Chemometrics in Excel /
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| Main Author: | |
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| Corporate Author: | |
| Format: | Electronic eBook |
| Language: | English Russian |
| Published: |
Hoboken, New Jersey :
Wiley,
[2014]
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| Subjects: | |
| Online Access: | Connect to this title online (unlimited simultaneous users allowed; 325 uses per year) |
MARC
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| 035 | |a (DLC)EBC1680599 | ||
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| 041 | 1 | |a eng |h rus | |
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| 072 | 7 | |a SCI |x 013010 |2 bisacsh | |
| 082 | 0 | 0 | |a 543.0285/554 |2 23 |
| 100 | 1 | |a Pomerant︠s︡ev, A. L. |q (Alekseĭ Leonidovich), |d 1954- |e author. | |
| 240 | 1 | 0 | |a Khemometrika v Excel. |l English |
| 245 | 1 | 0 | |a Chemometrics in Excel / |c Alexey L. Pomerantsev. |
| 264 | 1 | |a Hoboken, New Jersey : |b Wiley, |c [2014] | |
| 300 | |a 1 online resource (xviii, 313 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 500 | |a Includes index. | ||
| 533 | |a Electronic reproduction. |b Perth, W.A. |n Available via World Wide Web. | ||
| 546 | |a Translated from the Russian. | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on June 9, 2014). | ||
| 505 | 0 | 0 | |a Machine generated contents note: |g 1. |t What is Chemometrics? -- |g 1.1. |t Subject of Chemometrics, -- |g 1.2. |t Historical Digression, -- |g 2. |t What the Book Is About? -- |g 2.1. |t Useful Hints, -- |g 2.2. |t Book Syllabus, -- |g 2.3. |t Notations, -- |g 3. |t Installation of Chemometrics Add-In -- |g 3.1. |t Installation, -- |g 3.2. |t General Information, -- |g 4. |t Further Reading on Chemometrics -- |g 4.1. |t Books, -- |g 4.1.1. |t Basics, -- |g 4.1.2. |t Chemometrics, -- |g 4.1.3. |t Supplements, -- |g 4.2. |t Internet, -- |g 4.2.1. |t Tutorials, -- |g 4.3. |t Journals, -- |g 4.3.1. |t Chemometrics, -- |g 4.3.2. |t Analytical, -- |g 4.3.3. |t Mathematical, -- |g 4.4. |t Software, -- |g 4.4.1. |t Specialized Packages, -- |g 4.4.2. |t General Statistic Packages, -- |g 4.4.3. |t Free Ware, -- |g 5. |t Matrices and Vectors -- |g 5.1. |t Basics, -- |g 5.1.1. |t Matrix, -- |g 5.1.2. |t Simple Matrix Operations, -- |g 5.1.3. |t Matrices Multiplication, -- |g 5.1.4. |t Square Matrix, -- |g 5.1.5. |t Trace and Determinant, -- |g 5.1.6. |t Vectors, -- |g 5.1.7. |t Simple Vector Operations, -- |g 5.1.8. |t Vector Products, -- |g 5.1.9. |t Vector Norm, -- |g 5.1.10. |t Angle Between Vectors, -- |g 5.1.11. |t Vector Representation of a Matrix, -- |g 5.1.12. |t Linearly Dependent Vectors, -- |g 5.1.13. |t Matrix Rank, -- |g 5.1.14. |t Inverse Matrix, -- |g 5.1.15. |t Pseudoinverse, -- |g 5.1.16. |t Matrix-Vector Product, -- |g 5.2. |t Advanced Information, -- |g 5.2.1. |t Systems of