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PORPORTION CONFIDENCE LIMITSName:
In Dataplot, you define a success by entering the command
before entering the PROPORTION CONFIDENCE LIMITS command. That is, you specify the lower and upper values that define a success. Then the estimate for the proportion of successes is simply the number of points in the success region divided by the total number of points. Note that in many programs you would simply enter your data as a series of 0's and 1's where one of these defines a success and the other defines a failure. If your data is already in this format, simply define appropiate limits (e.g., ANOP LIMITS 0.5 1.5). If there are P successes in N observations and the significance level is alpha (e.g., 0.05), then the 2-sided confidence interval for the proportion of successes is:
Dataplot computes this inverval for a number of different probability levels.
<SUBSET/EXCEPT/FOR qualification> where <y> is the response variable; and where the <SUBSET/EXCEPT/FOR qualification> is optional.
PROPORTION CONFIDENCE LIMITS Y
ANOP LIMITS 0.80 1.0
SERIAL READ CLEAR.DAT Y ANOP LIMITS 1.5 2.0 PROPORTION CONFIDENCE LIMITS Y This command generated the following output:
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** PROPORTION CONFIDENCE LIMITS Y **
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CONFIDENCE LIMITS FOR A PROPORTION
(2-SIDED)
NUMBER OF OBSERVATIONS = 325
NUMBER OF SUCCESSES = 87
PORPORTION OF SUCCESS = .2676923
CONFIDENCE LOWER UPPER
VALUE (%) LIMIT LIMIT
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50.000 .252308 .283077
75.000 .240000 .295385
90.000 .227692 .307692
95.000 .221538 .316923
99.000 .206154 .332308
99.900 .190769 .350769
99.990 .175385 .366154
99.999 .163077 .378462
Date created: 6/5/2001 |