Professional Reference Guide from C.H.I.P.S.
Statistical Methods in Analytical Chemistry
2nd Edition
by Peter C. Meier and Richard E. Zünd

This new edition continues to provide you with practical information on the use of statistical methods for solving real-world problems in complex industrial environments.

Statistical Methods in Analytical Chemistry is a thoroughly updated book that clearly demonstrates how to obtain reliable results by choosing the most appropriate experimental design and data evaluation methods.

Features

• Covers univariate, bivariate, and multivariate data
• Features case studies from the pharmaceutical and chemical industries demonstrating typical problems analysts encounter and the techniques used to solve them
• Offers information on ancillary techniques, including a short introduction to optimization, exploratory data analysis, smoothing and computer simulation, and recapitulation of error propagation
• Boasts numerous Excel files and compiled Visual Basic programs-no statistical table lookups required
• Uses Monte Carlo simulation to illustrate the variability inherent in statistically indistinguishable data sets
• and more!
Contents

1. Univariate Data
• Mean and Standard Deviation
• Distributions and the Problem of Small Numbers
• Confidence Limits
• Simulation of a Series of Measurements
• Number of Determinations
• Width of a Distribution
• Charting a Distribution
• Errors of the First and Second Kind

2. Bi- and Multivariate Data
• Correlation
• Linear Regression
• Nonlinear Regression
• Multidimensional Data/Visualizing Data

3. Related Topics
• GMP Background: Selectivity and Interference/Linearity/Accuracy/Precision/ Reliability/Economic Considerations
• Development, Qualification, and Validation; Installation Qualification, Operations Qualification, Performance Qualification/Method Development/Method Validation
• Data Treatment Scheme: Data Acquisition/Acceptance Criteria/Data Assembly and Clean-Up/Data Evaluation/Presentation of Results/Specifications/Records Retention
• Exploratory Data Analysis (EDA)
• Optimization Techniques
• Smoothing and Filtering Data/Box-Car Averaging/Moving Average/Savitzky-Golay Filtering/ CUSUM
• Error Propagation and Numerical Artifacts
• Programs

4. Complex Examples
• Nonlinear Fitting
• UV-Assay Cost Structure
• Process Validation
• Regulations and Realities
• Diffusing Vapors
• Stability à la Carte
• Table Press Woes
• Sounding Out Solubility
• Exploring a Data Jungle
• Sifting Through Sieved Samples
• Controlling Cyanide
• Ambiguous Automation
• Mistrusted Method
• Quirks of Quantitation
• Pursuing Propagating Errors
• Content Uniformity
• Arrhenius-Abiding Aging
• Proving Proficiency
• Limits of Nonlinearities
• Zealous Statistical Apprentice
• Complacent Control
• Systems Suitability
• Keeping Track of Dissolving Tablets

5. Appendices
• Numerical Approximations to Some Frequently Used Distributions
• Core Instructions Used in Several Programs
• Installation and Use of Programs on Diskette
• Program and Data File Description

Technical Tidbits
Glossary
References

Index