Introduction to Multivariate Statistical Analysis in ChemometricsCRC Press, 19 de abr. de 2016 - 336 páginas Using formal descriptions, graphical illustrations, practical examples, and R software tools, Introduction to Multivariate Statistical Analysis in Chemometrics presents simple yet thorough explanations of the most important multivariate statistical methods for analyzing chemical data. It includes discussions of various statistical methods, such as |
Conteúdo
| 1 | |
Multivariate Data | 31 |
Principal Component Analysis | 59 |
Calibration | 103 |
Classification | 195 |
Cluster Analysis | 251 |
Preprocessing | 283 |
Symbols and Abbreviations | 293 |
Matrix Algebra | 297 |
Introduction to R | 305 |
Outras edições - Ver todos
Introduction to Multivariate Statistical Analysis in Chemometrics Kurt Varmuza,Peter Filzmoser Prévia não disponível - 2009 |
Termos e frases comuns
algorithm applied assigned autoscaled bootstrap boxplot calibration set Chemom chemometrics cluster analysis columns computed correlation coefficient covariance matrix data set defined dendrogram eigenvalues eigenvectors Equation estimated Euclidean distance evaluation example function Geladi instance k-means kernel Lasso regression latent variable loading vectors Mahalanobis distance mean-centered methods misclassification error model-based clustering multivariate normal distribution Neural Networks nonlinear normal distribution number of clusters number of components number of objects number of PLS number of variables obtained OLS regression optimal optimum number orthogonal outliers overfitting parameters partial least squares PCA scores PCA space PLS components prediction errors prediction performance principal components prior probabilities regression coefficients regression model regressor variables repeated double CV residuals Ridge regression robust regression samples score plot score vector Section SEPCV similar spectra squared standard deviation statistical substructure test data test set training data transformed values variable selection variance Varmuza x-data x-variables y-data