The UNIVARIATE Procedure
- Overview
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Getting Started
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Syntax
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Details
Missing Values Rounding Descriptive Statistics Calculating the Mode Calculating Percentiles Tests for Location Confidence Limits for Parameters of the Normal Distribution Robust Estimators Creating Line Printer Plots Creating High-Resolution Graphics Using the CLASS Statement to Create Comparative Plots Positioning Insets Formulas for Fitted Continuous Distributions Goodness-of-Fit Tests Kernel Density Estimates Construction of Quantile-Quantile and Probability Plots Interpretation of Quantile-Quantile and Probability Plots Distributions for Probability and Q-Q Plots Estimating Shape Parameters Using Q-Q Plots Estimating Location and Scale Parameters Using Q-Q Plots Estimating Percentiles Using Q-Q Plots Input Data Sets OUT= Output Data Set in the OUTPUT Statement OUTHISTOGRAM= Output Data Set OUTKERNEL= Output Data Set OUTTABLE= Output Data Set Tables for Summary Statistics ODS Table Names ODS Tables for Fitted Distributions ODS Graphics Computational Resources
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Examples
Computing Descriptive Statistics for Multiple Variables Calculating Modes Identifying Extreme Observations and Extreme Values Creating a Frequency Table Creating Plots for Line Printer Output Analyzing a Data Set With a FREQ Variable Saving Summary Statistics in an OUT= Output Data Set Saving Percentiles in an Output Data Set Computing Confidence Limits for the Mean, Standard Deviation, and Variance Computing Confidence Limits for Quantiles and Percentiles Computing Robust Estimates Testing for Location Performing a Sign Test Using Paired Data Creating a Histogram Creating a One-Way Comparative Histogram Creating a Two-Way Comparative Histogram Adding Insets with Descriptive Statistics Binning a Histogram Adding a Normal Curve to a Histogram Adding Fitted Normal Curves to a Comparative Histogram Fitting a Beta Curve Fitting Lognormal, Weibull, and Gamma Curves Computing Kernel Density Estimates Fitting a Three-Parameter Lognormal Curve Annotating a Folded Normal Curve Creating Lognormal Probability Plots Creating a Histogram to Display Lognormal Fit Creating a Normal Quantile Plot Adding a Distribution Reference Line Interpreting a Normal Quantile Plot Estimating Three Parameters from Lognormal Quantile Plots Estimating Percentiles from Lognormal Quantile Plots Estimating Parameters from Lognormal Quantile Plots Comparing Weibull Quantile Plots Creating a Cumulative Distribution Plot Creating a P-P Plot
- References
Example 4.5 Creating Plots for Line Printer Output
The PLOT option in the PROC UNIVARIATE statement requests several basic plots for display in line printer output. For more information about plots created by the PLOT option, see the section Creating Line Printer Plots. This example illustrates the use of the PLOT option as well as BY processing in PROC UNIVARIATE.
A researcher is analyzing a data set consisting of air pollution data from three different measurement sites. The data set AirPoll, created by the following statements, contains the variables Site and Ozone, which are the site number and ozone level, respectively.
data AirPoll (keep = Site Ozone);
label Site = 'Site Number'
Ozone = 'Ozone level (in ppb)';
do i = 1 to 3;
input Site @@;
do j = 1 to 15;
input Ozone @@;
output;
end;
end;
datalines;
102 4 6 3 4 7 8 2 3 4 1 3 8 9 5 6
134 5 3 6 2 1 2 4 3 2 4 6 4 6 3 1
137 8 9 7 8 6 7 6 7 9 8 9 8 7 8 5
;
The following statements produce stem-and-leaf plots, box plots, and normal probability plots for each site in the AirPoll data set:
ods select Plots SSPlots; proc univariate data=AirPoll plot; by Site; var Ozone; run;
The PLOT option produces a stem-and-leaf plot, a box plot, and a normal probability plot for the Ozone variable at each site. Because the BY statement is used, a side-by-side box plot is also created to compare the ozone levels across sites. Note that AirPoll is sorted by Site; in general, the data set should be sorted by the BY variable by using the SORT procedure. The ODS SELECT statement restricts the output to the "Plots" and "SSPlots" tables; see the section ODS Table Names. Optionally, you can specify the PLOTSIZE=
option to control the approximate number of rows (between 8 and the page size) that the plots occupy.
Output 4.5.1 through Output 4.5.3 show the plots produced for each BY group. Output 4.5.4 shows the side-by-side box plot for comparing Ozone values across sites.
| Analysis of Score Changes |
Stem Leaf # Boxplot
9 0 1 |
8 00 2 |
7 0 1 +-----+
6 00 2 | |
5 0 1 | |
4 000 3 *--+--*
3 000 3 +-----+
2 0 1 |
1 0 1 |
----+----+----+----+
Normal Probability Plot
9.5+ *++++
| * * ++++
| * +++++
| * *+++
5.5+ +*++
| **+*
| * *+*+
| *++++
1.5+ *++++
+----+----+----+----+----+----+----+----+----+----+
-2 -1 0 +1 +2
|
| Analysis of Score Changes |
Stem Leaf # Boxplot
6 000 3 |
5 0 1 +-----+
4 000 3 | |
3 000 3 *--+--*
2 000 3 +-----+
1 00 2 |
----+----+----+----+
Normal Probability Plot
6.5+ * * ++*+++
| * ++++++
| **+*+++
| **+*+++
| *+*+*++
1.5+ * ++*+++
+----+----+----+----+----+----+----+----+----+----+
-2 -1 0 +1 +2
|
| Analysis of Score Changes |
Stem Leaf # Boxplot
9 000 3 |
8 00000 5 +-----+
7 0000 4 +--+--+
6 00 2 |
5 0 1 0
----+----+----+----+
Normal Probability Plot
9.5+ * *++++*++++
| * ** *+*+++++
7.5+ * * **++++++
| *++*+++++
5.5+ +++*++++
+----+----+----+----+----+----+----+----+----+----+
-2 -1 0 +1 +2
|
| Analysis of Score Changes |
|
10 +
|
| | |
| | |
8 + | *-----*
| | | + |
| +-----+ +-----+
| | | |
6 + | | | |
| | | |
| | + | +-----+ 0
| | | | |
4 + *-----* | |
| | | | + |
| +-----+ *-----*
| | | |
2 + | +-----+
| | |
| | |
|
0 +
------------+-----------+-----------+-----------
Site 102 134 137
|
Note that you can use the PROBPLOT statement with the NORMAL option to produce high-resolution normal probability plots; see the section Modeling a Data Distribution.
Note that you can use the BOXPLOT procedure to produce box plots that use high-resolution graphics. See Chapter 25, The BOXPLOT Procedure (SAS/STAT User's Guide).
A sample program for this example, uniex04.sas, is available in the SAS Sample Library for Base SAS software.