Chemometrics in Excel

Chemometrics in Excel /

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

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