{"product_id":"chemometrics-in-excel-9781118605356","title":"Chemometrics in Excel","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eProviding an explanation of the fundamentals, methods, and applications of chemometrics, this title acts as a practical guide to multivariate data analysis techniques. It explains the methods used in Chemometrics and teaches the reader to perform all relevant calculations. It presents the basic chemometric methods as worksheet functions in Excel.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“The book is for sure very interesting and very well written, and it covers all the major topics of chemometrics.”  (\u003ci\u003eJournal of Chemometrics\u003c\/i\u003e, 14 July 2015)\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I INTRODUCTION 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 What is Chemometrics? 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Subject of Chemometrics, 3\u003c\/p\u003e \u003cp\u003e1.2 Historical Digression, 5\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 What the Book Is About? 8\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Useful Hints, 8\u003c\/p\u003e \u003cp\u003e2.2 Book Syllabus, 9\u003c\/p\u003e \u003cp\u003e2.3 Notations, 10\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Installation of Chemometrics Add-In 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Installation, 11\u003c\/p\u003e \u003cp\u003e3.2 General Information, 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Further Reading on Chemometrics 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Books, 15\u003c\/p\u003e \u003cp\u003e4.1.1 The Basics, 15\u003c\/p\u003e \u003cp\u003e4.1.2 Chemometrics, 16\u003c\/p\u003e \u003cp\u003e4.1.3 Supplements, 16\u003c\/p\u003e \u003cp\u003e4.2 The Internet, 17\u003c\/p\u003e \u003cp\u003e4.2.1 Tutorials, 17\u003c\/p\u003e \u003cp\u003e4.3 Journals, 17\u003c\/p\u003e \u003cp\u003e4.3.1 Chemometrics, 17\u003c\/p\u003e \u003cp\u003e4.3.2 Analytical, 18\u003c\/p\u003e \u003cp\u003e4.3.3 Mathematical, 18\u003c\/p\u003e \u003cp\u003e4.4 Software, 18\u003c\/p\u003e \u003cp\u003e4.4.1 Specialized Packages, 18\u003c\/p\u003e \u003cp\u003e4.4.2 General Statistic Packages, 19\u003c\/p\u003e \u003cp\u003e4.4.3 Free Ware, 19\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II THE BASICS 21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Matrices and Vectors 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 The Basics, 23\u003c\/p\u003e \u003cp\u003e5.1.1 Matrix, 23\u003c\/p\u003e \u003cp\u003e5.1.2 Simple Matrix Operations, 24\u003c\/p\u003e \u003cp\u003e5.1.3 Matrices Multiplication, 25\u003c\/p\u003e \u003cp\u003e5.1.4 Square Matrix, 26\u003c\/p\u003e \u003cp\u003e5.1.5 Trace and Determinant, 27\u003c\/p\u003e \u003cp\u003e5.1.6 Vectors, 28\u003c\/p\u003e \u003cp\u003e5.1.7 Simple Vector Operations, 29\u003c\/p\u003e \u003cp\u003e5.1.8 Vector Products, 29\u003c\/p\u003e \u003cp\u003e5.1.9 Vector Norm, 30\u003c\/p\u003e \u003cp\u003e5.1.10 Angle Between Vectors, 30\u003c\/p\u003e \u003cp\u003e5.1.11 Vector Representation of a Matrix, 30\u003c\/p\u003e \u003cp\u003e5.1.12 Linearly Dependent Vectors, 31\u003c\/p\u003e \u003cp\u003e5.1.13 Matrix Rank, 31\u003c\/p\u003e \u003cp\u003e5.1.14 Inverse Matrix, 31\u003c\/p\u003e \u003cp\u003e5.1.15 Pseudoinverse, 32\u003c\/p\u003e \u003cp\u003e5.1.16 Matrix–Vector Product, 33\u003c\/p\u003e \u003cp\u003e5.2 Advanced Information, 33\u003c\/p\u003e \u003cp\u003e5.2.1 Systems of Linear Equations, 33\u003c\/p\u003e \u003cp\u003e5.2.2 Bilinear and Quadratic Forms, 34\u003c\/p\u003e \u003cp\u003e5.2.3 Positive Definite Matrix, 34\u003c\/p\u003e \u003cp\u003e5.2.4 Cholesky Decomposition, 34\u003c\/p\u003e \u003cp\u003e5.2.5 Polar Decomposition, 34\u003c\/p\u003e \u003cp\u003e5.2.6 Eigenvalues and Eigenvectors, 35\u003c\/p\u003e \u003cp\u003e5.2.7 Eigenvalues, 35\u003c\/p\u003e \u003cp\u003e5.2.8 Eigenvectors, 35\u003c\/p\u003e \u003cp\u003e5.2.9 Equivalence and Similarity, 36\u003c\/p\u003e \u003cp\u003e5.2.10 Diagonalization, 37\u003c\/p\u003e \u003cp\u003e5.2.11 Singular Value Decomposition (SVD), 37\u003c\/p\u003e \u003cp\u003e5.2.12 Vector Space, 38\u003c\/p\u003e \u003cp\u003e5.2.13 Space Basis, 39\u003c\/p\u003e \u003cp\u003e5.2.14 Geometric Interpretation, 39\u003c\/p\u003e \u003cp\u003e5.2.15 Nonuniqueness of Basis, 39\u003c\/p\u003e \u003cp\u003e5.2.16 Subspace, 40\u003c\/p\u003e \u003cp\u003e5.2.17 Projection, 40\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Statistics 42\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 The Basics, 42\u003c\/p\u003e \u003cp\u003e6.1.1 Probability, 42\u003c\/p\u003e \u003cp\u003e6.1.2 Random Value, 43\u003c\/p\u003e \u003cp\u003e6.1.3 Distribution Function, 43\u003c\/p\u003e \u003cp\u003e6.1.4 Mathematical Expectation, 44\u003c\/p\u003e \u003cp\u003e6.1.5 Variance and Standard Deviation, 44\u003c\/p\u003e \u003cp\u003e6.1.6 Moments, 44\u003c\/p\u003e \u003cp\u003e6.1.7 Quantiles, 45\u003c\/p\u003e \u003cp\u003e6.1.8 Multivariate Distributions, 45\u003c\/p\u003e \u003cp\u003e6.1.9 Covariance and Correlation, 45\u003c\/p\u003e \u003cp\u003e6.1.10 Function, 46\u003c\/p\u003e \u003cp\u003e6.1.11 Standardization, 46\u003c\/p\u003e \u003cp\u003e6.2 Main Distributions, 46\u003c\/p\u003e \u003cp\u003e6.2.1 Binomial Distribution, 46\u003c\/p\u003e \u003cp\u003e6.2.2 Uniform Distribution, 47\u003c\/p\u003e \u003cp\u003e6.2.3 Normal Distribution, 48\u003c\/p\u003e \u003cp\u003e6.2.4 Chi-Squared Distribution, 50\u003c\/p\u003e \u003cp\u003e6.2.5 Student’s Distribution, 52\u003c\/p\u003e \u003cp\u003e6.2.6 F-Distribution, 53\u003c\/p\u003e \u003cp\u003e6.2.7 Multivariate Normal Distribution, 54\u003c\/p\u003e \u003cp\u003e6.2.8 Pseudorandom Numbers, 55\u003c\/p\u003e \u003cp\u003e6.3 Parameter Estimation, 56\u003c\/p\u003e \u003cp\u003e6.3.1 Sample, 56\u003c\/p\u003e \u003cp\u003e6.3.2 Outliers and Extremes, 56\u003c\/p\u003e \u003cp\u003e6.3.3 Statistical Population, 56\u003c\/p\u003e \u003cp\u003e6.3.4 Statistics, 57\u003c\/p\u003e \u003cp\u003e6.3.5 Sample Mean and Variance, 57\u003c\/p\u003e \u003cp\u003e6.3.6 Sample Covariance and Correlation, 58\u003c\/p\u003e \u003cp\u003e6.3.7 Order Statistics, 59\u003c\/p\u003e \u003cp\u003e6.3.8 Empirical Distribution and Histogram, 60\u003c\/p\u003e \u003cp\u003e6.3.9 Method of Moments, 61\u003c\/p\u003e \u003cp\u003e6.3.10 The Maximum Likelihood Method, 62\u003c\/p\u003e \u003cp\u003e6.4 Properties of the Estimators, 62\u003c\/p\u003e \u003cp\u003e6.4.1 Consistency, 62\u003c\/p\u003e \u003cp\u003e6.4.2 Bias, 63\u003c\/p\u003e \u003cp\u003e6.4.3 Effectiveness, 63\u003c\/p\u003e \u003cp\u003e6.4.4 Robustness, 63\u003c\/p\u003e \u003cp\u003e6.4.5 Normal Sample, 64\u003c\/p\u003e \u003cp\u003e6.5 Confidence Estimation, 64\u003c\/p\u003e \u003cp\u003e6.5.1 Confidence Region, 64\u003c\/p\u003e \u003cp\u003e6.5.2 Confidence Interval, 65\u003c\/p\u003e \u003cp\u003e6.5.3 Example of a Confidence Interval, 65\u003c\/p\u003e \u003cp\u003e6.5.4 Confidence Intervals for the Normal Distribution, 65\u003c\/p\u003e \u003cp\u003e6.6 Hypothesis Testing, 66\u003c\/p\u003e \u003cp\u003e6.6.1 Hypothesis, 66\u003c\/p\u003e \u003cp\u003e6.6.2 Hypothesis Testing, 66\u003c\/p\u003e \u003cp\u003e6.6.3 Type I and Type II Errors, 67\u003c\/p\u003e \u003cp\u003e6.6.4 Example, 67\u003c\/p\u003e \u003cp\u003e6.6.5 Pearson’s Chi-Squared Test, 67\u003c\/p\u003e \u003cp\u003e6.6.6 F-Test, 69\u003c\/p\u003e \u003cp\u003e6.7 Regression, 70\u003c\/p\u003e \u003cp\u003e6.7.1 Simple Regression, 70\u003c\/p\u003e \u003cp\u003e6.7.2 The Least Squares Method, 71\u003c\/p\u003e \u003cp\u003e6.7.3 Multiple Regression, 72\u003c\/p\u003e \u003cp\u003eConclusion, 73\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Matrix Calculations in Excel 74\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Basic Information, 74\u003c\/p\u003e \u003cp\u003e7.1.1 Region and Language, 74\u003c\/p\u003e \u003cp\u003e7.1.2 Workbook, Worksheet, and Cell, 76\u003c\/p\u003e \u003cp\u003e7.1.3 Addressing, 77\u003c\/p\u003e \u003cp\u003e7.1.4 Range, 78\u003c\/p\u003e \u003cp\u003e7.1.5 Simple Calculations, 78\u003c\/p\u003e \u003cp\u003e7.1.6 Functions, 78\u003c\/p\u003e \u003cp\u003e7.1.7 Important Functions, 81\u003c\/p\u003e \u003cp\u003e7.1.8 Errors in Formulas, 85\u003c\/p\u003e \u003cp\u003e7.1.9 Formula Dragging, 86\u003c\/p\u003e \u003cp\u003e7.1.10 Create a Chart, 87\u003c\/p\u003e \u003cp\u003e7.2 Matrix Operations, 88\u003c\/p\u003e \u003cp\u003e7.2.1 Array Formulas, 88\u003c\/p\u003e \u003cp\u003e7.2.2 Creating and Editing an Array Formula, 90\u003c\/p\u003e \u003cp\u003e7.2.3 Simplest Matrix Operations, 91\u003c\/p\u003e \u003cp\u003e7.2.4 Access to the Part of a Matrix, 91\u003c\/p\u003e \u003cp\u003e7.2.5 Unary Operations, 93\u003c\/p\u003e \u003cp\u003e7.2.6 Binary Operations, 95\u003c\/p\u003e \u003cp\u003e7.2.7 Regression, 95\u003c\/p\u003e \u003cp\u003e7.2.8 Critical Bug in Excel 2003, 99\u003c\/p\u003e \u003cp\u003e7.2.9 Virtual Array, 99\u003c\/p\u003e \u003cp\u003e7.3 Extension of Excel Possibilities, 100\u003c\/p\u003e \u003cp\u003e7.3.1 VBA Programming, 100\u003c\/p\u003e \u003cp\u003e7.3.2 Example, 101\u003c\/p\u003e \u003cp\u003e7.3.3 Macro Example, 103\u003c\/p\u003e \u003cp\u003e7.3.4 User-Defined Function Example, 104\u003c\/p\u003e \u003cp\u003e7.3.5 Add-Ins, 