QMB3200 Exam 1 Complete Questions with 100% Correct Answers

QMB3200 Exam 1 Complete Questions with 100% Correct Answers A random sample of 121 bottles of cologne showed an average content of 4 ounces. It is known that the standard deviation of the contents (i.e., of the population) is .22 ounces. In this problem, the value .22 ounces is: a parameter For a(n) _____ population, it is impossible to construct a sampling frame. infinite When drawing a sample from a population, the goal is for the sample to: match the targeted population A simple random sample of size n from an infinite population of size N is to be selected. Each possible sample should have: the same probability of being selected 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: a. N has a probability of .1 of being selected. b. n has a probability of .5 of being selected. c. n has the same probability of being selected. d. N has the same probability of being selected. c. n has the same probability of being selected. A simple random sample of size n from an infinite population is a sample selected such that: each element has a probability of at least .5 of being selected. The medical director of a company looks at the medical records of all 50 employees and finds that the mean systolic blood pressure for these employees is 126.07. The value of 126.07 is: a parameter parameter A characteristic or constant factor, limit Which of the following is a point estimator? a. μ b. p c. s d. σ c. s The value of the _____ ________ is used to estimate the value of the population parameter. sample statistics Sample statistics, such as x̅ , s, or p̅, that provide the point estimate of the population parameter are known as: point estimators The sample mean is the point estimator of: μ. The population we want to make inferences about is called the: target population The sample statistic characteristic s is the point estimator of: σ Which of these best describes a sampling distribution of a statistic? a. It is the probability that the sample statistic equals the parameter of interest. b. It is the probability distribution of the values of a statistic that are contained in all possible samples of the same sample size. c. It is the distribution of all of the statistics calculated from all possible samples of the same sample size. d. It is the histogram of sample statistics from all possible samples of the same sample size. c. It is the distribution of all of the statistics calculated from all possible samples of the same sample size. 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 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 how to get standard error Divide the standard deviation by the square root of the sample size (n). In a recent Gallup Poll, the decision was made to increase the size of its random sample of voters from 1500 people to about 4000 people. The purpose of this increase is to: reduce the standard error of the estimate. A sample of 92 observations is taken from an infinite population. The sampling distribution of is approximately: a. normal because is always approximately normally distributed. b. normal because the sample size is small in comparison to the population size. c. normal because of the central limit theorem. d. None of these alternatives is correct. c. normal because of the central limit theorem. Doubling the size of the sample will: reduce the standard error of the mean 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: central limit theorem A simple random sample of 100 observations was taken from a large population. The sample mean and the standard deviation were determined to be 80 and 12, respectively. The standard error of the mean is: 12/ sqr root 100 = 1.2 In computing the standard error of the mean, the finite population correction factor is used when: n/N>.05 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. As the sample size increases, the standard error of the mean ___________ decreases. 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. Which of the following statements regarding the sampling distribution of sample means is incorrect? a. The standard deviation of the sampling distribution is the standard deviation of the population. b. The sampling distribution is approximately normal when the population is normal or the sample size is sufficiently large. c. The mean of the sampling distribution is the mean of the population. d. 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. a. The standard deviation of the sampling distribution is the standard deviation of the population. The standard deviation of a point estimator is called the: standard error