Linear Equations, -- |g 5.2.2. |t Bilinear and Quadratic Forms, -- |g 5.2.3. |t Positive Definite Matrix, -- |g 5.2.4. |t Cholesky Decomposition, -- |g 5.2.5. |t Polar Decomposition, -- |g 5.2.6. |t Eigenvalues and Eigenvectors, -- |g 5.2.7. |t Eigenvalues, -- |g 5.2.8. |t Eigenvectors, -- |g 5.2.9. |t Equivalence and Similarity, -- |g 5.2.10. |t Diagonalization, -- |g 5.2.11. |t Singular Value Decomposition (SVD), -- |g 5.2.12. |t Vector Space, -- |g 5.2.13. |t Space Basis, -- |g 5.2.14. |t Geometric Interpretation, -- |g 5.2.15. |t Nonuniqueness of Basis, -- |g 5.2.16. |t Subspace, -- |g 5.2.17. |t Projection, -- |g 6. |t Statistics -- |g 6.1. |t Basics, -- |g 6.1.1. |t Probability, -- |g 6.1.2. |t Random Value, -- |g 6.1.3. |t Distribution Function, -- |g 6.1.4. |t Mathematical Expectation, -- |g 6.1.5. |t Variance and Standard Deviation, -- |g 6.1.6. |t Moments, -- |g 6.1.7. |t Quantiles, -- |g 6.1.8. |t Multivariate Distributions, -- |g 6.1.9. |t Covariance and Correlation, -- |g 6.1.10. |t Function, -- |g 6.1.11. |t Standardization, -- |g 6.2. |t Main Distributions, -- |g 6.2.1. |t Binomial Distribution, -- |g 6.2.2. |t Uniform Distribution, -- |g 6.2.3. |t Normal Distribution, -- |g 6.2.4. |t Chi-Squared Distribution, -- |g 6.2.5. |t Student's Distribution, -- |g 6.2.6. |t F-Distribution, -- |g 6.2.7. |t Multivariate Normal Distribution, -- |g 6.2.8. |t Pseudorandom Numbers, -- |g 6.3. |t Parameter Estimation, -- |g 6.3.1. |t Sample, -- |g 6.3.2. |t Outliers and Extremes, -- |g 6.3.3. |t Statistical Population, -- |g 6.3.4. |t Statistics, -- |g 6.3.5. |t Sample Mean and Variance, -- |g 6.3.6. |t Sample Covariance and Correlation, -- |g 6.3.7. |t Order Statistics, -- |g 6.3.8. |t Empirical Distribution and Histogram, -- |g 6.3.9. |t Method of Moments, -- |g 6.3.10. |t Maximum Likelihood Method, -- |g 6.4. |t Properties of the Estimators, -- |g 6.4.1. |t Consistency, -- |g 6.4.2. |t Bias, -- |g 6.4.3. |t Effectiveness, -- |g 6.4.4. |t Robustness, -- |g 6.4.5. |t Normal Sample, -- |g 6.5. |t Confidence Estimation, -- |g 6.5.1. |t Confidence Region, -- |g 6.5.2. |t Confidence Interval, -- |g 6.5.3. |t Example of a Confidence Interval, -- |g 6.5.4. |t Confidence Intervals for the Normal Distribution, -- |g 6.6. |t Hypothesis Testing, -- |g 6.6.1. |t Hypothesis, -- |g 6.6.2. |t Hypothesis Testing, -- |g 6.6.3. |t Type I and Type II Errors, -- |g 6.6.4. |t Example, -- |g 6.6.5. |t Pearson's Chi-Squared Test, -- |g 6.6.6. |t F-Test, -- |g 6.7. |t Regression, -- |g 6.7.1. |t Simple Regression, -- |g 6.7.2. |t Least Squares Method, -- |g 6.7.3. |t Multiple Regression, -- |t Conclusion, -- |g 7. |t Matrix Calculations in Excel -- |g 7.1. |t Basic Information, -- |g 7.1.1. |t Region and Language, -- |g 7.1.2. |t Workbook, Worksheet, and Cell, -- |g 7.1.3. |t Addressing, -- |g 7.1.4. |t Range, -- |g 7.1.5. |t Simple Calculations, -- |g 7.1.6. |t Functions, -- |g 