Chemometrics in Environmental AnalysisJohn Wiley & Sons, 1997 - 384 pagine J. W. Einax, H. W. Zwanziger S. Gei Chemometrics in Environmental Analysis Make the most of your data! This new title will serve both as an introduction and as a practical guide to those techniques of chemometrics which are applicable to environmental analysis. By describing the optimum methods of data analysis it will help all chemists in this field to save time and money. Because the authors demonstrate the most important chemometric methods with the aid of numerous examples, the reader will learn to solve a given problem by use of the appropriate method. Applications range from sampling, through laboratory analysis, to evaluation. Interpretation of the findings is explained clearly. The text covers not only basic methods such as univariate statistics, regression analysis, and statistical test planning, but also multivariate data analysis, for example, cluster analysis, principal components analysis, and factor and discriminant analysis. Case studies show the enormous possibilities, and the limits, of chemometric methods. The book will help all environmental analytical scientists, even those with only a basic knowledge of mathematics, to optimize the evaluation and interpretation of the results of their measurements. |
Sommario
Measurements and Basic Statistics | 25 |
Remarks on Experimental Design and Optimization | 71 |
Sampling and Sampling Design | 95 |
Multivariate Data Analysis | 139 |
7 | 192 |
1 | 205 |
5 | 217 |
7 | 233 |
Atmosphere | 251 |
Hydrosphere | 285 |
Pedosphere | 319 |
Related Topics | 359 |
Appendix | 373 |
Parole e frasi comuni
â₁ algorithm analysis of variance anthropogenic application ARIMA assessment autocorrelation function autoregression calculated calibration chemometric methods Chemometrics cluster analysis computed correlation coefficient cross-correlation data set demonstrated described determined discriminant analysis discriminant functions distance effects eigenvalues EINAX elements emission error estimated example factor analysis factor loadings feeder stream fraction geostatistical grid grid plan heavy metals influence interactions investigated kriging laboratory levels linear mathematical matrix mean value measurements mg L-¹ monitoring moving average Multivariate Analysis multivariate autocorrelation multivariate statistical MVDA nitrate concentration normal distribution objects obtained optimum outliers parameters partial autocorrelation plot pollution possible principal components principal components analysis procedure random regression model relationship river robust statistics sampling points Scores of factor seasonal Section sedimented semivariogram significant squares standard deviation statistical methods storage reservoir tion trend univariate variables Weida Weiße Elster x₁

