Robert N. Rodriguez’s research while affiliated with SAS Institute and other places

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Publications (23)


Who Will Celebrate Our 200th Anniversary? Growing the Next Generation of ASA Members
  • Article

April 2015

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11 Reads

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5 Citations

Robert N. Rodriguez

During the next 25 years, the growth and vitality of the American Statistical Association will depend on how well we attract and serve members in emerging areas of practice such as data science, where statistics as a skill set is in high demand but statistics as a profession has low recognition. Successful adaptation to the era of Big Data requires that we broaden our understanding of statistical practice to include the work of all those who learn from data. In order to grow the next generation of members, we must also retain a much higher proportion of today's student members, many of whom leave the ASA upon graduation. By providing value that meets the needs of these groups and equips them to flourish in their organizations, we can become the Big Tent for Statistics.



SAS

January 2011

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336 Reads

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102 Citations

Wiley Interdisciplinary Reviews: Computational Statistics

SAS® software is a comprehensive set of integrated tools and solutions for accessing, managing, and analyzing data. SAS, which was formed as a company in 1976, is a leading developer of statistical software, which is widely used in academic, business, and government organizations. Since the 1980s, SAS has expanded its analytical software to include forecasting and econometrics, data mining, text mining, and operations research. SAS now builds on these components to provide software for business analytics and solutions for industry-specific problems such as customer intelligence, fraud prevention, and risk management. This article describes the evolution of SAS as a company and overviews new directions in its analytical software. An example program illustrates key elements of SAS programming that are useful for statistical analysis. WIREs Comp Stat 2011 3 1–11 DOI: 10.1002/wics.131 For further resources related to this article, please visit the WIREs website


It's All About Variation: Improving Your Business Process with Statistical Thinking

December 2010

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14 Reads

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1 Citation

This paper explains how statistical thinking and statistical process monitoring, which have been practiced in manufac-turing for the past thirty years, are proving valuable for process improvement in business environments that range from health care to financial services. Basic examples drawn from real scenarios introduce the statistical concepts and show how to get started with SAS/QC ® software. The concepts also apply to complex systems that involve large volumes of multivariate data with multiple sources of variation. The examples demonstrate the use of graphical displays, cre-ated with ODS Statistical Graphics, for visualizing and analyzing the variation in a process and for explaining results to clients and management.


Getting Started with ODS Statistical Graphics in SAS® 9.2—Revised 2009

January 2009

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20 Reads

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1 Citation

ODS Statistical Graphics (or ODS Graphics for short) is major new functionality for creating statistical graphics that is available in a number of SAS software products, including the SAS/STAT®, SAS/ETS®, SAS/QC®, and SAS/GRAPH® products. With the production release of ODS Graphics in SAS 9.2, over sixty statistical procedures have been mod- ified to use this functionality, and they now produce graphs as automatically as they produce tables. In addition, new SAS/GRAPH procedures use this functionality to produce plots for exploratory data analysis and for customized statis- tical displays. SAS/GRAPH software is required for ODS Graphics functionality in SAS 9.2. This paper presents the essential information you need to get started with ODS Graphics in SAS 9.2. ODS Graphics is an extension of ODS (the Output Delivery System), which manages procedure output and lets you display it in a variety of destinations, such as HTML and RTF. Consequently, many familiar features of ODS for tabular output apply equally to graphs. For statistical procedures that support ODS Graphics, you invoke this functionality with the statement ODS GRAPHICS ON. Graphs and tables created by these procedures are then integrated in your ODS output destination. ODS Graphics produces graphs in standard image file formats, and the consistent appearance and individual layout of these graphs are controlled by ODS styles and templates, respectively. Since the default templates for procedure graphs are provided by SAS software, you do not need to know the details of templates to create statistical graphics. However, with some understanding of the underlying Graph Template Lan- guage, you can modify the default templates to make changes to graphs that are permanently in effect each time you run the procedure. Alternatively, to facilitate making immediate changes to a particular graph, SAS 9.2 introduces the ODS Graphics Editor, a point-and-click interface with which you can customize titles, annotate points, and make other enhancements.


Getting Started with ODS Statistical Graphics in SAS® 9.2

January 2008

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76 Reads

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4 Citations

ODS Statistical Graphics (or ODS Graphics for short) is major new functionality for creating statistical graphics that is available in a number of SAS software products, including SAS/STAT®, SAS/ETS®, SAS/QC®, and SAS/GRAPH®. With the production release of ODS Graphics in SAS 9.2, over sixty statistical procedures have been modified to use this functionality, and they now produce graphs as automatically as they produce tables. In addition, new procedures in SAS/GRAPH use this functionality to produce plots for exploratory data analysis and for customized statistical displays. SAS/GRAPH is required for ODS Graphics functionality in SAS 9.2. This paper presents the essential information you need to get started with ODS Graphics in SAS 9.2. ODS Graphics is an extension of ODS (the Output Delivery System), which manages procedure output and lets you display it in a variety of destinations, such as HTML and RTF. Consequently, many familiar features of ODS for tabular output apply equally to graphs. For statistical procedures that support ODS Graphics, you invoke this functionality with the ods graphics on statement. Graphs and tables created by these procedures are then integrated in your ODS output destination. ODS Graphics produces graphs in standard image file formats, and the consistent appearance and individual layout of these graphs are controlled by ODS styles and templates, respectively. Since the default templates for procedure graphs are provided by SAS, you do not need to know the details of templates to create statistical graphics. However, with some understanding of the underlying Graph Template Language, you can modify the default templates to make changes to graphs that are permanently in effect each time you run the procedure. Alternatively, to facilitate making immediate changes to a particular graph, SAS 9.2 introduces the ODS Graphics Editor, a point-and-click interface with which you can customize titles, annotate points, and make other enhancements.


