clt.hlp.htm

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help for clt
Central limit theorem demonstration
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The programs clt is designed to illustrate 
the Central Limit Theorem.  

clt allows you to take a given number of samples of size n
from a given distribution and show the distribution of sample means. 

After you invoke clt, a dialogue box is shown allowing you to
make the following choices regarding how the data is sampled.

Distribution Type
  Normal      - A normal distribution.
  Log normal  - Log of a normal distribution.
  Exp normal  - Exponential of a normal distribution.
  Exponential - Exponential of a uniform distribution
  Bimodal     - Two normal populations with means 3 units apart
                that are added together
  Binomial    - A 0/1 variable. If you choose this, you can use the
                If Binomial, P= pulldown to choose the probability
                of a 1 occurring.
  Uniform     - A uniform distribution.

If Binomial, P= 
  If you draw from a binomial distribution, you can use the p() 
  option to specify the probability of success. The default is .5 .

N per sample
  Allows you to choose the sample size, default is 1.

# of Samples
  Allows you to choose how many samples are drawn, default is 1000.

You can also make the following choices regarding how the 
graph of the sampling distribution is displayed.

Show Normal Overlay
  Includes a normal overlay on the histogram.

Draw Lines as +-1SD and +-2SD
  Draws lines at 
    xbar - 2sd  
    xbar - 1sd
    xbar
    xbar + 1sd
    xbar + 2sd

Show means as Z scores
  Shows the X axis as Z scores, helping you to see
  skewness in the data.

Show sums instead of means
  Shows the values on the X axis as the sum of the scores
  in the sample (rather than the mean).  This is useful
  when the distribution is binomial.  

After you have made your selections, you can click the
show button and the results are calculated and displayed.
If you choose a large number of samples (say over 3000), then
there may be a delay while the samples are drawn.

When you want to exit the program, click on the done button.


Author
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Statistical Consulting Group
Institute for Digital Education and Research, UCLA
idrestat@ucla.edu