A Survey of Statistical Approaches to Preserving Confidentiality of Contingency Table Entries

2008 ◽  
pp. 291-312 ◽  
Author(s):  
Stephen E. Fienberg ◽  
Aleksandra B. Slavkovic
Keyword(s):  
Contingency Table ◽  
Statistical Approaches

scholarly journals statsExpressions: R Package for Tidy Dataframes and Expressions with Statistical Details

2021 ◽  
Author(s):  
Indrajeet Patil
Keyword(s):  
Contingency Table ◽  
R Package ◽  
T Test ◽  
Correlation Analyses ◽  
Statistical Approaches ◽  
Meta Analyses ◽  
One Way Anova

The `statsExpressions` package is designed to facilitate producing dataframes with rich statistical details for the most common types of statistical approaches and tests: parametric,nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages to display these details.

Advanced Statistical Tools for Improving Yield and Reliability

1999 ◽  
Author(s):  
Richard C. Kittler
Keyword(s):  
Data Mining ◽  
Analysis Data ◽  
Neural Nets ◽  
Future Trends ◽  
Statistical Tools ◽  
Mining Technology ◽  
Explanatory Variables ◽  
Data Coverage ◽  
Statistical Approaches ◽  
Adequate Data

Abstract Analysis of manufacturing data as a tool for failure analysts often meets with roadblocks due to the complex non-linear behaviors of the relationships between failure rates and explanatory variables drawn from process history. The current work describes how the use of a comprehensive engineering database and data mining technology over-comes some of these difficulties and enables new classes of problems to be solved. The characteristics of the database design necessary for adequate data coverage and unit traceability are discussed. Data mining technology is explained and contrasted with traditional statistical approaches as well as those of expert systems, neural nets, and signature analysis. Data mining is applied to a number of common problem scenarios. Finally, future trends in data mining technology relevant to failure analysis are discussed.

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