The UNIVARIATE Procedure
- Overview
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Getting Started
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Syntax
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DetailsMissing ValuesRoundingDescriptive StatisticsCalculating the ModeCalculating PercentilesTests for LocationConfidence Limits for Parameters of the Normal DistributionRobust EstimatorsCreating Line Printer PlotsCreating High-Resolution GraphicsUsing the CLASS Statement to Create Comparative PlotsPositioning InsetsFormulas for Fitted Continuous DistributionsGoodness-of-Fit TestsKernel Density EstimatesConstruction of Quantile-Quantile and Probability PlotsInterpretation of Quantile-Quantile and Probability PlotsDistributions for Probability and Q-Q PlotsEstimating Shape Parameters Using Q-Q PlotsEstimating Location and Scale Parameters Using Q-Q PlotsEstimating Percentiles Using Q-Q PlotsInput Data SetsOUT= Output Data Set in the OUTPUT StatementOUTHISTOGRAM= Output Data SetOUTKERNEL= Output Data SetOUTTABLE= Output Data SetTables for Summary StatisticsODS Table NamesODS Tables for Fitted DistributionsODS GraphicsComputational Resources
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ExamplesComputing Descriptive Statistics for Multiple VariablesCalculating ModesIdentifying Extreme Observations and Extreme ValuesCreating a Frequency TableCreating Basic Summary PlotsAnalyzing a Data Set With a FREQ VariableSaving Summary Statistics in an OUT= Output Data SetSaving Percentiles in an Output Data SetComputing Confidence Limits for the Mean, Standard Deviation, and VarianceComputing Confidence Limits for Quantiles and PercentilesComputing Robust EstimatesTesting for LocationPerforming a Sign Test Using Paired DataCreating a HistogramCreating a One-Way Comparative HistogramCreating a Two-Way Comparative HistogramAdding Insets with Descriptive StatisticsBinning a HistogramAdding a Normal Curve to a HistogramAdding Fitted Normal Curves to a Comparative HistogramFitting a Beta CurveFitting Lognormal, Weibull, and Gamma CurvesComputing Kernel Density EstimatesFitting a Three-Parameter Lognormal CurveAnnotating a Folded Normal CurveCreating Lognormal Probability PlotsCreating a Histogram to Display Lognormal FitCreating a Normal Quantile PlotAdding a Distribution Reference LineInterpreting a Normal Quantile PlotEstimating Three Parameters from Lognormal Quantile PlotsEstimating Percentiles from Lognormal Quantile PlotsEstimating Parameters from Lognormal Quantile PlotsComparing Weibull Quantile PlotsCreating a Cumulative Distribution PlotCreating a P-P Plot
- References
Creating Line Printer Plots
When ODS Graphics is disabled, the PLOTS option in the PROC UNIVARIATE statement provides up to four diagnostic line printer plots to examine the data distribution. These plots are the stem-and-leaf plot or horizontal bar chart, the box plot, the normal probability plot, and the side-by-side box plots. If you specify the WEIGHT statement, PROC UNIVARIATE provides a weighted histogram, a weighted box plot based on the weighted quantiles, and a weighted normal probability plot.
Note that these plots are a legacy feature of the UNIVARIATE procedure in earlier versions of SAS. They predate the addition of the CDFPLOT, HISTOGRAM, PPPLOT, PROBPLOT, and QQPLOT statements, which provide high-resolution graphics displays. Also note that line printer plots requested with the PLOTS option are mainly intended for use with the ODS LISTING destination. See Example 4.5.
Stem-and-Leaf Plot
The first plot in the output is either a stem-and-leaf plot (Tukey 1977) or a horizontal bar chart. If any single interval contains more than 49 observations, the horizontal bar chart appears. Otherwise, the stem-and-leaf plot appears. The stem-and-leaf plot is like a horizontal bar chart in that both plots provide a method to visualize the overall distribution of the data. The stem-and-leaf plot provides more detail because each point in the plot represents an individual data value.
To change the number of stems that the plot displays, use PLOTSIZE= to increase or decrease the number of rows. Instructions that appear below the plot explain how to determine the values of the variable. If no instructions appear, you multiply Stem.Leaf by 1 to determine the values of the variable. For example, if the stem value is 10 and the leaf value is 1, then the variable value is approximately 10.1. For the stem-and-leaf plot, the procedure rounds a variable value to the nearest leaf. If the variable value is exactly halfway between two leaves, the value rounds to the nearest leaf with an even integer value. For example, a variable value of 3.15 has a stem value of 3 and a leaf value of 2.
