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Statistics  for Management Confidence Interval Estimation
Lesson Topics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Mean,   , is unknown Population Random Sample I am 95% confident that     is between 40 & 60. Mean  X = 50 Estimation Process Sample
Estimate Population Parameter... with Sample Statistic Mean  Proportion p p s Variance s 2 Population Parameters Estimated  2 Difference    -   1 2 x  -  x  1 2 X _ _ _
[object Object],[object Object],[object Object],[object Object],[object Object],Confidence Interval Estimation
Confidence Interval Sample Statistic Confidence Limit (Lower) Confidence Limit (Upper) A Probability That the Population Parameter Falls Somewhere Within the Interval. Elements of Confidence Interval Estimation
Parameter =  Statistic ± Its  Error © 1984-1994 T/Maker Co. Confidence Limits for Population Mean Error =   Error  = Error Error
90% Samples 95% Samples Confidence Intervals 99% Samples X _  x _
[object Object],[object Object],[object Object],[object Object],[object Object],Level of Confidence
Confidence Intervals  Intervals Extend from (1 -   ) % of Intervals Contain   .    % Do Not. 1 -   /2  /2 X _  x _ Intervals &  Level of Confidence Sampling Distribution of the Mean to
[object Object],[object Object],[object Object],[object Object],Intervals Extend from © 1984-1994 T/Maker Co. Factors Affecting Interval Width X  -  Z    to  X  +  Z      x x
Mean  Unknown Confidence Intervals Proportion Finite Population  Known Confidence Interval Estimates
[object Object],[object Object],[object Object],[object Object],[object Object],Confidence Intervals (  Known)
Mean  Unknown Confidence Intervals Proportion Finite Population  Known Confidence Interval Estimates
[object Object],[object Object],[object Object],[object Object],[object Object],Confidence Intervals (  Unknown)
Z t 0 t  ( df  = 5) Standard Normal t  ( df  = 13) Bell-Shaped Symmetric ‘ Fatter’ Tails Student’s  t   Distribution
[object Object],[object Object],[object Object],[object Object],[object Object],degrees of freedom =  n  -1  = 3 -1 = 2 Degrees of Freedom ( df )
Upper Tail Area df .25 .10 .05 1 1.000 3.078 6.314 2 0.817 1.886 2.920 3 0.765 1.638 2.353 t 0 Assume: n = 3  df =  n  - 1 = 2     = .10    /2 =.05 2.920 t  Values    / 2 .05 Student’s   t  Table
[object Object],[object Object],   . . 46 69 53 30 Example: Interval Estimation  Unknown
Mean  Unknown Confidence Intervals Proportion Finite Population  Known Confidence Interval Estimates
[object Object],[object Object],[object Object],[object Object],[object Object],X    Estimation for Finite Populations
Mean  Unknown Confidence Intervals Proportion Finite Population  Known Confidence Interval Estimates
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Confidence Interval Estimate Proportion
[object Object],p   .053 .107 Example: Estimating Proportion
Sample Size ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],n Z Error     2 2 2 2 2 2 1 645 45 5 219 2 220  . . Example: Sample Size  for Mean Round Up
[object Object],Example: Sample Size  for Proportion Round Up 228 
[object Object],Example: Sample Size  for Mean Using fpc Round Up where 153 
Lesson Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Lesson04_Static11

  • 1. Statistics for Management Confidence Interval Estimation
  • 2.
  • 3. Mean,  , is unknown Population Random Sample I am 95% confident that  is between 40 & 60. Mean X = 50 Estimation Process Sample
  • 4. Estimate Population Parameter... with Sample Statistic Mean  Proportion p p s Variance s 2 Population Parameters Estimated  2 Difference  -  1 2 x - x 1 2 X _ _ _
  • 5.
  • 6. Confidence Interval Sample Statistic Confidence Limit (Lower) Confidence Limit (Upper) A Probability That the Population Parameter Falls Somewhere Within the Interval. Elements of Confidence Interval Estimation
  • 7. Parameter = Statistic ± Its Error © 1984-1994 T/Maker Co. Confidence Limits for Population Mean Error = Error = Error Error
  • 8. 90% Samples 95% Samples Confidence Intervals 99% Samples X _  x _
  • 9.
  • 10. Confidence Intervals Intervals Extend from (1 -  ) % of Intervals Contain  .   % Do Not. 1 -   /2  /2 X _  x _ Intervals & Level of Confidence Sampling Distribution of the Mean to
  • 11.
  • 12. Mean  Unknown Confidence Intervals Proportion Finite Population  Known Confidence Interval Estimates
  • 13.
  • 14. Mean  Unknown Confidence Intervals Proportion Finite Population  Known Confidence Interval Estimates
  • 15.
  • 16. Z t 0 t ( df = 5) Standard Normal t ( df = 13) Bell-Shaped Symmetric ‘ Fatter’ Tails Student’s t Distribution
  • 17.
  • 18. Upper Tail Area df .25 .10 .05 1 1.000 3.078 6.314 2 0.817 1.886 2.920 3 0.765 1.638 2.353 t 0 Assume: n = 3 df = n - 1 = 2   = .10  /2 =.05 2.920 t Values  / 2 .05 Student’s t Table
  • 19.
  • 20. Mean  Unknown Confidence Intervals Proportion Finite Population  Known Confidence Interval Estimates
  • 21.
  • 22. Mean  Unknown Confidence Intervals Proportion Finite Population  Known Confidence Interval Estimates
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.