All Stories

  1. We Stand on the Shoulders of Giants - Pioneers of Statistics in Industry
  2. Discussion
  3. A Practical Approach to Data Mining: I Have All These Data; Now What Should I Do?
  4. Industry and Business, Statistics in
  5. Discussion of ‘Opportunities to empower statisticians in emerging areas’
  6. Discussion of “Statistics: A Life Cycle View”
  7. Statistics in Industry
  8. Graphical Representation of Data
  9. Histograms
  10. Window Plot
  11. Applying statistical thinking to ‘Big Data’ problems
  12. Assessing Component Effects in Formulation Systems
  13. Statistical Engineering—Roles for Statisticians and the Path Forward
  14. Statistical Engineering—Forming the Foundations
  15. Leadership—Essential for Developing the Discipline of Statistical Engineering
  16. Statistics to Facilitate Innovation*: A Panel Discussion
  17. Statistics in Industry
  18. Understanding Formulation Systems—A Six Sigma Approach
  19. Rejoinder: A Consensus that the Statistics Profession Must Change, Is Growing
  20. Response to “Statistical Thinking and Methods in Quality Improvement: A Look to the Future” by Roger Hoerl and Ronald Snee
  21. Lean Six Sigma – getting better all the time
  22. Measuring the hydrophobicity of lubricated blends of pharmaceutical excipients
  23. Moving the Statistics Profession Forward to the Next Level
  24. Shear-induced APAP de-agglomeration
  25. ‘Post-financial meltdown: What do the services industries need from us now?’ by Roger W. Hoerl and Ronald D. Snee: Rejoinder
  26. Post-financial meltdown: What do the services industries need from us now?
  27. ‘Post-financial meltdown: What do the services industries need from us now?’ by Roger W. Hoerl and Ronald D. Snee: Discussion 2
  28. ‘Post-financial meltdown: What do the services industries need from us now?’ by Roger W. Hoerl and Ronald D. Snee: Discussion 1
  29. Shear-induced APAP de-agglomeration
  30. W. Edwards Deming's “Making AnotherWorld”
  31. Failure Modes and Effects Analysis
  32. Discussion
  33. Getting better all the time: the future of business improvement methodology
  34. Industry, Statistics in
  35. Graphical Representation of Data
  36. Histograms
  37. Window Plot
  38. Leading Business Improvement: a New Role for Statisticians and Quality Professionals
  39. Graphical Representation of Data
  40. Histograms
  41. Industry, Statistics in
  42. Window Plot
  43. Six-Sigma: the evolution of 100 years of business improvement methodology
  44. Guest Editorial
  45. Discussion: Development and Use of Statistical Thinking: A New Era
  46. Discussion: Development and Use of Statistical Thinking: A New Era
  47. Discussion: Statisticians Must Develop Data-Based Management and Improvement Systems as Well as Create Measurement Systems
  48. Discussion: Statisticians Must Develop Data-Based Management and Improvement Systems as Well as Create Measurement Systems