7.1.7. |t Important Functions, -- |g 7.1.8. |t Errors in Formulas, -- |g 7.1.9. |t Formula Dragging, -- |g 7.1.10. |t Create a Chart, -- |g 7.2. |t Matrix Operations, -- |g 7.2.1. |t Array Formulas, -- |g 7.2.2. |t Creating and Editing an Array Formula, -- |g 7.2.3. |t Simplest Matrix Operations, -- |g 7.2.4. |t Access to the Part of a Matrix, -- |g 7.2.5. |t Unary Operations, -- |g 7.2.6. |t Binary Operations, -- |g 7.2.7. |t Regression, -- |g 7.2.8. |t Critical Bug in Excel 2003, -- |g 7.2.9. |t Virtual Array, -- |g 7.3. |t Extension of Excel Possibilities, -- |g 7.3.1. |t VBA Programming, -- |g 7.3.2. |t Example, -- |g 7.3.3. |t Macro Example, -- |g 7.3.4. |t User-Defined Function Example, -- |g 7.3.5. |t Add-Ins, -- |g 7.3.6. |t Add-In Installation, -- |t Conclusion, -- |g 8. |t Projection Methods in Excel -- |g 8.1. |t Projection Methods, -- |g 8.1.1. |t Concept and Notation, -- |g 8.1.2. |t PCA, -- |g 8.1.3. |t PLS, -- |g 8.1.4. |t Data Preprocessing, -- |g 8.1.5. |t Didactic Example, -- |g 8.2. |t Application of Chemometrics Add-In, -- |g 8.2.1. |t Installation, -- |g 8.2.2. |t General, -- |g 8.3. |t PCA, -- |g 8.3.1. |t ScoresPCA, -- |g 8.3.2. |t LoadingsPCA, -- |g 8.4. |t PLS, -- |g 8.4.1. |t ScoresPLS, -- |g 8.4.2. |t UScoresPLS, -- |g 8.4.3. |t LoadingsPLS, -- |g 8.4.4. |t WLoadingsPLS, -- |g 8.4.5. |t QLoadingsPLS, -- |g 8.5. |t PLS2, -- |g 8.5.1. |t ScoresPLS2, -- |g 8.5.2. |t UScoresPLS2, -- |g 8.5.3. |t LoadingsPLS2, -- |g 8.5.4. |t WLoadingsPLS2, -- |g 8.5.5. |t QLoadingsPLS2, -- |g 8.6. |t Additional Functions, -- |g 8.6.1. |t MIdent, -- |g 8.6.2. |t MIdentD2, -- |g 8.6.3. |t MCutRows, -- |g 8.6.4. |t MTrace, -- |t Conclusion, -- |g 9. |t Principal Component Analysis (PCA) -- |g 9.1. |t Basics, -- |g 9.1.1. |t Data, -- |g 9.1.2. |t Intuitive Approach, -- |g 9.1.3. |t Dimensionality Reduction, -- |g 9.2. |t Principal Component Analysis, -- |g 9.2.1. |t Formal Specifications, -- |g 9.2.2. |t Algorithm, -- |g 9.2.3. |t PCA and SVD, -- |g 9.2.4. |t Scores, -- |g 9.2.5. |t Loadings, -- |g 9.2.6. |t Data of Special Kind, -- |g 9.2.7. |t Errors, -- |g 9.2.8. |t Validation, -- |g 9.2.9. |t Decomposition "Quality", -- |g 9.2.10. |t Number of Principal Components, -- |g 9.2.11. |t Ambiguity of PCA, -- |g 9.2.12. |t Data Preprocessing, -- |g 9.2.13. |t Leverage and Deviation, -- |g 9.3. |t People and Countries, -- |g 9.3.1. |t Example, -- |g 9.3.2. |t Data, -- |g 9.3.3. |t Data Exploration, -- |g 9.3.4. |t Data Pretreatment, -- |g 9.3.5. |t Scores and Loadings Calculation, -- |g 9.3.6. |t Scores Plots, -- |g 9.3.7. |t Loadings Plot, -- |g 9.3.8. |t Analysis of Residuals, -- |t Conclusion, -- |g 10. |t Calibration -- |g 10.1. |t Basics, -- |g 10.1.1. |t Problem Statement, -- |g 10.1.2. |t Linear and Nonlinear Calibration, -- |g 10.1.3. |t Calibration and Validation, -- |g 10.1.4. |t Calibration "Quality", -- |g 10.1.5. |t Uncertainty, Precision, and