# DS Chapter 7 and Clickers

### 42 terms by ms_bcorbin

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### sampling distribution of the sample means

The probability distribution of the population of all possible sample means that could be obtained from all possible samples of the sample size.

### sampling distribution assumption

has a normal distribution if the sampled population has a normal distribution; has the same mean μ, denoted with "x bar"; has a standard deviation of: σ ÷ (√n)

### Central Limit Theorem

If the sampled population is N( μ , σ ) , then the population of the sampling distribution of the sample means is N(μ, [σ ÷ √n]) regardless of sample size

### if n is sufficiently large...

No matter what the distribution of the sampling population is, if the sample size n is sufficently large (n ≥ 30) , then the population of all possible sample means is approximately normally distributed. The larger the sample size n is, the more nearly normally distributed is the population of all possible sample means.

### when to use sampling distribution

asks for mean ____ (ie mean payment time)

### Sampling Distribution of the sample proportion

The proportion of all possible sample proportions is approximately normally
distributed with mean "μ denoted with p" = p and standard deviation "σ denoted with p" = √(pq ÷ n) if (np ≥ 5 and n(1-p) ≥ 5).

### "p hat"

probability ÷ sample size, then solve for z-value

### Stratified Random Sampling

First the population is divided into nonoverlapping groups of similar elements (people, objects, etc.) then a random sample is selected from each stratum, and these samples are combined to form the full sample.

### Systematic Sampling

Systematically select a sample of n elements without replacement from a frame of N elements. We ÷ N by n & round the result down to the nearest whole number. Let the rounded result l , we then randomly select one element from the first l elements in the frame. This is the first element in the systematic sample. The remaining elements in the sample are obtained by selecting every l the element following the first element.

### sampling error

not a mistake, just a variation due to sampling

true

true

true

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false

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false

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means

### Consider two population distributions labeled A and B. Distribution A is highly skewed and non-normal, while the distribution B is slightly skewed and near normal. In order for the sampling distributions of A and B to achieve the same degree of normality:

population A will require a larger sample size

### As the sample size ______ the variation of the sampling distribution of "x bar" ______.

increases, decreases

48 and 4

(check notebook)

6.68%

### Whenever the population has a normal distribution, the sampling distribution of "x bar" is normal or near normal distribution:

for any sample size

### If a population distribution is known to be normal, then it follows that:

"none of the above"

### For non-normal populations, as the sample size (n) ____, the distribution of sample means approaches a(n) _____ distribution.

increases, normal

.1732

5.48%

.0668

.02145

.9332

true

false

### 31The population of all sample proportions has a normal distribution if the sample size (n) is sufficiently large. The rule of thumb for ensuring that n is sufficiently large is:

np ≥ 5 and n(1-p) ≥ 5

.03

.9207

.6808

98.44%

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