Example 4.1: Fitting a Beta Curve

HISTOGRAM Statement

Example 4.1: Fitting a Beta Curve

See CAPBTA2 in the SAS/QC Sample Library

You can use a beta distribution to model the distribution of a quantity that is known to vary between lower and upper bounds. In this example, a manufacturing company uses a robotic arm to attach hinges on metal sheets. The attachment point should be offset 10.1 mm from the left edge of the sheet. The actual offset varies between 10.0 and 10.5 mm due to variation in the arm. Offsets for 50 attachment points are saved in the following data set:

   data measures;
      input length @@;
      label length = 'Attachment Point Offset in mm';
      datalines;
   10.147  10.070  10.032  10.042  10.102
   10.034  10.143  10.278  10.114  10.127
   10.122  10.018  10.271  10.293  10.136
   10.240  10.205  10.186  10.186  10.080
   10.158  10.114  10.018  10.201  10.065
   10.061  10.133  10.153  10.201  10.109
   10.122  10.139  10.090  10.136  10.066
   10.074  10.175  10.052  10.059  10.077
   10.211  10.122  10.031  10.322  10.187
   10.094  10.067  10.094  10.051  10.174
   ;

The following statements create a histogram with a fitted beta density curve:

   title 'Fitted Beta Distribution of Offsets';
   legend1 frame cframe=ligr cborder=black position=center;
   proc capability data=measures noprint;
      specs usl=10.25 lusl=20 cusl=salmon clsl=salmon
            cright=yellow pright=solid;
      histogram length /
         beta(theta=10 scale=0.5 color=blue fill)
         cfill     = ywh
         HREF=10
         hreflabel = 'Lower Bound'
         lHREF=2
         vaxis     = axis1
         cframe    = ligr
         legend    = legend1;
      axis1 label=(a=90 r=0);
      inset n = 'Sample Size'
            beta(pchisq = 'P-Value') / pos=ne  cfill = blank;
   run;

The histogram is shown in Output 4.1.1. The THETA= beta-option specifies the lower threshold. The SCALE= beta-option specifies the range between the lower threshold and the upper threshold (in this case, 0.5 mm). Note that in general, the default THETA= and SCALE= values are zero and one, respectively.

Output 4.1.1: Superimposing a Histogram with a Fitted Beta Curve

The FILL beta-option specifies that the area under the curve is to be filled with the CFILL= color. (If FILL were omitted, the CFILL= color would be used to fill the histogram bars instead.) The CRIGHT= option in the SPEC statement specifies the color under the curve to the right of the upper specification limit. If the CRIGHT= option were not specified, the entire area under the curve would be filled with the CFILL= color. When a lower specification limit is available, you can use the CLEFT= option in the SPEC statement to specify the color under the curve to the left of this limit.

The HREF=option draws a reference line at the lower bound, and the HREFLABEL= option adds the label Lower Bound. The option LHREF=2 specifies a dashed line type. The INSET statement adds an inset with the sample size and the p-value for a chi-square goodness-of-fit test.

In addition to displaying the beta curve, the BETA option summarizes the curve fit, as shown in Output 4.1.2. The output tabulates the parameters for the curve, the chi-square goodness-of-fit test whose p-value is shown in Output 4.1.1, the observed and estimated percents above the upper specification limit, and the observed and estimated quantiles. For instance, based on the beta model, the percent of offsets greater than the upper specification limit is 6.6%. For computational details, see "Formulas for Fitted Curves".

Output 4.1.2: Summary of Fitted Beta Distribution

Fitted Beta Distribution of Offsets

The CAPABILITY Procedure

Fitted Beta Distribution for length

Parameters for Beta Distribution
Parameter	Symbol	Estimate
Threshold	Theta	10
Scale	Sigma	0.5
Shape	Alpha	2.06832
Shape	Beta	6.022479
Mean		10.12782
Std Dev		0.072339

Goodness-of-Fit Tests for Beta Distribution
Test	Statistic		DF	p Value
Chi-Square	Chi-Sq	1.02463588	3	Pr > Chi-Sq	0.795

Quantiles for Beta Distribution
Percent	Quantile
Percent	Observed	Estimated
1.0	10.0180	10.0124
5.0	10.0310	10.0285
10.0	10.0380	10.0416
25.0	10.0670	10.0718
50.0	10.1220	10.1174
75.0	10.1750	10.1735
90.0	10.2255	10.2292
95.0	10.2780	10.2630
99.0	10.3220	10.3237

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