The UNIVARIATE Procedure

 
OUTTABLE= Output Data Set

The OUTTABLE= data set saves univariate statistics in a data set that contains one observation per analysis variable. The following variables are saved:

Table 4.122 Variables in the OUTTABLE= Data Set

Variable

Description

_CSS_

corrected sum of squares

_CV_

coefficient of variation

_GINI_

Gini’s mean difference

_KURT_

kurtosis

_MAD_

median absolute difference about the median

_MAX_

maximum

_MEAN_

mean

_MEDIAN_

median

_MIN_

minimum

_MODE_

mode

_MSIGN_

sign statistic

_NMISS_

number of missing observations

_NOBS_

number of nonmissing observations

_NORMAL_

test statistic for normality

_P1_

st percentile

_P5_

th percentile

_P10_

th percentile

_P90_

th percentile

_P95_

th percentile

_P99_

th percentile

_PROBM_

p-value of sign statistic

_PROBN_

p-value of test for normality

_PROBS_

p-value of signed rank test

_PROBT_

p-value of t statistic

_Q1_

th percentile (lower quartile)

_Q3_

th percentile (upper quartile)

_QN_

_QRANGE_

interquartile range (upper quartile minus lower quartile)

_RANGE_

range

_SGNRNK_

centered sign rank

_SKEW_

skewness

_SN_

(see Robust Estimates of Scale)

_STD_

standard deviation

_STDGINI_

Gini’s standard deviation

_STDMAD_

MAD standard deviation

_STDMEAN_

standard error of the mean

_STDQN_

standard deviation

_STDQRANGE_

interquartile range standard deviation

_STDSN_

standard deviation

_SUMWGT_

sum of the weights

_SUM_

sum

_T_

Student’s t statistic

_USS_

uncorrected sum of squares

_VARI_

variance

_VAR_

variable name

The OUTTABLE= data set and the OUT= data set see the section OUT= Output Data Set in the OUTPUT Statement) contain essentially the same information. in the OUT= data set (see the section OUT= Output Data Set in the OUTPUT Statement). However, the structure of the OUTTABLE= data set may be more appropriate when you are computing summary statistics for more than one analysis variable in the same invocation of the UNIVARIATE procedure. Each observation in the OUTTABLE= data set corresponds to a different analysis variable, and the variables in the data set correspond to summary statistics and indices.

For example, suppose you have 10 analysis variables (P1-P10). The following statements create an OUTTABLE= data set named Table, which contains summary statistics for each of these variables:

data Analysis;
   input A1-A10;
   datalines;
 72  223  332  138  110  145   23  293  353  458
 97   54   61  196  275  171  117   72   81  141
 56  170  140  400  371   72   60   20  484  138
124    6  332  493  214   43  125   55  372   30
152  236  222   76  187  126  192  334  109  546
  5  260  194  277  176   96  109  184  240  261
161  253  153  300   37  156  282  293  451  299
128  121  254  297  363  132  209  257  429  295
116  152  331   27  442  103   80  393  383   94
 43  178  278  159   25  180  253  333   51  225
 34  128  182  415  524  112   13  186  145  131
142  236  234  255  211   80  281  135  179   11
108  215  335   66  254  196  190  363  226  379
 62  232  219  474   31  139   15   56  429  298
177  218  275  171  457  146  163   18  155  129
  0  235   83  239  398   99  226  389  498   18
147  199  324  258  504    2  218  295  422  287
 39  161  156  198  214   58  238   19  231  548
120   42  372  420  232  112  157   79  197  166
178   83  238  492  463   68   46  386   45   81
161  267  372  296  501   96   11  288  330   74
 14    2   52   81  169   63  194  161  173   54
 22  181   92  272  417   94  188  180  367  342
 55  248  214  422  133  193  144  318  271  479
 56   83  169   30  379    5  296  320  396  597
;
proc univariate data=Analysis outtable=Table noprint;
  var A1-A10;
run;

The following statements create the table shown in Figure 4.15, which contains the mean, standard deviation, and so on, for each analysis variable:

proc print data=Table label noobs;
   var _VAR_ _MIN_ _MEAN_ _MAX_ _STD_;
   label _VAR_='Analysis';
run;

Figure 4.15 Tabulating Results for Multiple Process Variables
Test Scores for a College Course

Analysis Minimum Mean Maximum Standard Deviation
A1 0 90.76 178 57.024
A2 2 167.32 267 81.628
A3 52 224.56 372 96.525
A4 27 258.08 493 145.218
A5 25 283.48 524 157.033
A6 2 107.48 196 52.437
A7 11 153.20 296 90.031
A8 18 217.08 393 130.031
A9 45 280.68 498 140.943
A10 11 243.24 597 178.799