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Statistics and Data Analysis

​​​​​​​​​​​​​​​Statistics and Data Analysis​​​​

​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​The Statistics Group provides experimental design, data management, uncertainty analysis, and data analysis services to a wide variety of laboratory programs. 

The statistics team is multidisciplinary, with expertise in statistics, mathematics, geochemistry, and information technology. The department has expertise in SAS, JMP, SQL Server, R, and MATLAB, allowing team members to select the best tool for data being analyzed.

The core capabilities of the group include:

  • Experimental Design

  • Pattern recognition

  • Reliability analysis​

  • Bayesian statistics

  • Spatial modeling and geostatistics

  • Stochastic modeling

  • Uncertainty analysis and error propagation

  • Development of data quality standards

  • Database design and development

Word cloude of data scientist terms​​​

Applying these capabilities, guidance and analysis is provided for:

  • defining data needs 

  • designing statistically relevant experiments 

  • managing data and databases

  • analyzing data so that it can support decision making, including reliability analysis

  • summarizing data and analysis results in reports, web pages, and presentations

  • assuring the quality of data and software​

Statistical support is provided to:   

  • Light Water Reactor Sustainability (LWRS) Program

  • Advanced Reactor Technology (ART) Program

  • Nuclear Regulatory Commission (NRC)

  • Accident Tolerant Fuel Program

  • Homeland Security

  • Internal INL organizations​

Experimental Design and Statistical Analysis​

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The group's experimental design expertise has been utilized by several programs and projects at INL. Some examples include factorial design and analysis for the study of the impact of harvest equipment on ash variability of baled corn stover biomass for bioenergy, mixture design for optimizing the blend of biomass feedstock for economic feasibility of biofuel production, response surface design for modeling the effects of various irradiation parameters (e.g. burnup) on fuel plates (e.g. blistering, swelling), and paired comparison design to determine the effect of TRISO coated fuel type and elevated temperature on fuel performance.

Extensive sampling plans have been developed for a number of different applications. Examples are: the statistical analysis of borated stainless steel, compliance of TRISO coated fuel parameters to fabrication specifications, and cost estimation of waste container treatments. 

Standard and non-standard statistical analysis techniques are used in the analysis of data from multiple programs with a wide variety of applications. These include Bayesian statistics in the trending of nuclear power plant system and component failure events, statistical monitoring of thermocouple performance in irradiation experiments, regression analysis of compensation practices, distribution estimation of nuclear fuel properties, and uncertainty analyses of calculated fuel temperatures from irradiation experiments. ​

Nuclear Data Management and Analysis System (NDMAS)

The statistics group developed and maintains the Nuclear Data Management and Analysis System (NDMAS), a data management system that was originally developed to house the Next Generation Nuclear Plant data. The work performed by the NDMAS team includes: 
  • Data archival 

  • Long-term data storage in a relational database 

  • Tracking of data qualification 

  • Assistance with the data qualification process 

  • Data analysis 

  • Web delivery of data, graphs, and analysis results via SharePoint​

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Nancy Lybeck
Statistics and Data Analysis
Nancy Lybeck​