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 Getting Started

## Statistics

Finally, the transaction flows to a Bucket, which collects transactions and can also save the value of transaction attributes in a SAS data set.

Figure 1.7: A Bucket Control Panel

From the bucket control panel (Figure 1.7) you can set the size of the transaction collection buffer and name the transaction attribute to accumulate. You can also name a SAS data set into which to collect the transaction attribute values.

 Distribution Function Estimate

 The UNIVARIATE Procedure Variable: value

 name=age

 Moments N 1803 Sum Weights 1803 Mean 24.5093328 Sum Observations 44190.3271 Std Deviation 14.8024337 Variance 219.112044 Skewness -0.1060516 Kurtosis -1.2191446 Uncorrected SS 1477915.34 Corrected SS 394839.903 Coeff Variation 60.3950903 Std Error Mean 0.34860632

 Basic Statistical Measures Location Variability Mean 24.50933 Std Deviation 14.80243 Median 26.21585 Variance 219.11204 Mode . Range 53.62360 Interquartile Range 26.10309

 Tests for Location: Mu0=0 Test Statistic p Value Student's t t 70.30662 Pr > |t| <.0001 Sign M 901.5 Pr >= |M| <.0001 Signed Rank S 813153 Pr >= |S| <.0001

 Quantiles (Definition 5) Quantile Estimate 100% Max 53.62741148 99% 50.95981599 95% 46.44804080 90% 43.36915659 75% Q3 37.45295397 50% Median 26.21585009 25% Q1 11.34985903 10% 3.08194661 5% 1.38985610 1% 0.28776996 0% Min 0.00381383

 Extreme Observations Lowest Highest Value Obs Value Obs 0.00381383 1442 51.9481 819 0.02449773 1486 52.1543 818 0.02846473 1458 52.1938 418 0.04001101 1249 52.4247 419 0.06573967 1700 53.6274 817

Figure 1.8: Statistics on a Transaction Attribute

If you collect data into a SAS data set and then press the Analyze button, univariate statistics will be calculated by the UNIVARIATE procedure (Figure 1.8) and a sample distribution function as shown in Figure 1.9 will be plotted.

Figure 1.9: Sample Distribution Function

See Chapter 8, "Analyzing the Sample Path," for more information on on collecting statistics and analyzing simulation data.

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