
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 2sided confidence interval for the proportion of successes is:
with BINPPF denoting the binomial percent point function. 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
SKIP 25 SERIAL READ CLEAR.DAT Y SET WRITE DECIMALS 4 ANOP LIMITS 1.5 2.0 PROPORTION CONFIDENCE LIMITS YThis command generated the following output: TwoSided Confidence Limits for a Proportion Response Variable: Y Sample 1: Number of Observations: 325 Number of Successes: 87 Proportion of Successes: 0.2677  Confidence Lower Upper Value (%) Limit Limit  50.000 0.2523 0.2831 75.000 0.2400 0.2954 90.000 0.2277 0.3077 95.000 0.2215 0.3169 99.000 0.2062 0.3323 99.900 0.1908 0.3508 99.990 0.1754 0.3662 99.999 0.1631 0.3785  
Privacy
Policy/Security Notice
NIST is an agency of the U.S. Commerce Department.
Date created: 06/05/2001 