The variability of a staistic is described by

the spread of its sampling distribution

The sampling distribution of a mean

is an imaginary collection of means for all random samples

If sample mean comes from a population that is ≈ Normal, the CLT states

the sample size can be less than 30

The sampling distribution of a statistic is

the distribution of all possible values of the statistic

According to the CLT, as n increases, the

standard deviation of the sampling distribution decreases

A statistic is an unbiased estimator of a parameter if

the parameter is at the center of the sampling distribution

What condition must be met in order to use the CLT for a mean?

The population is normal OR n ≥ 30

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Central Limit Theorem (Means)

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#Central Limit Theroem #Sampling distribution of means

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Questions 12

Answers

1
The variability of a staistic is described by
the spread of its sampling distribution
the different values of the statistic
the amount of bias present
the stability of the population
2
A distribution of sample means is approximately normal if
the population is skewed
the sample size is over 30
the population is more than 10 times the sample size
the samples were random
3
73% of the people who responded to a poll reported that they had debt. The number 73% is a(n)
sample
parameter
statistic
unbiased estimate
4
The sampling distribution of a mean
is an imaginary collection of means for all random samples
tells us nothing about the variation of a statistic
is unrelated to statistics
is the same as the distribution of the sample
5
If sample mean comes from a population that is ≈ Normal, the CLT states
the sampling distribution will be uniform
the sample size can be less than 30
the sampling distribution will be skewed
that n must be at least 30
6
The sampling distribution of a statistic is
the distribution of all possible values of the statistic
The probability we obtain the statistic in repeated samples
the distribution of a sample of a certain size
the extent to which results differ from the truth
7
A distribution of sample means is approximately normal if
the population is normal
the statistic is unbiased
the mean is greater than the standard deviation
n times p and n times 1-p are greater than 10
8
According to the CLT, as n increases, the
standard deviation of the sampling distribution decreases
standard deviation of the sampling distribution increases
mean of the sampling distribution increases.
mean of the sampling distribution decreases
9
A statistic is an unbiased estimator of a parameter if
the parameter is at the center of the sampling distribution
a large sample is taken
the sample size is at least 30
the parameter is randomly selected
10
What condition must be met in order to use the CLT for a mean?
The population must be normal
The population is normal OR n ≥ 30
The sample comes from a small population.
11
What is another name for a distribution of sample means?
Sampling Distribution
Approximate Distribution
Skewed Right
Normal Distribution
12
The CLT does not apply
for a proportion
to all skewed populations
to convenience samples.
to all small samples

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