105\u003c\/p\u003e \u003cp\u003e7.3.6 Add-In Installation, 106\u003c\/p\u003e \u003cp\u003eConclusion, 107\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Projection Methods in Excel 108\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Projection Methods, 108\u003c\/p\u003e \u003cp\u003e8.1.1 Concept and Notation, 108\u003c\/p\u003e \u003cp\u003e8.1.2 PCA, 109\u003c\/p\u003e \u003cp\u003e8.1.3 PLS, 110\u003c\/p\u003e \u003cp\u003e8.1.4 Data Preprocessing, 111\u003c\/p\u003e \u003cp\u003e8.1.5 Didactic Example, 112\u003c\/p\u003e \u003cp\u003e8.2 Application of Chemometrics Add-In, 113\u003c\/p\u003e \u003cp\u003e8.2.1 Installation, 113\u003c\/p\u003e \u003cp\u003e8.2.2 General, 113\u003c\/p\u003e \u003cp\u003e8.3 PCA, 114\u003c\/p\u003e \u003cp\u003e8.3.1 ScoresPCA, 114\u003c\/p\u003e \u003cp\u003e8.3.2 LoadingsPCA, 114\u003c\/p\u003e \u003cp\u003e8.4 PLS, 116\u003c\/p\u003e \u003cp\u003e8.4.1 ScoresPLS, 116\u003c\/p\u003e \u003cp\u003e8.4.2 UScoresPLS, 117\u003c\/p\u003e \u003cp\u003e8.4.3 LoadingsPLS, 118\u003c\/p\u003e \u003cp\u003e8.4.4 WLoadingsPLS, 119\u003c\/p\u003e \u003cp\u003e8.4.5 QLoadingsPLS, 120\u003c\/p\u003e \u003cp\u003e8.5 PLS2, 121\u003c\/p\u003e \u003cp\u003e8.5.1 ScoresPLS2, 121\u003c\/p\u003e \u003cp\u003e8.5.2 UScoresPLS2, 122\u003c\/p\u003e \u003cp\u003e8.5.3 LoadingsPLS2, 124\u003c\/p\u003e \u003cp\u003e8.5.4 WLoadingsPLS2, 125\u003c\/p\u003e \u003cp\u003e8.5.5 QLoadingsPLS2, 126\u003c\/p\u003e \u003cp\u003e8.6 Additional Functions, 127\u003c\/p\u003e \u003cp\u003e8.6.1 MIdent, 127\u003c\/p\u003e \u003cp\u003e8.6.2 MIdentD2, 127\u003c\/p\u003e \u003cp\u003e8.6.3 MCutRows, 129\u003c\/p\u003e \u003cp\u003e8.6.4 MTrace, 129\u003c\/p\u003e \u003cp\u003eConclusion, 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART IIICHEMOMETRICS 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Principal Component Analysis (PCA) 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 The Basics, 133\u003c\/p\u003e \u003cp\u003e9.1.1 Data, 133\u003c\/p\u003e \u003cp\u003e9.1.2 Intuitive Approach, 134\u003c\/p\u003e \u003cp\u003e9.1.3 Dimensionality Reduction, 136\u003c\/p\u003e \u003cp\u003e9.2 Principal Component Analysis, 136\u003c\/p\u003e \u003cp\u003e9.2.1 Formal Specifications, 136\u003c\/p\u003e \u003cp\u003e9.2.2 Algorithm, 137\u003c\/p\u003e \u003cp\u003e9.2.3 PCA and SVD, 137\u003c\/p\u003e \u003cp\u003e9.2.4 Scores, 138\u003c\/p\u003e \u003cp\u003e9.2.5 Loadings, 139\u003c\/p\u003e \u003cp\u003e9.2.6 Data of Special Kind, 140\u003c\/p\u003e \u003cp\u003e9.2.7 Errors, 140\u003c\/p\u003e \u003cp\u003e9.2.8 Validation, 143\u003c\/p\u003e \u003cp\u003e9.2.9 Decomposition “Quality”, 143\u003c\/p\u003e \u003cp\u003e9.2.10 Number of Principal Components, 144\u003c\/p\u003e \u003cp\u003e9.2.11 The Ambiguity of PCA, 145\u003c\/p\u003e \u003cp\u003e9.2.12 Data Preprocessing, 146\u003c\/p\u003e \u003cp\u003e9.2.13 Leverage and Deviation, 146\u003c\/p\u003e \u003cp\u003e9.3 People and Countries, 146\u003c\/p\u003e \u003cp\u003e9.3.1 Example, 146\u003c\/p\u003e \u003cp\u003e9.3.2 Data, 147\u003c\/p\u003e \u003cp\u003e9.3.3 Data Exploration, 