Exam 2 - Central Limit Theorem Flashcards Preview

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Flashcards in Exam 2 - Central Limit Theorem Deck (12):
0

Sampling distribution of x-bar is the distribution of

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

1

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

Population mean, mu

2

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

Sigma over square root of n

3

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

Normal

4

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

Approximately normal when n is large

5

Central Limit Theorem:

If......
Then...,,

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

6

As n increases shape gets more

Normal

7

n is considered to be large if it is

Bigger than 30, n>30

8

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

Standard normal table

9

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

True

10

How to predict sampling distribution of x- bar in statistical practice?
1. Take
2. Use sample

1. Take only 1 sample of size n
2. Use sample results to make inference about population

11

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

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)