I WILL GIVE BRAINLIEST, 5 STARS AND THANKS!!! When an inequality models a practical problem, why might some of the points on the
graph not be solutions to the problem even though they are solutions to the inequality?
1 answer:
Answer:
An inequality often covers an infinite area. Depending on the problem, any area either below or above the function is included in the set of solutions.
For example, see the attached photo.
y < x is graphed here.
When you substitute x = 0 into the function, y will equal 0 and this point is a solution to the problem. However, if any point under this line, such as (1, 0), (-9, 0), or even (1000000000, 0), was chosen, these are still solutions to the inequality, but not the problem.
Step-by-step explanation:
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Answer:
Explained below.
Step-by-step explanation:
According to the Central limit theorem, if from an unknown population large samples of sizes n > 30, are selected and the sample proportion for each sample is computed then the sampling distribution of sample proportion follows a Normal distribution.
The mean of this sampling distribution of sample proportion is:
The standard deviation of this sampling distribution of sample proportion is:
(a)
The sample selected is of size <em>n</em> = 450 > 30.
Then according to the central limit theorem the sampling distribution of sample proportion is normally distributed.
The mean and standard deviation are:
So, the sampling distribution of sample proportion is .
(b)
Compute the probability that the sample proportion will be within 0.04 of the population proportion as follows:
Thus, the probability that the sample proportion will be within 0.04 of the population proportion is 0.95.
(c)
The sample selected is of size <em>n</em> = 200 > 30.
Then according to the central limit theorem the sampling distribution of sample proportion is normally distributed.
The mean and standard deviation are:
So, the sampling distribution of sample proportion is .
(d)
Compute the probability that the sample proportion will be within 0.04 of the population proportion as follows:
Thus, the probability that the sample proportion will be within 0.04 of the population proportion is 0.81.
(e)
The probability that the sample proportion will be within 0.04 of the population proportion if the sample size is 450 is 0.95.
And the probability that the sample proportion will be within 0.04 of the population proportion if the sample size is 200 is 0.81.
So, there is a gain in precision on increasing the sample size.