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An introduction to the concept of sampling distributions and estimators in statistics. It explains that a sample is a subset of a larger population and that statistics, such as mean and variance, can be calculated from a sample. The document also discusses the importance of sampling distributions in determining how close sample statistics are to the true population values. It includes exercises for identifying population, sample, and estimator, as well as calculating sample statistics and confidence intervals.
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What’s variability got to do with it?
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Press “YES” if the last digit of your ID is even (0 2 4 6 8)(0,2,4,6,8) Pres “NO” if the last digit of your ID is odd (1,3,5,7,9)
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Order the choices to match the
terms:
a. Population b. Sample proportion c. Sample d. Population proportion
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Samples are different, so proportions are different
How “close” is sample mean to truth?
Sampling Distribution will tell us!
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Simple definition: a statistic is something you calculate from a sample
Hard definition: a statistic is a real-valued function of the samplefunction of the sample mean -- standard deviation -- the largest value
Each statistic has a distribution what are the possible values? how often do we expect those values to occur?
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X X X
μ
Population
1 2 3
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X X
X X
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p^ ˆ p ˆ p ˆ
p
Population
(^1 ) 3
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p p^ ˆ
p p
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(1 ) ˆ ,
p p p N p n
X N
⎛ (^) − ⎞ ⎜⎜ ⎟⎟ ⎝ ⎠
⎛ ⎞
∼
X N , n
⎛ ⎞ ⎜ ⎟ ⎝ ⎠
∼
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A (1- α)% confidence interval for μ is given by:
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Has CEO pay increased?
Sample data Mean percentage increase = 6.9% Standard deviation of increase = 55%Standard deviation of increase = 55% N = 104
95% confidence interval
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μ
μ
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