A. The sample are drawn from a normally distributed population.
B. The variance of the population is known
C. All above
D. s (sample standard deviation) is an unbiased estimate of the population variance.
Related Mcqs:
- If in a sampling distribution of X the sample size is 25, what assumption must hold for the sampling distribution of X to be normal ?
A. Population distribution is normal
B. μ1 = μx
C. Population distribution is uniform
D. σx = σx /√n - If the sampled population has a normal distribution, when is the sampling distribution of the sample mean X also a normal distribution ?
A. Only when the sample size is at least 100.
B. Only when the sample size is smaller than 30
C. Always
D. Only when the sample size is at least 30. - condition for applying Central Limit Theorem (CLT) which approximate the sampling distribution of the mean with a normal distribution is ?
A. n > 30
B. N(1 – p) > 5
C. 2 n 5
E. N < 30 - The mean of a distribution is 23, the median is 24, and the mode is 25.5. It is most likely that this distribution is_____________?
A. Positively Skewed
B. Symmetrical
C. Asymptotic
D. Negatively Skewed - A sampling distribution is the probability distribution for which one of the following___________?
A. A sample
B. A sample statistic
C. A population
D. A population parameter - For a binomial distribution with n = 15 as p changes from. 50 toward. 05 the distribution will__________?
A. Become more positively skewed
B. Become more negatively skewed
C. Become symmetrical
D. All of the above. - If mean of the binomial probability distribution is 4.8, then variance of this distribution is :_______________?
A. -2.3
B. 5.3
C. -4.8
D. 2.3 - A distribution is called _____ distribution if it has two modes?
A. Uni-modal
B. Bi-model
C. Tri-modal
D. None of these - Normal distribution is the distribution of _______________?
A. Discrete r.v.
B. Continuous r.v.
C. Qualitative variable
D. Quantitative variable - Normal distribution is _____ distribution?
A. Symmetrical
B. non-symmetrical
C. + vely skewed
D. -vely skewed
