What is the best way for practising analysts to assess the fitness for purpose of their statistics processing routines? This book, published as part of the Valid Analytical Measurement initiative, provides reference data sets which will form an essential tool for the validation of the major post-analysis computational processes in analytical chemistry.
Statistics Software Qualification will enable readers to test implementations of many of the important algorithms used in statistics and chemometrics. The quality of the results returned by these algorithms can vary widely. As the use of automated instruments in analysis increases, the need to detect possible deficiencies in software has become vital. This timely book provides the reader with a tool to bring a crucial part of his data analysis under the control of a quality assurance programme.
The book presents a general method for generating test data, describes a step-by-step testing methodology with worked examples, and includes over 170 data sets for testing the most important statistical procedures. It also discusses the main algorithms in common use, their strengths and weaknesses, and methods of overcoming poor performance in existing software. All the data sets described in the book are also provided on disk.
Statistics Software Qualification will prove an essential tool for practising analytical chemists in academia or industry, as well as instrument manufacturers developing new technology.
Contents
Test Methodology and Rationale; Advice on the Use of Statistical Procedures; Instructions on the Use of Reference Data and Results for Testing; The Sample Mean and Sample Standard Deviation; Univariate Outlier Detection; Polynomial Regression; Univariate Linear Regression; Multivariate Linear Regression; Partial Least Squares; Principal Component Analysis; Basic Statistical Distributions; Procedure for Testing Peak Processing Software; Statistical Procedures: Algorithms, Implementation and Use; The Sample Mean and Standard Deviation; Outlier Detection in Small Normal Samples; Polynomial Regression; Linear Regression; Principal Component Analysis; Basic Statistical Distributions; Peak Processing; Reference Data Sets and Reference Results; Sample Mean and Standard Deviation; Outlier Detection; Polynomial Regression; Linear Regression; Data Sets and Results for PCA; Basic Statistical Distributions; Peak Integration with Baseline Correction.
* This publication it out of print and there are no copies available.