Accuracy, -- |g 10.1.6. |t Underfitting and Overfitting, -- |g 10.1.7. |t Multicollinearity, -- |g 10.1.8. |t Data Preprocessing, -- |g 10.2. |t Simulated Data, -- |g 10.2.1. |t Principle of Linearity, -- |g 10.2.2. |t "Pure" Spectra, -- |g 10.2.3. |t "Standard" Samples, -- |g 10.2.4. |t X Data Creation, -- |g 10.2.5. |t Data Centering, -- |g 10.2.6. |t Data Overview, -- |g 10.3. |t Classic Calibration, -- |g 10.3.1. |t Univariate (Single Channel) Calibration, -- |g 10.3.2. |t Vierordt Method, -- |g 10.3.3. |t Indirect Calibration, -- |g 10.4. |t Inverse Calibration, -- |g 10.4.1. |t Multiple Linear Calibration, -- |g 10.4.2. |t Stepwise Calibration, -- |g 10.5. |t Latent Variables Calibration, -- |g 10.5.1. |t Projection Methods, -- |g 10.5.2. |t Latent Variables Regression, -- |g 10.5.3. |t Implementation of Latent Variable Calibration, -- |g 10.5.4. |t Principal Component Regression (PCR), -- |g 10.5.5. |t Projection on the Latent Structures-1 (PLS1), -- |g 10.5.6. |t Projection on the Latent Structures-2 (PLS2), -- |g 10.6. |t Methods Comparison, -- |t Conclusion, -- |g 11. |t Classification -- |g 11.1. |t Basics, -- |g 11.1.1. |t Problem Statement, -- |g 11.1.2. |t Types of Classes, -- |g 11.1.3. |t Hypothesis Testing, -- |g 11.1.4. |t Errors in Classification, -- |g 11.1.5. |t One-Class Classification, -- |g 11.1.6. |t Training and Validation, -- |g 11.1.7. |t Supervised and Unsupervised Training, -- |g 11.1.8. |t Curse of Dimensionality, -- |g 11.1.9. |t Data Preprocessing, -- |g 11.2. |t Data, -- |g 11.2.1. |t Example, -- |g 11.2.2. |t Data Subsets, -- |g 11.2.3. |t Workbook Iris.xls, -- |g 11.2.4. |t Principal Component Analysis, -- |g 11.3. |t Supervised Classification, -- |g 11.3.1. |t Linear Discriminant Analysis (LDA), -- |g 11.3.2. |t Quadratic Discriminant Analysis (QDA), -- |g 11.3.3. |t PLS Discriminant Analysis (PLSDA), -- |g 11.3.4. |t SIMCA, -- |g 11.3.5. |t k-Nearest Neighbors (kNN), -- |g 11.4. |t Unsupervised Classification, -- |g 11.4.1. |t PCA Again (Revisited), -- |g 11.4.2. |t Clustering by K-Means, -- |t Conclusion, -- |g 12. |t Multivariate Curve Resolution -- |g 12.1. |t Basics, -- |g 12.1.1. |t Problem Statement, -- |g 12.1.2. |t Solution Ambiguity, -- |g 12.1.3. |t Solvability Conditions, -- |g 12.1.4. |t Two Types of Data, -- |g 12.1.5. |t Known Spectrum or Profile, -- |g 12.1.6. |t Principal Component Analysis (PCA), -- |g 12.1.7. |t PCA and MCR, -- |g 12.2. |t Simulated Data, -- |g 12.2.1. |t Example, -- |g 12.2.2. |t Data, -- |g 12.2.3. |t PCA, -- |g 12.2.4. |t HELP Plot, -- |g 12.3. |t FActor Analysis, -- |g 12.3.1. |t Procrustes Analysis, -- |g 12.3.2. |t Evolving Factor Analysis (EFA), -- |g 12.3.3. |t Windows Factor Analysis (WFA), -- |g 12.4. |t Iterative Methods, -- |g 12.4.1. |t Iterative Target Transform Factor Analysis (ITTFA), -- |g 12.4.2. |t Alternating Least Squares (ALS), -- |t Conclusion, -- |g 