147\u003c\/p\u003e \u003cp\u003e9.3.4 Data Pretreatment, 148\u003c\/p\u003e \u003cp\u003e9.3.5 Scores and Loadings Calculation, 149\u003c\/p\u003e \u003cp\u003e9.3.6 Scores Plots, 151\u003c\/p\u003e \u003cp\u003e9.3.7 Loadings Plot, 152\u003c\/p\u003e \u003cp\u003e9.3.8 Analysis of Residuals, 153\u003c\/p\u003e \u003cp\u003eConclusion, 153\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Calibration 156\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 The Basics, 156\u003c\/p\u003e \u003cp\u003e10.1.1 Problem Statement, 156\u003c\/p\u003e \u003cp\u003e10.1.2 Linear and Nonlinear Calibration, 157\u003c\/p\u003e \u003cp\u003e10.1.3 Calibration and Validation, 158\u003c\/p\u003e \u003cp\u003e10.1.4 Calibration “Quality”, 160\u003c\/p\u003e \u003cp\u003e10.1.5 Uncertainty, Precision, and Accuracy, 162\u003c\/p\u003e \u003cp\u003e10.1.6 Underfitting and Overfitting, 163\u003c\/p\u003e \u003cp\u003e10.1.7 Multicollinearity, 164\u003c\/p\u003e \u003cp\u003e10.1.8 Data Preprocessing, 166\u003c\/p\u003e \u003cp\u003e10.2 Simulated Data, 166\u003c\/p\u003e \u003cp\u003e10.2.1 The Principle of Linearity, 166\u003c\/p\u003e \u003cp\u003e10.2.2 “Pure” Spectra, 166\u003c\/p\u003e \u003cp\u003e10.2.3 “Standard” Samples, 166\u003c\/p\u003e \u003cp\u003e10.2.4 X Data Creation, 167\u003c\/p\u003e \u003cp\u003e10.2.5 Data Centering, 168\u003c\/p\u003e \u003cp\u003e10.2.6 Data Overview, 168\u003c\/p\u003e \u003cp\u003e10.3 Classic Calibration, 169\u003c\/p\u003e \u003cp\u003e10.3.1 Univariate (Single Channel) Calibration, 169\u003c\/p\u003e \u003cp\u003e10.3.2 The Vierordt Method, 172\u003c\/p\u003e \u003cp\u003e10.3.3 Indirect Calibration, 174\u003c\/p\u003e \u003cp\u003e10.4 Inverse Calibration, 176\u003c\/p\u003e \u003cp\u003e10.4.1 Multiple Linear Calibration, 177\u003c\/p\u003e \u003cp\u003e10.4.2 Stepwise Calibration, 178\u003c\/p\u003e \u003cp\u003e10.5 Latent Variables Calibration, 180\u003c\/p\u003e \u003cp\u003e10.5.1 Projection Methods, 180\u003c\/p\u003e \u003cp\u003e10.5.2 Latent Variables Regression, 184\u003c\/p\u003e \u003cp\u003e10.5.3 Implementation of Latent Variable Calibration, 185\u003c\/p\u003e \u003cp\u003e10.5.4 Principal Component Regression (PCR), 186\u003c\/p\u003e \u003cp\u003e10.5.5 Projection on the Latent Structures-1 (PLS1), 188\u003c\/p\u003e \u003cp\u003e10.5.6 Projection on the Latent Structures-2 (PLS2), 191\u003c\/p\u003e \u003cp\u003e10.6 Methods Comparison, 193\u003c\/p\u003e \u003cp\u003eConclusion, 197\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Classification 198\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 The Basics, 198\u003c\/p\u003e \u003cp\u003e11.1.1 Problem Statement, 198\u003c\/p\u003e \u003cp\u003e11.1.2 Types of Classes, 199\u003c\/p\u003e \u003cp\u003e11.1.3 Hypothesis Testing, 199\u003c\/p\u003e \u003cp\u003e11.1.4 Errors in Classification, 200\u003c\/p\u003e \u003cp\u003e11.1.5 One-Class Classification, 200\u003c\/p\u003e \u003cp\u003e11.1.6 Training and Validation, 201\u003c\/p\u003e \u003cp\u003e11.1.7 Supervised and Unsupervised Training, 201\u003c\/p\u003e \u003cp\u003e11.1.8 The Curse of Dimensionality, 