Preface,
Glossary of symbols, CHAPTER 1 Introduction
1.1 Analytical problems, 1.2 Errors in quantitative analysis, 1.3 Types of error, 1.4 Random and systematic errors in titrimetric analysis, 1.5 Handling systematic errors, 1.6 Planning and design of experiments, 1.7 Calculators and computers in statistical calculations, Bibliography, Exercises, CHAPTER 2 Statistics of repeated measurements
2.1 Mean and standard deviation, 2.3 The distribution of repeated measurements, 2.4 Definition of a sample , 2.5 The sampling distribution of the mean, 2.6 Confidence limits of the mean for large samples, 2.7 Confidence limits of the mean for small samples, 2.8 Presentation of results, 2.9 Other uses of confidence limits, 2.10 Confidence limits of the geometric mean for a log-normal distribution, 2.11 Propagation of random errors, 2.12 Propagation of systematic errors, Bibliography, Exercises, CHAPTER 3 Significance tests
3.1 Introduction, 3.2 Comparison of an experimental mean with a known value, 3.3 Comparison of two experimental means, 3.4 Paired t-test, 3.5 One-side and two-sided tests, 3.6 F-test for comparison of standard deviations, 3.7 Outliers, 3.8 Analysis of variance, 3.9 Comparison of several means, 3.10 The arithmetic of ANOVA calculations, 3.11 The Chi-squared test, 3.12 Testing normality of distribution, 3.13 Conclusions from significance tests, Bibliography, Exercises, CHAPTER 4 The quality of analytical measurements
4.1 Introduction, 4.2 Sampling, 4.3 Separation and estimation of variances using ANOVA, 4.4 Sampling strategy, 4.5 Quality control methods: introduction, 4.6 Shewhart charts for mean values, 4.7 Shewhart charts for ranges, 4.8 Establishing the process capability, 4.9 Average run length: Cusum charts, 4.10 Proficiency testing schemes, 4.11 Collaborative trials, 4.12 Uncertainty, 4.13 Acceptance sampling, Bibliography, Exercises, CHAPTER 5 Calibration methods in instrumental analysis: regression and correlation
5.1 Introduction: instrumental analysis, 5.2 Calibration graphs in instrumental analysis, 5.3 The product-moment correlation coefficient, 5.4 The line of regression of y on x, 5.5 Errors in the slope and intercept of the regression line, 5.6 Calculation of a concentration and its random error, 5.7 Limits of detection, 5.8 The method of standard additions, 5.9 Use of regression lines for comparing analytical methods, 5.10 Weighted regression lines, 5.11 Intersection of two straight lines, 5.12 ANOVA and regression calculations, 5.13 Curvilinear regression methods: introduction, 5.14 Curve fitting, 5.15 Outliers in regression, Bibliography, Exercises, CHAPTER 6 Non-parametric and robust methods
6.1 Introduction, 6.2 The median: initial data analysis, 6.3 The sign test, 6.4 The Wald-Wolfowitz runs test, 6.5 The Wilcoxon signed rank test, 6.6 Simple tests for two independent samples, 6.7 Non-parametric tests for more than two samples, 6.8 Rank correlation, 6.9 Non-parametric regression methods, 6.10 Robust methods, 6.11 Robust regression methods, 6.12 The Kolmogorov text for goodness of fit, 6.13 Conclusions, Bibliography, Exercises, CHAPTER 7 Experimental design and optimisation
7.1 Introduction, 7.2 Randomisation and blocking, 7.3 Two way ANOVA, 7.4 Latin squares and other designs, 7.7 Interactions, 7.6 Factorial versus one-at-a-time design, 7.7 Factorial design and optimisation, 7.8 Optimisation: basic principles and univariate methods, 7.9 Optimisation using the alternating variable search method, 7.10 The method of steepest ascent, 7.11 Simplex optimisation, 7.12 Simulated annealing, Bibliography, Exercises, CHAPTER 8 Multivariate analysis
8.1 Introduction, 8.2 Initial analysis, 8.3 Principal component analysis, 8.4 Cluster analysis, 8.5 Discriminant analysis, 8.6 K-nearest neighbour model, 8.7 Disjoint class modelling, 8.8 Multiple regression, 8.9 Principal components regression, 8.10 Multivari |
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