QMB 3200 EXAM 1 UCF Chapter 7 Questions & Answers 100% Correct!

The central limit theorem is important in Statistics because it - ANSWERSenables 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 fact that the sampling distribution of sample means can be approximated by a normal probability distribution whenever the sample size becomes large is based on the: - ANSWERScentral limit theorem. Sample statistics, such as x̅ , s, or p̅, that provide the point estimate of the population parameter are known as: - ANSWERSPoint Estimators As a rule of thumb, the sampling distribution of the sample proportion can be approximated by a normal probability distribution when: - ANSWERSn(1 - p) ≥ 5 and np ≥ 5. Which of the following statements regarding the sampling distribution of sample means is incorrect? The mean of the sampling distribution is the mean of the population. The sampling distribution is approximately normal when the population is normal or the sample size is sufficiently large. The sampling distribution is found by taking repeated samples of the same size from the population of interest and computing the mean of each sample. The standard deviation of the sampling distribution is the standard deviation of the population. - ANSWERSThe standard deviation of the sampling distribution is the standard deviation of the population. As the sample size increases, the: - ANSWERSstandard error of the mean decreases The sample mean is the point estimator of: - ANSWERSμ Which of the following is a point estimator - ANSWERSs A simple random sample of size n from a finite population of size N is a sample selected such that each possible sample of size: - ANSWERSn has the same probability of being selected Doubling the size of the sample will: