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 Plots for Line Printer OutputAnalyzing 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
Figure 4.1 shows a table of basic summary measures and a table of extreme observations for the loan-to-value ratios of 5,840 home mortgages.
The ratios are saved as values of the variable LoanToValueRatio in a data set named HomeLoans. The following statements request a univariate analysis:
ods select BasicMeasures ExtremeObs; proc univariate data=HomeLoans; var LoanToValueRatio; run;
The ODS SELECT statement restricts the default output to the tables for basic statistical measures and extreme observations.
Figure 4.1: Basic Measures and Extreme Observations
The UNIVARIATE Procedure
Variable: LoanToValueRatio (Loan to Value Ratio)
| Basic Statistical Measures | |||
|---|---|---|---|
| Location | Variability | ||
| Mean | 0.292512 | Std Deviation | 0.16476 |
| Median | 0.248050 | Variance | 0.02715 |
| Mode | 0.250000 | Range | 1.24780 |
| Interquartile Range | 0.16419 | ||
| Extreme Observations | |||
|---|---|---|---|
| Lowest | Highest | ||
| Value | Obs | Value | Obs |
| 0.0651786 | 1 | 1.13976 | 5776 |
| 0.0690157 | 3 | 1.14209 | 5791 |
| 0.0699755 | 59 | 1.14286 | 5801 |
| 0.0702412 | 84 | 1.17090 | 5799 |
| 0.0704787 | 4 | 1.31298 | 5811 |
The tables in Figure 4.1 show, in particular, that the average ratio is 0.2925 and the minimum and maximum ratios are 0.06518 and 1.1398, respectively.