Exam 2 - Central Limit Theorem Flashcards

0
Q

Center : the mean of the sampling distribution of x-bar equals the

A

Population mean, mu

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1
Q

Sampling distribution of x-bar is the distribution of

A

Values taken by x- bar from all possible samples of the same size from the same population

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2
Q

Spread: the standard deviation of the sampling distribution of x- bar equals

A

Sigma over square root of n

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3
Q

Shape: population normal - the shape of the sampling distribution of x- bar is

A

Normal

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4
Q

Shape: population non-normal- the shape of the sampling distribution of x-bars is

A

Approximately normal when n is large

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5
Q

Central Limit Theorem:

If……
Then…,,

A

If you take a large SRS of size n from any population
Then
The sampling distribution of x- bar is approximately Normal

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6
Q

As n increases shape gets more

A

Normal

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7
Q

n is considered to be large if it is

A

Bigger than 30, n>30

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8
Q

CLT allows us to use …….. ….. ……to compute approximate probabilities on x-bar

A

Standard normal table

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9
Q

T/F Increased sample size does not affect the shape of the population, only the shape of sampling distribution.

A

True

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10
Q

How to predict sampling distribution of x- bar in statistical practice?

  1. Take
  2. Use sample
A
  1. Take only 1 sample of size n

2. Use sample results to make inference about population

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11
Q

What 2 facts allow us do predictions without creating sampling distribution of x- bar?

A
  1. Mean = mu and standard deviation of x- bar = sigma over square root of n
  2. Shape is approx normal if the sample size is large (CLT)
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