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
Rounding
When you specify ROUND=u, PROC UNIVARIATE rounds a variable by using the rounding unit to divide the number line into intervals with midpoints of the form , where u is the nonnegative rounding unit and i is an integer. The interval width is u. Any variable value that falls in an interval is rounded to the midpoint of that interval. A variable value that is midway between two midpoints, and is therefore on the boundary of two intervals, rounds to the even midpoint. Even midpoints occur when i is an even integer .
When ROUND=1 and the analysis variable values are between –2.5 and 2.5, the intervals are as follows:
Table 4.27: Intervals for Rounding When ROUND=1
|
i |
Interval |
Midpoint |
Left endpt rounds to |
Right endpt rounds to |
|---|---|---|---|---|
|
–2 |
[–2.5, –1.5] |
–2 |
–2 |
–2 |
|
–1 |
[–1.5, –0.5] |
–1 |
–2 |
0 |
|
0 |
[–0.5, 0.5] |
0 |
0 |
0 |
|
1 |
[0.5, 1.5] |
1 |
0 |
2 |
|
2 |
[1.5, 2.5] |
2 |
2 |
2 |
When ROUND=0.5 and the analysis variable values are between –1.25 and 1.25, the intervals are as follows:
Table 4.28: Intervals for Rounding When ROUND=0.5
|
i |
Interval |
Midpoint |
Left endpt rounds to |
Right endpt rounds to |
|---|---|---|---|---|
|
–2 |
[–1.25, –0.75] |
–1.0 |
–1 |
–1 |
|
–1 |
[–0.75, –0.25] |
–0.5 |
–1 |
0 |
|
0 |
[–0.25, 0.25] |
0.0 |
0 |
0 |
|
1 |
[0.25, 0.75] |
0.5 |
0 |
1 |
|
2 |
[0.75, 1.25] |
1.0 |
1 |
1 |
As the rounding unit increases, the interval width also increases. This reduces the number of unique values and decreases the amount of memory that PROC UNIVARIATE needs.