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Statistics and Chemometrics for Analytical Chemistry

Statistics and Chemometrics for Analytical Chemistry

James Miller | Jane C Miller

(2018)

Additional Information

Book Details

Abstract

Statistics and Chemometrics for Analytical Chemistry 7th edition provides a clear, accessible introduction to main statistical methods used in modern analytical laboratories.

It continues to be the ideal companion for students in Chemistry and related fields keen to build their understanding of how to conduct high quality analyses in areas such as the safety of food, water and medicines, environmental monitoring, and chemical manufacturing. With a focus on the underlying statistical ideas, this book incorporates useful real world examples, step by step explanation and helpful exercises throughout.

 

Features of the new edition:

 

·    Significant revision of the Quality of analytical measurements chapter to incorporate more detailed coverage of the estimation of measurement uncertainty and the validation of analytical methods.

 

·    Updated coverage of a range of topics including robust statistics, Bayesian methods, and testing for normality of distribution, plus expanded material on regression and calibration methods.

 

·    Additional experimental design methods, including the increasingly popular optimal designs.

 

·    Worked examples have been updated throughout to ensure compatibility with the latest versions of Excel and Minitab.

 

·    Exercises are available at the end of each chapter to allow student to check understanding and prepare for exams.  Answers are provided at the back of the book for handy reference. 

 

 

This book is aimed at undergraduate and graduate courses in Analytical Chemistry and related topics.  It will also be a valuable resource for researchers and chemists working in analytical chemistry.

Table of Contents

Section Title Page Action Price
Cover Cover
Title Page iii
Copyright Page iv
Contents v
Preface to the seventh edition ix
Preface to the first edition xi
Publisher’s acknowledgements xiii
Glossary of symbols xvii
1 Introduction 1
1.1 Analytical problems 1
1.2 Errors in quantitative analysis 2
1.3 Types of error 3
1.4 Random and systematic errors in titrimetric analysis 6
1.5 Handling systematic errors 8
1.6 Planning and design of experiments 11
1.7 Statistical calculations 12
Bibliography and resources 14
Exercises 15
2 Statistics of repeated measurements 16
2.1 Mean and standard deviation 16
2.2 The distribution of repeated measurements 18
2.3 Log-normal distribution 23
2.4 Definition of a ‘sample’ 24
2.5 The sampling distribution of the mean 25
2.6 Confidence limits of the mean for large samples 26
2.7 Confidence limits of the mean for small samples 27
2.8 Presentation of results 29
2.9 Other uses of confidence limits 30
2.10 Confidence limits of the geometric mean for a log-normal distribution 30
2.11 Propagation of random errors 31
2.12 Propagation of systematic errors 34
Bibliography 35
Exercises 35
3 Significance tests 37
3.1 Introduction 37
3.2 Comparison of an experimental mean with a known value 38
3.3 Comparison of two experimental means 39
3.4 Paired t-test 43
3.5 One-sided and two-sided tests 45
3.6 F-test for the comparison of standard deviations 47
3.7 Outliers 49
3.8 Analysis of variance 52
3.9 Comparison of several means 53
3.10 The arithmetic of ANOVA calculations 56
3.11 The chi-squared test 59
3.12 Testing for normality of distribution 61
3.13 Conclusions from significance tests 65
3.14 Bayesian statistics 67
Bibliography 70
Exercises 71
4 The quality of analytical measurements 75
4.1 Introduction 75
4.2 Sampling 76
4.3 Separation and estimation of variances using ANOVA 77
4.4 Sampling strategy 78
4.5 Introduction to quality control methods 79
4.6 Shewhart charts for mean values 80
4.7 Shewhart charts for ranges 82
4.8 Establishing the process capability 84
4.9 Average run length: CUSUM charts 87
4.10 Zone control charts (J-charts) 91
4.11 Proficiency testing schemes 93
4.12 Method performance studies (collaborative trials) 96
4.13 Uncertainty 100
4.14 Acceptance sampling 109
4.15 Method validation 112
Bibliography 116
Exercises 117
5 Calibration methods in instrumental analysis: regression and correlation 120
5.1 Introduction: instrumental analysis 120
5.2 Calibration graphs in instrumental analysis 122
5.3 The product–moment correlation coefficient 124
5.4 The line of regression of y on x 128
5.5 Errors in the slope and intercept of the regression line 129
5.6 Calculation of a concentration and its random error 131
5.7 Limits of detection 134
5.8 The method of standard additions 138
5.9 Use of regression lines for comparing analytical methods 140
5.10 Weighted regression lines 145
5.11 Intersection of two straight lines 150
5.12 ANOVA and regression calculations 152
5.13 Introduction to curvilinear regression methods 153
5.14 Curve fitting 156
5.15 Outliers in regression 160
Bibliography 162
Exercises 162
6 Non-parametric and robust methods 165
6.1 Introduction 165
6.2 The median: initial data analysis 166
6.3 The sign test 171
6.4 The Wald–Wolfowitz runs test 173
6.5 The Wilcoxon signed rank test 174
6.6 Simple tests for two independent samples 177
6.7 Non-parametric tests for more than two samples 180
6.8 Rank correlation 182
6.9 Non-parametric regression methods 183
6.10 Introduction to robust methods 186
6.11 Simple robust methods: trimming and winsorisation 187
6.12 Further robust estimates of location and spread 188
6.13 Robust ANOVA 190
6.14 Robust regression methods 191
6.15 Re-sampling statistics 193
6.16 Conclusion 194
Bibliography and resources 195
Exercises 196
7 Experimental design and optimisation 198
7.1 Introduction 198
7.2 Randomisation and blocking 200
7.3 Two-way ANOVA 201
7.4 Latin squares and other designs 204
7.5 Interactions 205
7.6 Identifying the important factors: factorial designs 211
7.7 Fractional factorial designs 216
7.8 Optimal experimental designs 219
7.9 Optimisation: basic principles and univariate methods 220
7.10 Optimisation using the alternating variable search method 223
7.11 The method of steepest ascent 225
7.12 Simplex optimisation 227
7.13 Simulated annealing 231
Bibliography and resources 232
Exercises 232
8 Multivariate analysis 235
8.1 Introduction 235
8.2 Initial analysis 237
8.3 Principal component analysis 238
8.4 Cluster analysis 242
8.5 Discriminant analysis 246
8.6 K-nearest neighbour (KNN) method 250
8.7 Disjoint class modelling 251
8.8 Regression methods 251
8.9 Multiple linear regression (MLR) 253
8.10 Principal components regression (PCR) 255
8.11 Partial least-squares (PLS) regression 257
8.12 Natural computation methods: artificial neural networks 260
8.13 Conclusion 261
Bibliography and resources 262
Exercises 262
Solutions to exercises 265
Appendix 1: Commonly used statistical significance tests 275
Appendix 2: Statistical tables 278
Index 287
A 287
B 287
C 287
D 288
E 288
F 288
G 289
H 289
I 289
J 289
K 289
L 289
M 289
N 290
O 290
P 290
Q 290
R 291
S 291
T 292
U 292
V 292
W 292
X 292
Y 292
Z 292
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