201\u003c\/p\u003e \u003cp\u003e11.1.9 Data Preprocessing, 201\u003c\/p\u003e \u003cp\u003e11.2 Data, 202\u003c\/p\u003e \u003cp\u003e11.2.1 Example, 202\u003c\/p\u003e \u003cp\u003e11.2.2 Data Subsets, 203\u003c\/p\u003e \u003cp\u003e11.2.3 Workbook Iris.xls, 204\u003c\/p\u003e \u003cp\u003e11.2.4 Principal Component Analysis, 205\u003c\/p\u003e \u003cp\u003e11.3 Supervised Classification, 205\u003c\/p\u003e \u003cp\u003e11.3.1 Linear Discriminant Analysis (LDA), 205\u003c\/p\u003e \u003cp\u003e11.3.2 Quadratic Discriminant Analysis (QDA), 210\u003c\/p\u003e \u003cp\u003e11.3.3 PLS Discriminant Analysis (PLSDA), 214\u003c\/p\u003e \u003cp\u003e11.3.4 SIMCA, 217\u003c\/p\u003e \u003cp\u003e11.3.5 k-Nearest Neighbors (kNN), 223\u003c\/p\u003e \u003cp\u003e11.4 Unsupervised Classification, 225\u003c\/p\u003e \u003cp\u003e11.4.1 PCA Again (Revisited), 225\u003c\/p\u003e \u003cp\u003e11.4.2 Clustering by K-Means, 225\u003c\/p\u003e \u003cp\u003eConclusion, 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Multivariate Curve Resolution 230\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 The Basics, 230\u003c\/p\u003e \u003cp\u003e12.1.1 Problem Statement, 230\u003c\/p\u003e \u003cp\u003e12.1.2 Solution Ambiguity, 232\u003c\/p\u003e \u003cp\u003e12.1.3 Solvability Conditions, 234\u003c\/p\u003e \u003cp\u003e12.1.4 Two Types of Data, 235\u003c\/p\u003e \u003cp\u003e12.1.5 Known Spectrum or Profile, 236\u003c\/p\u003e \u003cp\u003e12.1.6 Principal Component Analysis (PCA), 236\u003c\/p\u003e \u003cp\u003e12.1.7 PCA and MCR, 237\u003c\/p\u003e \u003cp\u003e12.2 Simulated Data, 237\u003c\/p\u003e \u003cp\u003e12.2.1 Example, 237\u003c\/p\u003e \u003cp\u003e12.2.2 Data, 238\u003c\/p\u003e \u003cp\u003e12.2.3 PCA, 238\u003c\/p\u003e \u003cp\u003e12.2.4 The HELP Plot, 240\u003c\/p\u003e \u003cp\u003e12.3 Factor Analysis, 241\u003c\/p\u003e \u003cp\u003e12.3.1 Procrustes Analysis, 241\u003c\/p\u003e \u003cp\u003e12.3.2 Evolving Factor Analysis (EFA), 244\u003c\/p\u003e \u003cp\u003e12.3.3 Windows Factor Analysis (WFA), 246\u003c\/p\u003e \u003cp\u003e12.4 Iterative Methods, 249\u003c\/p\u003e \u003cp\u003e12.4.1 Iterative Target Transform Factor Analysis (ITTFA), 249\u003c\/p\u003e \u003cp\u003e12.4.2 Alternating Least Squares (ALS), 250\u003c\/p\u003e \u003cp\u003eConclusion, 252\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART IV SUPPLEMENTS 255\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Extension Of Chemometrics Add-In 257\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Using Virtual Arrays, 257\u003c\/p\u003e \u003cp\u003e13.1.1 Simulated Data, 257\u003c\/p\u003e \u003cp\u003e13.1.2 Virtual Array, 259\u003c\/p\u003e \u003cp\u003e13.1.3 Data Preprocessing, 259\u003c\/p\u003e \u003cp\u003e13.1.4 Decomposition, 260\u003c\/p\u003e \u003cp\u003e13.1.5 Residuals Calculation, 260\u003c\/p\u003e \u003cp\u003e13.1.6 Eigenvalues Calculation, 262\u003c\/p\u003e \u003cp\u003e13.1.7 Orthogonal Distances Calculation, 263\u003c\/p\u003e \u003cp\u003e13.1.8 Leverages Calculation, 264\u003c\/p\u003e \u003cp\u003e13.2 Using VBA Programming, 265\u003c\/p\u003e \u003cp\u003e13.2.1 VBA Advantages, 265\u003c\/p\u003e \u003cp\u003e13.2.2 Virtualization of Real Arrays, 265\u003c\/p\u003e \u003cp\u003e13.2.3 Data Preprocessing, 266\u003c\/p\u003e \u003cp\u003e13.2.4 Residuals Calculation, 267\u003c\/p\u003e \u003cp\u003e13.2.5 Eigenvalues Calculation, 268\u003c\/p\u003e \u003cp\u003e13.2.6 Orthogonal Distances Calculation, 269\u003c\/p\u003e \u003cp\u003e13.2.7 Leverages Calculation, 270\u003c\/p\u003e \u003cp\u003eConclusion, 271\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Kinetic Modeling of Spectral Data 272\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 The “Grey” Modeling Method, 272\u003c\/p\u003e \u003cp\u003e14.1.1 Problem Statement, 272\u003c\/p\u003e \u003cp\u003e14.1.2 Example, 274\u003c\/p\u003e \u003cp\u003e14.1.3 Data, 274\u003c\/p\u003e \u003cp\u003e14.1.4 Soft Method of Alternating Least Squares (Soft-ALS), 275\u003c\/p\u003e \u003cp\u003e14.1.5 Hard Method of Alternating Least Squares (Hard-ALS), 277\u003c\/p\u003e \u003cp\u003e14.1.6 Using Solver Add-In, 279\u003c\/p\u003e \u003cp\u003eConclusions, 282\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 MATLAB®: Beginner’s Guide 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 The Basics, 283\u003c\/p\u003e \u003cp\u003e15.1.1 Workspace, 283\u003c\/p\u003e \u003cp\u003e15.1.2 Basic Calculations, 285\u003c\/p\u003e \u003cp\u003e15.1.3 Echo, 285\u003c\/p\u003e \u003cp\u003e15.1.4 Workspace Saving: MAT-Files, 286\u003c\/p\u003e \u003cp\u003e15.1.5 Diary, 286\u003c\/p\u003e \u003cp\u003e15.1.6 Help, 287\u003c\/p\u003e \u003cp\u003e15.2 Matrices, 287\u003c\/p\u003e \u003cp\u003e15.2.1 Scalars, Vectors, and Matrices, 287\u003c\/p\u003e \u003cp\u003e15.2.2 Accessing Matrix Elements, 289\u003c\/p\u003e \u003cp\u003e15.2.3 Basic Matrix Operations, 289\u003c\/p\u003e \u003cp\u003e15.2.4 Special Matrices, 290\u003c\/p\u003e \u003cp\u003e15.2.5 Matrix Calculations, 292\u003c\/p\u003e \u003cp\u003e15.3 Integrating Excel and MATLAB®, 294\u003c\/p\u003e \u003cp\u003e15.3.1 Configuring Excel, 294\u003c\/p\u003e \u003cp\u003e15.3.2 Data Exchange, 294\u003c\/p\u003e \u003cp\u003e15.4 Programming, 295\u003c\/p\u003e \u003cp\u003e15.4.1 M-Files, 295\u003c\/p\u003e \u003cp\u003e15.4.2 Script File, 296\u003c\/p\u003e \u003cp\u003e15.4.3 Function File, 297\u003c\/p\u003e \u003cp\u003e15.4.4 Plotting, 298\u003c\/p\u003e \u003cp\u003e15.4.5 Plot Printing, 300\u003c\/p\u003e \u003cp\u003e15.5 Sample Programs, 301\u003c\/p\u003e \u003cp\u003e15.5.1 Centering and Scaling, 301\u003c\/p\u003e \u003cp\u003e15.5.2 SVD\/PCA, 301\u003c\/p\u003e \u003cp\u003e15.5.3 PCA\/NIPALS, 302\u003c\/p\u003e \u003cp\u003e15.5.4 PLS1, 303\u003c\/p\u003e \u003cp\u003e15.5.5 PLS2, 304\u003c\/p\u003e \u003cp\u003eConclusion, 306\u003c\/p\u003e \u003cp\u003eAfterword. The Fourth Paradigm 307\u003c\/p\u003e \u003cp\u003eIndex 311\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":53186829779287,"sku":"9781118605356","price":72.86,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/chemometrics-in-excel-9781118605356","provider":"Book Curl","version":"1.0","type":"link"}