Creating Statistical Graphics in SAS 9.2: What Every Statistical User Should Know

January 2006

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124 Reads

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2 Citations

Effective graphics are essential to modern statistical analysis. SAS 9.1 introduced an experimental exten-sion to the Output Delivery System (ODS), which is used by over two dozen SAS/STAT ® and SAS/ETS ® procedures to create statistical graphics as automatically as they create tables. This extension, referred to as ODS Statistical Graphics (or ODS Graphics for short), requires minimal additional syntax, and it provides displays commonly needed for data analysis and statistical modeling, including scatter plots, histograms, and box-and-whisker plots. General ODS features, such as styles and destination statements, apply equally to tables and graphs. With the production release of ODS Graphics in SAS 9.2, many more statistical procedures have been modified to use this new functionality; see Appendix A for a list. For example, PROC LOGISTIC produces effects plots, and PROC RSREG produces contour and ridge plots. New SAS/GRAPH ® procedures, as well as existing SAS/QC ® procedures, also take advantage of ODS Graphics functionality. SAS 9.2 intro-duces an interactive graphics editor, with which you can make immediate, data-specific changes to your graphs, such as customizing titles and annotating points. SAS 9.2 also adds ODS styles that are designed specifically for statistical work. In addition to describing this new work, this paper presents the basics of creating, modifying, and managing graphics.


SAS SPM Solution for Healthcare: Quality Improvement for Providers Using Statistical Process Control

January 2004

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62 Reads

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1 Citation

Across the country, significant issues threaten the ability of hospitals to meet their commitments to their communities. These issues include the problems of retaining qualified staff and identifying incompetent staff, increasing costs of staff and supplies, and public pressure. Institute of Medicine studies show that over half of medical deaths in hospitals are preventable, and statewide data reveal variability in hospital quality. The healthcare industry generates large amounts of patient-specific data. However, few hospitals can use the data to identify unusual variability in staff and physician performance, cost of care, and preventable incidents that affect the outcome of a patient's care. SAS SPM for Healthcare provides the ability to access multiple data sources and create analysis-ready data; refer to SAS Institute Inc. (2004a). As illustrated in this paper, statistical process control (SPC) can then be used to identify variability due to special causes and focus further study to reduce variability. These techniques lead to improvements in quality of care, reduction of costs, opportunities to grow market share, and negotiation of better third-party payment. This paper provides examples that explain the use of SAS statistical software to analyze health care data with u charts, p charts, control charts for individual measurements, methods for discovering trends over time, basic forecasting methods, comparative histograms, analysis of means for rates and proportions, and model-based adjustments of mortality rates.


An Introduction to ODS for Statistical Graphics in SAS 9.1

January 2004

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40 Reads

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5 Citations

In SAS 9.1, over two dozen SAS/STAT® and SAS/ETS® procedures have been modified to use an experimental extension to the Output Delivery System (ODS) that enables them to create statistical graphics as automatically as tables are currently created. This extension, referred to as ODS for Statistical Graphics (ODS Graphics for short), provides commonly used displays, including scatter plots, histograms, box-and-whisker plots, and contour plots, in ODS output. Many ODS features for tables, such as destination statements, apply equally to graphics. ODS styles control the appearance and consistency of all graphs, whereas ODS templates control the layout and details of individual graphs. This paper introduces statistical users and other SAS programmers to ODS Graphics. Examples illustrate basic functionality, which requires minimal syntax, and typical graph management tasks. Familiarity with ODS for tables is assumed.


Recent Enhancements and New Directions in SAS/STAT Software, Part I: Updates

July 2000

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23 Reads

Version 7 of the SAS System brings major enhancements to the statistical software. All output is now handled by the Output Delivery System, which gives the user control over the printing of the results, allows all tables and statistics to be output to SAS data sets, and produces web-browsable HTML output. New procedures provide tools for partial least squares analysis and spatial prediction. The GENMOD procedure now provides LSMEANS and ESTIMATE statements, and its GEE facility provides the alternating logistic regression algorithm, produces Wald and score tests for model effects, and handles the ordinal response case. Additional exact tests have been added to several procedures, and even the TTEST procedure has been updated. In addition to procedures for survey design and analysis, Version 7 also introduces experimental procedures for nonparametric density estimation and nonparametric regression, as discussed in Part II of this paper. Introduction Statistical developers have been ...


