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Statistical and Multivariate Analysis in Material Science [Kõva köide]

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  • Formaat: Hardback, 276 pages, kõrgus x laius: 234x156 mm, kaal: 548 g, 96 Tables, black and white; 174 Illustrations, black and white
  • Ilmumisaeg: 21-May-2021
  • Kirjastus: CRC Press
  • ISBN-10: 1138196304
  • ISBN-13: 9781138196308
Teised raamatud teemal:
Statistical and Multivariate Analysis in Material ScienceSuurem pilt
  • Formaat: Hardback, 276 pages, kõrgus x laius: 234x156 mm, kaal: 548 g, 96 Tables, black and white; 174 Illustrations, black and white
  • Ilmumisaeg: 21-May-2021
  • Kirjastus: CRC Press
  • ISBN-10: 1138196304
  • ISBN-13: 9781138196308
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781138196308.html
Märksõnad:
The present work is an introductory text in statistics, addressed to researchers and students in the field of material science. It aims to give the readers basic knowledge on how statistical reasoning is exploitable in this field, improving their knowledge of statistical tools and helping them to carry out statistical analyses and to interpret the results. It also focuses on establishing a consistent multivariate workflow starting from a correct design of experiment followed by a multivariate analysis process.
Preface iii
Acknowledgements vii
PART I STATISTICS BASICS
1 Statistics Basics
2(44)
1.1 Introduction
2(1)
1.1.1 Data-set PLA
2(1)
1.1.2 Data-set FGO
2(1)
1.2 Samples and Variables
3(3)
1.3 Errors
6(1)
1.4 Initial Data Analysis
7(6)
1.4.1 Significant Digits
7(1)
1.4.2 Stripcharts, Stem-and-leaf Displays, and Histograms
8(5)
1.5 Mode, Median. Mean, Variance, and Standard Deviation
13(8)
1.5.1 The Median
13(2)
1.5.2 Mode
15(1)
1.5.3 Mean
15(1)
1.5.4 A Visual Comparison of Mean, Median, and Mode
16(1)
1.5.5 The Range
17(1)
1.5.6 Quartile and Interquartile Range
17(1)
1.5.7 Variance
18(1)
1.5.8 The Standard Deviation
19(1)
1.5.9 Distributions
19(2)
1.6 Z-score
21(2)
1.6.1 Box and Whiskers Plot
21(2)
1.7 Error Propagation and Uncertainty
23(2)
1.8 Normality Tests
25(1)
1.9 Significance Tests
26(2)
1.9.1 Outliers
27(1)
1.9.2 Q-test
27(1)
1.9.3 Cochran Test
27(1)
1.10 T-test
28(2)
1.11 F-test
30(1)
1.12 One-way Analysis of Variance ANOVA
31(1)
1.13 Two-way Analysis of Variance ANOVA
32(3)
1.13.1 Two way ANOVA with Interaction
32(3)
1.14 Type I, II, and III Errors
35(1)
1.15 Bootstrap
36(1)
1.15.1 Two-sample Problems: Comparing Means or Median?
36(1)
1.16 An Example of Non-normal Distribution
37(2)
1.17 About Visual Representation of Data
39(1)
1.18 FAQ
39(4)
1.18.1 Additional Data-set and Exercises
40(1)
1.18.2 Remarks
41(1)
1.18.3 Suggested Essential Literature
41(2)
Bibliography
43(3)
PART II ESSENTIAL MULTIVARIATE STATISTICS
2 Design of Experiment
46(37)
2.1 Introduction
46(1)
2.2 Randomization
47(2)
2.3 Data-set OPT Cables
49(1)
2.4 One Variable at a Time Design
49(1)
2.5 Factorial Design
50(2)
2.6 Regression Model Representations
52(7)
2.6.1 Factorial Model Including Three Replicates in the Center
53(4)
2.6.2 Model with More than Two Levels for each Factor
57(2)
2.7 Data-set EMAGMA, An Example of DoE with Three Factors
59(9)
2.7.1 Workflow using OVAT
60(1)
2.7.2 Factorial Design 2s
61(5)
2.7.3 Factorial Design 2K
66(1)
2.7.4 Fractional Factorial Design 2k-1
66(1)
2.7.5 On Graphical Representation of Factorials with Four Factors
67(1)
2.8 Mixture Design
68(3)
2.8.1 Data Set HIPS
69(2)
2.9 Design of Experiments Matrix vs Real Experiments Performed
71(5)
2.9.1 Mixture Design in Constrained Region
71(1)
2.9.2 Data Set CPCB
71(5)
2.10 Other Designs
76(1)
2.11 FAQ
77(4)
2.11.1 Exercises
78(1)
2.11.2 Remarks
79(1)
2.11.3 Suggested Essential Literature
80(1)
Bibliography
81(2)
3 Pattern Recognition
83(46)
3.1 Introduction
83(1)
3.2 Variable Correlation
84(4)
3.2.1 Datasaurus
87(1)
3.3 Principal Component Analysis
88(39)
3.3.1 Centering and Scaling
90(1)
3.3.2 Algorithms for PCA
91(1)
3.3.3 Data-set ELE: Example of PCA Applied to a Data-set Obtained Via Electrophoresis Characterization
92(5)
3.3.4 Data-set ASPHALT: An Application of PCA to ATR-FTIR Spectroscopy
97(7)
3.3.5 Data-set PCAMIX: PCA Applied to Binary Chemical Mixtures at Trace Levels
104(4)
3.3.6 Cluster Analysis
108(4)
3.3.7 Dendrograms
112(1)
3.3.8 K-means Method
113(3)
3.3.9 Discriminant Analysis
116(1)
3.3.10 Soft Independent Modelling of Class Analogy
117(6)
3.3.11 Artificial Neural Networks
123(1)
3.3.12 Other Methodologies
124(1)
