ผลต่างระหว่างรุ่นของ "Week11 practice 2"

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== Sample ==
 
== Sample ==
Suppose that you take <math>n</math> samples <math>X_1,X_2,\ldots,X_n</math> independently from the same distribution.
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Consider a certain distribution.  The mean <math>\mu</math> of the distribution is the expected value of a random variable <math>X</math> sample from the distribution.  I.e.,
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<center>
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<math>\mu=E[X]</math>.
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</center>
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Also recall that the variance of the distribution is
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<center>
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<math>\sigma^2=Var(X)=E[(X-\mu)^2]=E[X^2] = E[X]^2.</math>.
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</center>
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And finally, the standard deviation is <math>\sigma = \sqrt{Var(X)}</math>.
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Suppose that you take <math>n</math> samples <math>X_1,X_2,\ldots,X_n</math> independently from this distribution.  (Note that <math>X_1,X_2,\ldots,X_n</math> are random variables.
  
 
=== Sample means ===
 
=== Sample means ===
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is called a '''sample mean.'''
 
is called a '''sample mean.'''
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We can compute:
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<math>E[\bar{X}]= E\left[\frac{1}{n}\sum_{i=1}^n X_i\right] = \frac{1}{n}
  
 
=== Sample variances and sample standard deviations ===
 
=== Sample variances and sample standard deviations ===

รุ่นแก้ไขเมื่อ 01:34, 6 พฤศจิกายน 2557

Sample

Consider a certain distribution. The mean of the distribution is the expected value of a random variable sample from the distribution. I.e.,

.

Also recall that the variance of the distribution is

.

And finally, the standard deviation is .

Suppose that you take samples independently from this distribution. (Note that are random variables.

Sample means

The statistic

is called a sample mean.

We can compute:

<math>E[\bar{X}]= E\left[\frac{1}{n}\sum_{i=1}^n X_i\right] = \frac{1}{n}

Sample variances and sample standard deviations