Answer:
The correct option is (D).
Step-by-step explanation:
To construct the (1 - <em>α</em>)% confidence interval for population proportion the distribution of proportions must be approximated by the normal distribution.
A Normal approximation to binomial can be applied to approximate the distribution of proportion <em>p</em>, if the following conditions are satisfied:
In this case <em>p</em> is defined as the proportions of students who ride a bike to campus.
A sample of <em>n</em> = 125 students are selected. Of these 125 students <em>X</em> = 6 ride a bike to campus.
Compute the sample proportion as follows:

Check whether the conditions of Normal approximation are satisfied:

Since
, the Normal approximation to Binomial cannot be applied.
Thus, the confidence interval cannot be used to estimate the proportion of all students who ride a bike to campus.
Thus, the correct option is (D).
Answer:
x < 6/7
Step-by-step explanation:
Your question doesn't say what your choices are, so I'm assuming the inequality should be solved for x... which values of x make 8x < x + 6?
Subtract x from both sides, then divide by 7.
7x < 6
x < 6/7
Answer:
x = 7
Step-by-step explanation:
QY = 5x
YZ = 18
QZ = 53
Thus:
QY + YZ = QZ (segment addition postulate)
5x + 18 = 53
5x = 53 - 18 (subtraction property of equality)
5x = 35
Divide both sides by 5
x = 7
Answer:
the sampling distribution of proportions
Step-by-step explanation:
A sample is a small group of observations which is a subset of a larger population containing the entire set of observations. The proportion of success or measure of a certain statistic from the sample, (in the scenario above, the proportion of obese observations on our sample) gives us the sample proportion. Repeated measurement of the sample proportion of this sample whose size is large enough (usually greater Than 30) in other to obtain a range of different proportions for the sample is called the sampling distribution of proportion. Hence, creating a visual plot such as a dot plot of these repeated measurement of the proportion of obese observations gives the sampling distribution of proportions