  49. Non-statistical skills that can help statisticians be more effective
  50. Discussion: Increasing the Value of the Statistics Profession
  51. COMMENTS ON DRAPER, PRESCOTT, LEWIS, DEAN, JOHN, AND TUCK (1993): UNDERSTANDING FORMULATION SYSTEMS
  52. Comments on Draper, Prescott, Lewis, Dean, John, and Tuck (1993): Understanding Formulation Systems
  53. What's Missing in Statistical Education?
  54. What's Missing in Statistical Education?
  55. Commentary: A Partnership Is Needed
  56. Commentary: A Partnership is Needed
  57. Statistical Thinking and Its Contribution to Total Quality
  58. Mathematics Is Only One Tool That Statisticians Use
  59. Experiments in Industry: Analysis and Interpretation of Results.
  60. Graphical Display of Results of Three-Treatment Randomized Block Experiments
  61. Variation in the relationship between blood lead and air lead
  62. Introduction to the genetic toxicology association workshop on statistical analysis of cytogenetics test systems
  63. A procedure for the statistical evaluation of Ames Salmonella assay results comparison of results among 4 laboratories
  64. Comment: Collinearity Diagnostics Depend on the Domain of Prediction, the Model, and the Data
  65. Comment
  66. Cooperation Between University and Industry Statisticians
  67. Cooperation between University and Industry Statisticians
  68. Quantitative Risk Assessment: State-of-the-Art for Carcinogenesis
  69. Quantitative Risk Assessment: State-of-the-Art for Carcinogenesis
  70. Discussion
  71. [Developments in Linear Regression Methodology: 1959-1982]: Discussion
  72. Response to Dr. Brunekreef's comments on developing an air quality standard for lead
  73. Quantitative Risk Assessment: State-of-the-Art for Carcinogenesis
  74. Models for the relationship between blood lead and air lead
  75. A statistical method for analysis of mouse lymphoma L5178Y cell TK locus forward mutation assay
  76. Nonadditivity in a Two-Way Classification: Is it Interaction or Nonhomogeneous Variance?
  77. Nonadditivity in a Two-Way Classification: Is It Interaction or Nonhomogeneous Variance?
  78. Comment
  79. Development of an air quality standard for lead from community studies
  80. Silver Valley Lead Study: Further Analysis of the Relationship between Blood Lead and Air Lead
  81. Developing Blending Models for Gasoline and Other Mixtures
  82. Developing Blending Models for Gasoline and Other Mixtures
  83. Design of a statistical method for the analysis of mutagenesis at the hypoxanthine-guanine phosphoribosyl transferase locus of cultured Chinese hamster ovary cells
  84. Graphical Display of Means
  85. Graphical Display of Means
  86. Preparing Statisticians for Careers in Industry: Report of the ASA Section on Statistical Education Committee on Training of Statisticians for Industry
  87. Experimental designs for mixture systems with multicomponent constraints
  88. Validation of Regression Models: Methods and Examples
  89. Validation of Regression Models: Methods and Examples
  90. The Annual Average: An Alternative to the Second Highest Value as a Measure of Air Quality
  91. Analysis of Variance in Complex Experimental Designs
  92. Analysis of Variance in Complex Experimental Designs
  93. Screening Concepts and Designs for Experiments with Mixtures
  94. Discussion
  95. Discussion of: The Use of Gradients to Aid in the Interpretation of Mixture Response Surfaces
  96. Experimental Designs for Quadratic Models in Constrained Mixture Spaces
  97. Recovery of Blood Lead Concentration and of Red Cell 8-Aminolevulinic Acid Dehydrase Activity in Dogs Following Return to Normal Diets after 75 Weeks of Lead Feeding
  98. Ridge Regression in Practice
  99. Ridge Regression in Practice
  100. Test Statistics for Mixture Models
  101. Test Statistics for Mixture Models
  102. A New Approach to Setting Vehicle Emission Standards
  103. Extreme Vertices Designs for Linear Mixture Models
  104. Biometrika Tables for Statisticians, Volume 2
  105. Extreme Vertices Designs for Linear Mixture Models
  106. Graphical Display of Two-Way Contingency Tables
  107. Graphical Display of Two-way Contingency Tables
  108. Longitudinal Studies of Lead Levels in a US Population
  109. Techniques for the Analysis of Mixture Data
  110. Statistical Analysis of Interlaboratory Studies
  111. Fitting Equations to Data
  112. Effect of Lead on Blood Regeneration Following Acute Hemorrhage in Dogs
  113. On the Analysis of Response Curve Data
  114. On the Analysis of Response Curve Data
  115. A Note on the Use of Residuals for Examining the Assumptions of Covariance Analysis
  116. A Note on the Use of Residuals for Examining the Assumptions of Covariance Analysis
  117. Statistical Design and Analysis of Shape Studies
  118. The potato: A practical and scientific