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Sampling Distributions
Chapter Goals ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],_ _ _ _
Sampling Error ,[object Object],[object Object],[object Object],[object Object],[object Object]
Calculating Sampling Error ,[object Object],[object Object],[object Object],[object Object]
Review ,[object Object],where: μ = Population mean x = sample mean x i  = Values in the population or sample N = Population size n = sample size
Example If the population mean is  μ = 98.6  degrees and a sample of n = 5 temperatures yields a sample mean of  = 99.2   degrees, then the sampling error is
Sampling Errors ,[object Object],[object Object],[object Object],[object Object]
Sampling Distribution ,[object Object]
Developing a  Sampling Distribution ,[object Object],[object Object],[object Object],[object Object],A B C D
Developing a  Sampling Distribution .3 .2 .1 0 18  20  22  24 A  B  C  D Uniform Distribution P(x) x (continued) Summary Measures for the Population Distribution:
Now consider all possible samples of size n=2 16 possible samples (sampling with replacement) (continued) Developing a  Sampling Distribution 16 Sample Means
Sampling Distribution of All Sample Means 18  19  20  21  22  23  24 0  .1  .2  .3  P(x)   x Sample Means Distribution 16 Sample Means _ Developing a  Sampling Distribution (continued) (no longer uniform)
Summary Measures of this Sampling Distribution: Developing a Sampling Distribution (continued)
Comparing the Population with its Sampling Distribution 18  19  20  21  22  23  24 0  .1  .2  .3  P(x)   x 18   20   22   24 A  B  C  D 0  .1  .2  .3  Population N = 4 P(x)   x _ Sample Means Distribution n = 2
If the Population is Normal (THEOREM 6-1) If a population is  normal  with mean μ and standard deviation σ, the sampling distribution of  is  also normally distributed  with   and
z-value for Sampling Distribution of x ,[object Object],where: = sample mean = population mean = population standard deviation   n = sample size
Finite Population Correction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Sampling Distribution Properties ,[object Object],Normal Population Distribution Normal Sampling Distribution  (has the same mean)
Sampling Distribution Properties ,[object Object],[object Object],[object Object],Larger sample size Smaller sample size (continued)
If the Population is  not  Normal ,[object Object],[object Object],[object Object],[object Object],[object Object]
Central Limit Theorem n↑ As the sample size gets large enough…   the sampling distribution becomes almost normal regardless of shape of population
If the Population is  not  Normal Population Distribution Sampling Distribution  (becomes normal as n increases) Central Tendency Variation (Sampling with replacement) Larger sample size Smaller sample size (continued) Sampling distribution properties:
How Large is Large Enough? ,[object Object],[object Object],[object Object]
Example ,[object Object],[object Object]
Example ,[object Object],[object Object],[object Object],[object Object],[object Object],(continued)
Example ,[object Object],(continued) z 7.8  8.2 -0.4  0.4 Sampling Distribution Standard Normal Distribution .1554 +.1554 Population  Distribution ? ? ? ? ? ? ? ? ? ? ? ? Sample Standardize
Population Proportions, p ,[object Object],[object Object],[object Object],[object Object],[object Object]
Sampling Distribution of p ,[object Object],[object Object],[object Object],(where p = population proportion) Sampling Distribution P(   p   ) .3 .2 .1 0 0  . 2  .4  .6  8  1 p
z-Value for Proportions ,[object Object],Standardize p to a z value with the formula:
Example ,[object Object],i.e.:  if  p = .4  and  n = 200, what is   P(.40 ≤ p ≤ .45) ?
Example ,[object Object],[object Object],(continued) Find  :  Convert to standard normal:
Example ,[object Object],[object Object],z .45 1.44 .4251 Standardize Sampling Distribution Standardized  Normal Distribution (continued) Use standard normal table:  P(0 ≤ z ≤ 1.44) = .4251 .40 0 p

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Sampling distributions stat ppt @ bec doms

  • 2.
  • 3.
  • 4.
  • 5.
  • 6. Example If the population mean is μ = 98.6 degrees and a sample of n = 5 temperatures yields a sample mean of = 99.2 degrees, then the sampling error is
  • 7.
  • 8.
  • 9.
  • 10. Developing a Sampling Distribution .3 .2 .1 0 18 20 22 24 A B C D Uniform Distribution P(x) x (continued) Summary Measures for the Population Distribution:
  • 11. Now consider all possible samples of size n=2 16 possible samples (sampling with replacement) (continued) Developing a Sampling Distribution 16 Sample Means
  • 12. Sampling Distribution of All Sample Means 18 19 20 21 22 23 24 0 .1 .2 .3 P(x) x Sample Means Distribution 16 Sample Means _ Developing a Sampling Distribution (continued) (no longer uniform)
  • 13. Summary Measures of this Sampling Distribution: Developing a Sampling Distribution (continued)
  • 14. Comparing the Population with its Sampling Distribution 18 19 20 21 22 23 24 0 .1 .2 .3 P(x) x 18 20 22 24 A B C D 0 .1 .2 .3 Population N = 4 P(x) x _ Sample Means Distribution n = 2
  • 15. If the Population is Normal (THEOREM 6-1) If a population is normal with mean μ and standard deviation σ, the sampling distribution of is also normally distributed with and
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21. Central Limit Theorem n↑ As the sample size gets large enough… the sampling distribution becomes almost normal regardless of shape of population
  • 22. If the Population is not Normal Population Distribution Sampling Distribution (becomes normal as n increases) Central Tendency Variation (Sampling with replacement) Larger sample size Smaller sample size (continued) Sampling distribution properties:
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.