Citations (17)


... If P = 1, the number of infected sites in the sample has a hypergeometric distribution as in equation 3. If P < 1, equation 4 describes a "faulty inspection" distribution (12)(13)(14)(15)(16)(17) in which there is an acceptable rate ζ at which infected sites may wrongly be declared infection-free. ...

Reference:

Survey Methods for Assessment of Citrus tristeza virus Incidence in Urban Citrus Populations
Statistical Effects of Imperfect Inspection Sampling: I. Some Basic Distributions
  • Citing Article
  • January 1985

Journal of Quality Technology

... We now quickly note the effect of reduction of sensitivity and specificity through Dorfman's formula for expected number of tests; if the sensitivity is S e and the specificity is S p , then it can be shown easily that the expected number of tests for groups of size k changes to 1 + k [S e + (1 -S p -S e ) (1 -(1 -p) n )] (see Johnson et al., 1988Johnson et al., , 1989. Johnson et al. (1991) also developed the formulas for the sequential and the hierarchical cases, which we omit here for the sake of brevity. ...

Statistical Effects of Imperfect Inspection Sampling III. Screening (Group Testing)
  • Citing Article
  • April 1988

Journal of Quality Technology

... The confidence interval for based on � 4 , [ . 4 , . 4 ], is similarly defined. The confidence intervals for are also analyzed by Chou et al. (1990), Kushler and Hurley (1991), Franklin and Wasserman (1992), Guirguis and Rodriguez (1992), Dovich (1992), Nagata and Nagahata (1994), etc. ...

Computation of Owen's Q Function Applied to Process Capability Analysis
  • Citing Article
  • October 1992

Journal of Quality Technology

... Referem, de forma precisa, os requerimentos do consumidor e podem estar relacionados com um produto, um processo ou um serviço. A questão de saber se um processo é capaz de satisfazer as especificações tem sido perguntada numa grande variedade de indústrias desde o início de 1980 (Rodriguez, 1992). O enquadramento teórico para avaliar a capacidade do processo começou com o desenvolvimento do índice c p por Juran (1974) e ainda hoje é um dos índices mais populares (Rodriguez, 1992). ...

Recent Developments in Process Capability Analysis
  • Citing Article
  • October 1992

Journal of Quality Technology

... In summary, statisticians should embrace data science, approaching the collaboration with equal parts confidence in what statisticians can offer and humility to learn from the newer field (Diggle, 2015). Furthermore, the era of Big Data demands that statisticians broaden their understanding of statistical practice to be inclusive of all those who learn from data (Rodriguez, 2015). This is vital as data science has helped improve the reproducibility and communication of statistical outcomes, thereby adding to the reliability and validity of scientific studies (Carmichael and Marron, 2018). ...

Who Will Celebrate Our 200th Anniversary? Growing the Next Generation of ASA Members
  • Citing Article
  • April 2015

... The toxicity or efficacy of the insecticide formulations used was determined by calculating the LC50, 90 and 95 (the concentration required for the death of 50, 90 and 95% of insects) using a computerized probability analysis, SAS SOFTWARE (Rodriguez, 2011). Biochemical determinations were pooled from triplicate determinations. ...

SAS
  • Citing Article
  • January 2011

Wiley Interdisciplinary Reviews: Computational Statistics

... As in the case of service quality, it is difficult to analyze and improve reliability without meaningful metrics (Palm et al., 1997;Sulek, 2004). These metrics should align with the performance issues the company wishes to investigate. ...

Some Perspectives and Challenges for Control Chart Methods
  • Citing Article
  • Full-text available
  • April 1997

Journal of Quality Technology

... An analogy we have found useful for explaining the purpose of the LISA 2020 program is the "Big Tent." As the ASA's 107th president, Dr. Bob Rodriguez advocated for the ASA to become the "Big Tent" for statistics (Rodriguez 2013). Rodriguez explained, "Big tents do three things. ...

Building the Big Tent for Statistics
  • Citing Article
  • March 2013

... We shall investigate the effects of faulty testing on the properties of Dorfman-Sterrett procedures, using techniques developed in parallel investigations for standard acceptance sampling procedures (e.g. Johnson et al. (1985 Johnson et al. ( , 1986). Other variants of the standard Dorfman procedure have been suggested and studied by Sobel and Groll (1959), Sobel (1960,1968 In the formulas to be obtained below, we will leave Pr[Y=y] unspecified. ...

Statistical Effects of Imperfect Inspection Sampling: II. Double Sampling and Link Sampling
  • Citing Article
  • April 1986

Journal of Quality Technology

... Following this procedure, a contour map is created using a bilinear surface patch model. This method is explained in Rodriguez and Stokes (1998) and applied in SAS software. In this paper, the Kernel Standard Deviation was set to 6 to enable a comparison between all linkographs. ...

Recent Enhancements and New Directions in SAS/ST AT® Software, Part II. Nonparametric Modeling Procedures
  • Citing Article