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UCF QMB 3200 Final Exam Questions With Answers 100% Correct

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UCF QMB 3200 Final Exam Questions With Answers 100% Correct Cluster sampling is a probability sampling method. The central limit theorem states that if the sample size n is large, then the sampling distribution of the sample mean can be approximated by a normal distribution. The value of the...

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  • November 22, 2024
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  • Questions & answers
  • QMB 3200
  • QMB 3200
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UpperClass
UCF QMB 3200 Final Exam Questions With
Answers 100% Correct

Cluster sampling is a probability sampling method.




The central limit theorem states that if the sample size n is large, then the sampling

distribution of the sample mean can be approximated by a normal distribution.




The value of the _____ is used to estimate the value of the population parameter sample

statistic




The sampling distribution of is the probability distribution of all possible values of the

sample proportion.




Which of the following is not a symbol for a parameter? S.




The sample statistic characteristic s is the point estimator of σ..




The distribution of values taken by a statistic in all possible samples of the same size from the

same population is called a sampling distribution.

, UCF QMB 3200 Final Exam Questions With
Answers 100% Correct

Which of the following is a point estimator? S.




As a rule of thumb, the sampling distribution of the sample proportion can be approximated by a

normal probability distribution when n(1 - p) ≥ 5 and np ≥ 5.




A sample of 92 observations is taken from an infinite population. The sampling distribution of is

approximately normal because of the central limit theorem.




The central limit theorem is important in Statistics because it enables reasonably accurate

probabilities to be determined for events involving the sample average when the sample

size is large regardless of the distribution of the variable.




The distribution of values taken by a statistic in all possible samples of the same size from the

same population is the sampling distribution of The sample




Which of these best describes a sampling distribution of a statistic? It is the distribution of

all of the statistics calculated from all possible samples of the same sample size.

, UCF QMB 3200 Final Exam Questions With
Answers 100% Correct

The probability distribution of all possible values of the sample proportion is the sampling

distribution of p.




For a fixed confidence level and population standard deviation, if we would like to cut our

margin of error in half, we should take a sample size that is four times as large as the

original sample size.




We can reduce the margin of error in an interval estimate of p by doing any of the following

except increasing the planning value p* to .5.




When computing the sample size needed to estimate a proportion within a given margin of error

for a specific confidence level, what planning value of p should be used when no estimate of p is

available? 0.50




A statistics teacher started class one day by drawing the names of 10 students out of a hat and

asked them to do as many pushups as they could. The 10 randomly selected students averaged 15

pushups per person with a standard deviation of 9 pushups. Suppose the distribution of the

, UCF QMB 3200 Final Exam Questions With
Answers 100% Correct
population of number of pushups that can be done is approximately normal. Which of the

following statements is true? A t distribution should be used because σ is unknown.




The z value for a 99% confidence interval estimation is 2.58




In an interval estimation for a proportion of a population, the critical value of z at 99%

confidence is 2.576.




In interval estimation, as the sample size becomes larger, the interval estimate becomes

narrower.In general, higher confidence levels provide larger confidence intervals.




One way to have high confidence and a small margin of error is to increase the sample

size.




From a population that is normally distributed, a sample of 30 elements is selected and the

standard deviation of the sample is computed. For the interval estimation of μ, the proper

distribution to use is the t distribution with 29 degrees of freedom.

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