attached file, We want to know when X is above 50, we can solve that by using the standarization
