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- Multiple ChoicePlease save your changes before editing any questions.
Which theorem states that the larger the sample size, the closer the sample mean will be to the mean of the population?
Central limit theorem
Law of averages
Law of large numbers.
Chebyshev's inequality
- Multiple ChoicePlease save your changes before editing any questions.
Considering the set of observations, the percentage of values that lies within two standard deviations of the population mean is
68%
55%
95%
99.7%
- Multiple ChoicePlease save your changes before editing any questions.
The abbreviation of i.i.d stands for
independent and identically distributed
identically and independently distributed
both
none of these
- Multiple ChoicePlease save your changes before editing any questions.
In central limit theorem, the random variables are assumed to be
independent
identical
with same means and same variances
all the above
- Multiple ChoicePlease save your changes before editing any questions.
If Xn converges to 'a' in probability as the sample size increases, then which of these statements are true
(i) for any ε>0, P{|Xn-a|>ε}→1 as n→∞
(ii) for any ε>0, P{|Xn-a|<ε}→0 as n→∞
(i) only
(ii) only
both (i) and (ii)
neither (i) nor (ii)
- Multiple ChoicePlease save your changes before editing any questions.
The Central Limit Theorem says that the sampling distribution of the sample mean is approximately normal if
all possible samples are selected
the sample size is large
the standard error of the sampling distribution is small
the sample size is small
- Multiple ChoicePlease save your changes before editing any questions.
The Central Limit Theorem says that the mean of the sampling distribution of the sample means is
equal to the population mean divided by the square root of the sample size
close to the population mean if the sample size is large
exactly equal to the population mean
close to the population mean divided by the square root of the sample size
- Multiple ChoicePlease save your changes before editing any questions.
The Central Limit Theorem says that the standard deviation of the sampling distribution of the sample means is
equal to the population standard deviation divided by the square root of the sample size
close to the population standard deviation if the sample size is large
exactly equal to the population standard deviation
close to the population standard deviation divided by the square root of the sample size
- Multiple ChoicePlease save your changes before editing any questions.
Samples of size 25 are selected from a population with mean 40 and standard deviation 7.5. The mean of the sampling distribution of sample means is
7.5
8
40
25
- Multiple ChoicePlease save your changes before editing any questions.
Samples of size 25 are selected from a population with mean 40 and standard deviation 7.5. The standard error of the sampling distribution of sample means is
0.3
1.5
7.5
1.6