Suppose that the number of airplanes arriving at an airport per minute is a Poisson process. The average number of airplanes arriving per minute is 3. The probability that exactly 6 planes arrive in the next minute is 0.05041.
Indicate whether the statement is true or false
Q. 2Suppose a sample of n = 50 items is selected from a population of manufactured products and the weight, X, of each item is recorded. Prior experience has shown that the weight has a probability distribution with = 6 ounces and = 2.5 ounces. Which of the following is true about the sampling distribution of the sample mean if a sample size of 15 is selected?
A) The shape of the sampling distribution is approximately normal.
B) The standard deviation of the sampling distribution is 2.5 ounces.
C) The mean of the sampling distribution is 6 ounces.
D) All of the above are correct.
Q. 3Suppose that a judge's decisions follow a binomial distribution and that his verdict is incorrect 10 of the time. In his next 10 decisions, the probability that he makes fewer than 2 incorrect verdicts is 0.736.
Indicate whether the statement is true or false
Q. 4The Central Limit Theorem is considered powerful in statistics because it works for any population distribution provided the sample size is sufficiently large and the population mean and standard deviation are known.
Indicate whether the statement is true or false
Q. 5If remains constant in a binomial distribution, an increase in n will not change the mean.
Indicate whether the statement is true or false
Q. 6A sample that does not provide a good representation of the population from which it was collected is referred to as a(n) ________ sample.
Fill in the blank(s) with correct word
Q. 7If remains constant in a binomial distribution, an increase in n will increase the variance.
Indicate whether the statement is true or false
Q. 8Why is the Central Limit Theorem so important to the study of sampling distributions?
A) It allows us to disregard the shape of the population when n is large.
B) It allows us to disregard the size of the sample selected when the population is not normal.
C) It allows us to disregard the size of the population we are sampling from.
D) It allows us to disregard the shape of the sampling distribution when the size of the population is large.
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