Statistical Design Chemometrics 1st Edition by R E Bruns, I S Scarminio, B de Barros Neto ISBN 044452181X 9780444521811

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ISBN 10: 044452181X 
ISBN 13: 9780444521811
Author: R E Bruns, I S Scarminio, B de Barros Neto

Statistical Design-Chemometrics is applicable to researchers and professionals who wish to perform experiments in chemometrics and carry out analysis of the data in the most efficient way possible. The language is clear, direct and oriented towards real applications. The book provides 106 exercises with answers to accompany the study of theoretical principles. Forty two cases studies with real data are presented showing designs and the complete statistical analyses for problems in the areas chromatography, electroanalytical and electrochemistry, calibration, polymers, gas adsorption, semiconductors, food technology, biotechnology, photochemistry, catalysis, detergents and ceramics. These studies serve as a guide that the reader can use to perform correct data analyses.

-Provides 42 case studies containing step-by-step descriptions of calculational procedures that can be applied to most real optimization problems
-Contains 106 theoretical exercises to test individual learning and to provide classroom exercises and material for written tests and exams
-Written in a language that facilitates learning for physical and biological scientists and engineers
-Takes a practical approach for those involved in industrial optimization problems

Statistical Design Chemometrics 1st Table of contents:

1. How statistics can help
2. Statistics can help
3. Empirical models
4. Experimental design and optimization
5. When the situation is normal
6. Applications
7. From home to work
8. Bioequivalence of brand-name and generic medicines
9. Still more beans?
10. Marine algae productivity
11. Applying the normal distribution
12. Making comparisons with a reference value
13. Determining sample size
14. Statistically controlling processes
15. Comparing two treatments
16. Random sampling in normal populations
17. Linear combinations of random variables
18. Covariance and correlation
19. The normal distribution
20. Calculating probabilities of occurrence
21. Using the tails of the standard normal distribution
22. Why is the normal distribution so important
23. Calculating confidence intervals for the mean
24. Interpreting confidence intervals
25. Populations, samples and distributions
26. How to describe the characteristics of the sample
27. Errors
28. Types of error
29. Changing everything at the same time
30. A 2² factorial design
31. Calculating the effects
32. Geometrical interpretation of the effects
33. Estimating the error of an effect
34. Interpreting the results
35. An algorithm for calculating the effects
36. The statistical model
37. A 2³ factorial design
38. Calculating the effects
39. Estimating the error of an effect
40. Interpreting the results
41. The statistical model
42. A 24 factorial design
43. Calculating the effects
44. Estimating the error of an effect
45. Normal probability plots
46. Evolutionary operation with two-level designs
47. Blocking factorial designs
48. Applications
49. Resin hydrolysis
50. Cyclic voltammetry of methylene blue
51. Retention time in liquid chromatography
52. Gas separation by adsorption
53. Improving wave functions
54. Performance of Ti/TiO2 electrodes
55. Controlling detergent froth
56. Development of a detergent
57. A blocked design for producing earplugs
58. When there are many variables
59. Half-fractions of factorial designs
60. How to construct a half-fraction
61. Generators of fractional factorial designs
62. The concept of resolution
63. Resolution IV fractional factorial designs
64. Resolution V fractional factorial designs
65. Inert variables and factorials embedded in fractions
66. Half-fractions of maximum resolution
67. Screening variables
68. Resolution III fractional factorial designs
69. Saturated designs
70. How to construct resolution III fractional factorial designs
71. How to construct a 2IV^8-4 fraction from a 2III^7-4 fraction
72. Saturated Plackett–Burman designs
73. Taguchi techniques for quality engineering
74. Applications
75. Adsorption on organofunctionalized silicas
76. Calcium oxalate thermogravimetry
77. Chromatographic analysis of gases
78. Mn-porphyrin catalytic response
79. Oxide drainage in the steel industry
80. Violacein production by bacteria
81. Polyester resin cure
82. Screening design for earplug production
83. Plackett–Burman designs for screening factors
84. Empirical model-building
85. A model for y=f(X)
86. The analysis of variance
87. Confidence intervals
88. Statistical significance of the regression model
89. A new model for y=f(X)
90. Lack of fit and pure error
91. Correlation and regression
92. Applications
93. The spring of air
94. Chromatographic calibration
95. Multivariate calibration
96. Forbidden energy gaps in semiconductors
97. Heat of vaporization determination
98. Another calibration
99. Exploring the response surface
100. Response surface methodology
101. Initial modeling
102. Determining the path of steepest ascent
103. Finding the optimum point
104. The importance of the initial design
105. An experiment with three factors and two responses
106. Treating problems with many variables
107. Central composite designs
108. Box–Behnken designs
109. Doehlert designs
110. Optimal designs
111. Applications
112. 6A.1 Mo(VI) catalytic response
113. Osmotic dehydration of pineapple
114. Reducing cholesterol levels
115. Laccase production
116. Increasing the oxygen in air
117. Earplug optimization study — concluding phase
118. Photodegradability of herbicides
119. Mixture modeling
120. Two-component mixtures
121. Three-component mixtures
122. An example of a three-component mixture
123. Cubic models for three-component mixtures
124. Model evaluation
125. Pseudocomponents
126. Other mixture designs
127. Mixtures with more than three components
128. Applications
129. Solvent influence on Fe(III) ion complexation
130. Tensile strength of polymeric materials
131. Cr(VI) catalytic determination
132. Polymer blend conductivity
133. The proof of the pudding is not in the eating
134. Designing new ceramic materials
135. Improving selectivity in high-performance liquid chromatography
136. Simplex optimization
137. The basic simplex
138. The modified simplex
139. The supermodified simplex
140. References
141. Uncited References

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Tags: R E Bruns, I S Scarminio, B de Barros Neto, Statistical Design, Chemometrics