3.3.13 Q.A.
124(2)
3.3.14 Exercises
126(1)
3.3.15 Remarks
126(1)
3.3.16 Suggested Essential Literature
126(1)
Bibliography
127(2)
4 Calibration
129(23)
4.1 Introduction
129(1)
4.2 Univariate Calibration
129(2)
4.3 Univariate Calibration, Data-set Concrete
131(7)
4.3.1 Bivariate Models
132(6)
4.4 Multivariate Calibration
138(8)
4.4.1 Principal Component Regression
138(1)
4.4.2 An Example of Multivariate Regression using the Gasoline Data Set
139(3)
4.4.3 Partial Least Squares
142(4)
4.5 Other Regression Methodologies
146(5)
4.5.1 NWAY Methodologies
146(3)
4.5.2 A Short History of Partial Least Squares
149(1)
4.5.3 Q.A.
149(1)
4.5.4 Essential References
150(1)
Bibliography
151(1)
5 Case Studies
152(47)
5.1 Fast Fabrication of ZnO Superhydrophobic Surfaces without Chemical Post-treatment: Investigation of Important Parameters using Taguchi Mixed Level Design L8 (41 23)
153(1)
5.2 Introduction
153(1)
5.3 Materials and Methods
154(2)
5.3.1 Materials
154(1)
5.3.2 Design of Experiments (DOE)
155(1)
5.4 Sample Preparation
156(1)
5.4.1 Characterization
156(1)
5.5 Results and Discussion
156(8)
5.5.1 DOE Analysis
156(3)
5.5.2 XRD Results
159(1)
5.5.3 SEM Results
160(2)
5.5.4 ATR-FTIR Analysis
162(2)
5.6 Summary
164(1)
Bibliography
165(4)
5.7 An Example of Evolutionary Design of Experiment: Prediction of the Aging of Polymers
169(1)
5.8 Introduction
169(5)
5.8.1 Evolutionary Design of Experiment for Accelerated Aging Tests
171(3)
5.9 Prediction of Rubber Aging by Accelerated Aging Tests
174(4)
5.9.1 Successive Bayesian Estimation
177(1)
5.10 Results and Discussion
178(4)
5.11 Conclusions
182(2)
Bibliography
184(1)
5.12 Principal Component Analysis Applied to the Study of the Behavior of Steel Corrosion Inhibitors
185(1)
5.13 Introduction
185(1)
5.14 Materials and Methods
186(1)
5.14.1 Samples Preparation
186(1)
5.14.2 Chemical Speciation Equilibrium of Inhibitors
186(1)
5.15 Electrode Preparation
187(1)
5.16 Electrochemical Techniques
188(1)
5.16.1 Zero Current Potential and Potentiodynamic Polarisation Measurement
189(1)
5.17 Cyclic Voltammetry
189(1)
5.18 Data Management Multivariate Analysis
189(1)
5.19 Results and Discussion
189(3)
5.19.1 Open Circuit Potential (OCP) and Tafel Polarization Measurement
189(3)
5.20 Multivariate Analysis
192(4)
5.20.1 Principal Component Analysis
192(1)
5.20.2 Calibration-validation Test
192(1)
5.20.3 Cyclic Voltammetry Study
193(3)
5.21 Conclusions
196(1)
Bibliography
197(2)
Appendices
A Software Workflow
199(2)
A.1 Software
199(2)
Bibliography
201(52)
A.2
Chapter 1
202(10)
A.3
Chapter 2
212(12)
A.4
Chapter 3
224(13)
A.5
Chapter 4
237(14)
A.6 Appendix
251(1)
A.7 Plackett-Burman 16
252(1)
A.8 Statistical Tables
252(1)
B A Short Refresher of Matrix Algebra
253(6)
C Statistical Tables
259(7)
D Design of Experiment Tables
266(7)
D.1 Factorial Design
266(5)
D.2 Placket Burman
271(2)
Index 273
Giorgio Luciano is a researcher at the National Research Council of Italy at the "Giulio Natta" Institute of Chemical Sciences and Technologies (Scitec-Cnr). He started to be interested in multivariate statistics techniques applied to data from physic-chemical analysis methods 15 years ago during his Ph.D. and to apply to material science ever since. Enthusiast about programming in R and Python in order to write tools for everyday laboratory activities.