
FREQUENCY TO RAWName:
Note that the maximum limit on the size of a Dataplot variables still applies. So if the sum of the counts array exceeds this limit, an error will be returned.
<SUBSET/EXPCEPT/FOR qualification> where <xval> is a variable that contains the numeric values; <count> is a variable that contains the counts corresponding to <xval>; <y> is the variable to contain the raw data; and where the <SUBSET/EXCEPT/FOR qualification> is optional.
READ xval freq 0 174 1 112 2 54 3 14 4 4 5 1 6 1 END OF DATA LET Y = FREQUENCY TO RAW XVAL FREQ HERMITE MAXIMUM LIKELIHOOD YThe following output is generated. HERMITE PARAMETER ESTIMATION: NUMBER OF OBSERVATIONS = 360 SAMPLE MINIMUM = 0.000000 SAMPLE MAXIMUM = 6.000000 SAMPLE MEAN = 0.8027778 SAMPLE VARIANCE = 0.9665661 METHOD OF MOMENTS ESTIMATE OF ALPHA = 0.4047077 ESTIMATE OF BETA = 1.578891 ESTIMATE OF A = 0.6389894 ESTIMATE OF B = 0.8189416E01 ESTIMATE OF VARIANCE OF A = 0.6960734E02 ESTIMATE OF VARIANCE OF B = 0.1518932E02 ESTIMATE OF COVARIANCE OF A AND B = 0.2587889E02 METHOD OF EVEN POINTS SUM OF EVEN FREQUENCIES = 233.0000 SUM OF ODD FREQUENCIES = 127.0000 ESTIMATE OF ALPHA = 0.4375446 ESTIMATE OF BETA = 1.397189 ESTIMATE OF A = 0.6113325 ESTIMATE OF B = 0.9572265E01 METHOD OF ZERO FREQUENCY AND MEAN ZERO FREQUENCY = 174.0000 ESTIMATE OF ALPHA = 0.3891761 ESTIMATE OF BETA = 1.673586 ESTIMATE OF A = 0.6513197 ESTIMATE OF B = 0.7572901E01 ESTIMATE OF VARIANCE OF A = 0.5608298E02 ESTIMATE OF VARIANCE OF B = 0.1160128E02 ESTIMATE OF COVARIANCE OF A AND B = 0.1899538E02 METHOD OF MAXIMUM LIKELIHOOD ESTIMATE OF ALPHA = 0.3950545 ESTIMATE OF BETA = 1.637014 ESTIMATE OF A = 0.6467096 ESTIMATE OF B = 0.7803404E01 ESTIMATE OF VARIANCE OF ALPHA = 0.7677542E02 ESTIMATE OF VARIANCE OF BETA = 0.2727334 ESTIMATE OF COVARIANCE OF ALPHA AND BETA = 0.4439158E01
Date created: 6/7/2004 