Box Plot
The box plot, also known as a schematic box plot, appears beside the stem-and-leaf plot. Both plots use the same vertical scale. The box plot provides a visual summary of the data and identifies outliers. The bottom and top edges of the box correspond to the sample 25th (Q1) and 75th (Q3) percentiles. The box length is one interquartile range (Q3 – Q1). The center horizontal line with asterisk endpoints corresponds to the sample median. The central plus sign (+) corresponds to the sample mean. If the mean and median are equal, the plus sign falls on the line inside the box. The vertical lines that project out from the box, called whiskers, extend as far as the data extend, up to and including a distance of 1.5 interquartile ranges. Values farther away are potential outliers. The procedure identifies the extreme values with a zero or an asterisk (*). If zero appears, the value is between 1.5 and 3 interquartile ranges from the top or bottom edge of the box. If an asterisk appears, the value is more extreme.
Note: To produce box plots that use high-resolution graphics, use the BOXPLOT procedure in SAS/STAT software. See Chapter 28: The BOXPLOT Procedure in SAS/STAT 14.1 User's Guide.
Normal Probability Plot
The normal probability plot plots the empirical quantiles against the quantiles of a standard normal distribution. Asterisks
(*) indicate the data values. The plus signs (+) provide a straight reference line that is drawn by using the sample mean
and standard deviation. If the data are from a normal distribution, the asterisks tend to fall along the reference line. The
vertical coordinate is the data value, and the horizontal coordinate is 
where
![\[ \begin{array}{lcl} v_ i & = & \frac{r_ i -\frac{3}{8}}{n+\frac{1}{4}} \\ \Phi ^{-1}(\cdot ) & = & \mbox{inverse of the standard normal distribution function} \\ r_ i & = & \mbox{rank of the }i\mbox{th data value when ordered from smallest to largest} \\ n & = & \mbox{number of nonmissing observations} \\ \end{array} \]](images/procstat_univariate0270.png)
For a weighted normal probability plot, the ith ordered observation is plotted against where
![\[ \begin{array}{lcl} v_ i & = & \frac{(1-\frac{3}{8i})\sum _{j=1}^{i}w_{(j)}}{(1+\frac{1}{4n})\sum _{i=1}^{n}w_ i} \\ w_{(j)} & = & \mbox{weight associated with the }j\mbox{th ordered observation} \\ \end{array} \]](images/procstat_univariate0271.png)
When each observation has an identical weight, 
, the formula for 
reduces to the expression for in the unweighted normal probability plot:
![\[ v_ i = \frac{i-\frac{3}{8}}{n+\frac{1}{4}} \]](images/procstat_univariate0274.png)
When the value of VARDEF= is WDF or WEIGHT, a reference line with intercept 
and slope 
is added to the plot. When the value of VARDEF= is DF or N, the slope is 
where 
is the average weight.
When each observation has an identical weight and the value of VARDEF= is DF, N, or WEIGHT, the reference line reduces to
the usual reference line with intercept 
and slope in the unweighted normal probability plot.
If the data are normally distributed with mean 
and standard deviation 
, and each observation has an identical weight w, then the points on the plot should lie approximately on a straight line. The intercept for this line is . The slope is when VARDEF= is WDF or WEIGHT, and the slope is 
when VARDEF= is DF or N.
Note: To produce high-resolution probability plots, use the PROBPLOT statement in PROC UNIVARIATE; see the section PROBPLOT Statement.
Side-by-Side Box Plots
When you use a BY statement with the PLOTS option, PROC UNIVARIATE produces side-by-side box plots, one for each BY group. The box plots (also known as schematic plots) use a common scale that enables you to compare the data distribution across BY groups. This plot appears after the univariate analyses of all BY groups. Use the NOBYPLOT option to suppress this plot.
Note: To produce high-resolution side-by-side box plots, use the BOXPLOT procedure in SAS/STAT software. See Chapter 28: The BOXPLOT Procedure in SAS/STAT 14.1 User's Guide.