13. |t Extension Of Chemometrics Add-In -- |g 13.1. |t Using Virtual Arrays, -- |g 13.1.1. |t Simulated Data, -- |g 13.1.2. |t Virtual Array, -- |g 13.1.3. |t Data Preprocessing, -- |g 13.1.4. |t Decomposition, -- |g 13.1.5. |t Residuals Calculation, -- |g 13.1.6. |t Eigenvalues Calculation, -- |g 13.1.7. |t Orthogonal Distances Calculation, -- |g 13.1.8. |t Leverages Calculation, -- |g 13.2. |t Using VBA Programming, -- |g 13.2.1. |t VBA Advantages, -- |g 13.2.2. |t Virtualization of Real Arrays, -- |g 13.2.3. |t Data Preprocessing, -- |g 13.2.4. |t Residuals Calculation, -- |g 13.2.5. |t Eigenvalues Calculation, -- |
| 505 | 0 | 0 | |a Contents note continued: |g 13.2.6. |t Orthogonal Distances Calculation, -- |g 13.2.7. |t Leverages Calculation, -- |t Conclusion, -- |g 14. |t Kinetic Modeling of Spectral Data -- |g 14.1. |t "Grey" Modeling Method, -- |g 14.1.1. |t Problem Statement, -- |g 14.1.2. |t Example, -- |g 14.1.3. |t Data, -- |g 14.1.4. |t Soft Method of Alternating Least Squares (Soft-ALS), -- |g 14.1.5. |t Hard Method of Alternating Least Squares (Hard-ALS), -- |g 14.1.6. |t Using Solver Add-In, -- |t Conclusions, -- |g 15. |t MATLAB®: Beginner's Guide -- |g 15.1. |t Basics, -- |g 15.1.1. |t Workspace, -- |g 15.1.2. |t Basic Calculations, -- |g 15.1.3. |t Echo, -- |g 15.1.4. |t Workspace Saving: MAT-Files, -- |g 15.1.5. |t Diary, -- |g 15.1.6. |t Help, -- |g 15.2. |t Matrices, -- |g 15.2.1. |t Scalars, Vectors, and Matrices, -- |g 15.2.2. |t Accessing Matrix Elements, -- |g 15.2.3. |t Basic Matrix Operations, -- |g 15.2.4. |t Special Matrices, -- |g 15.2.5. |t Matrix Calculations, -- |g 15.3. |t Integrating Excel and MATLAB®, -- |g 15.3.1. |t Configuring Excel, -- |g 15.3.2. |t Data Exchange, -- |g 15.4. |t Programming, -- |g 15.4.1. |t M-Files, -- |g 15.4.2. |t Script File, -- |g 15.4.3. |t Function File, -- |g 15.4.4. |t Plotting, -- |g 15.4.5. |t Plot Printing, -- |g 15.5. |t Sample Programs, -- |g 15.5.1. |t Centering and Scaling, -- |g 15.5.2. |t SVD/PCA, -- |g 15.5.3. |t PCANTPALS, -- |g 15.5.4. |t PLS1, -- |g 15.5.5. |t PLS2, -- |t Conclusion,. |
| 630 | 0 | 0 | |a Microsoft Excel (Computer file) |
| 650 | 0 | |a Chemometrics |x Data processing. | |
| 710 | 2 | |a Ebooks Corporation | |
| 776 | 0 | 8 | |i Print version: |a Pomerant︠s︡ev, A. L. (Alekseĭ Leonidovich), 1954- |t Chemometrics in Excel |d Hoboken, New Jersey : John Wiley & Sons, [2014] |z 9781118605356 |w (DLC) 2013046049 |
| 856 | 4 | 0 | |u https://ebookcentral.proquest.com/lib/santaclara/detail.action?docID=1680599 |z Connect to this title online (unlimited simultaneous users allowed; 325 uses per year) |t 1 |
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| 998 | |a uww |b |c m |d z |e y |f eng |g nju |h 0 | ||
| 919 | |a .ulebk |b 2014-10-15 | ||
| 915 | |a YBP DDA - Also in ProQuest Academic Complete | ||
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