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Advanced Energy and Sustainability Research, EarlyView.This study integrates hybrid density functional theory, Boltzmann transport theory, and machine learning to accelerate the discovery of lead‐free halide double perovskites for thermoelectric energy conversion. By screening 102 compounds, the authors identify high‐performing candidates such as Rb2GeI6 and Cs2SnBr6, offering a sustainable pathway toward ...
Souraya Goumri‐Said +2 more
wiley +1 more source
Multivariate Analysis of Hydrochemical Data for Jewel Cave, Wind Cave, and Surrounding Areas
, 2019 Andrew J. Long +2 more
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Technometrics, 1973
Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each. Well-suited for the non-statistician, this applications-oriented introduction to multivariate
H. Herne +2 more
+4 more sources
Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each. Well-suited for the non-statistician, this applications-oriented introduction to multivariate
H. Herne +2 more
+4 more sources
International Statistical Review / Revue Internationale de Statistique, 1972
I Introduction 1 Introduction II Preparing For a MV Analysis 2 Examining Your Data 3 Factor Analysis III Dependence Techniques 4 Multiple Regression Analysis 5 Multiple Discriminate Analysis and Logistic Regression 6 Multivariate Analysis of Variance 7 Conjoint Analysis IV Interdependence Techniques 8 Cluster Analysis 9 Multidimensional Scaling and ...
J. C. Gower, W. W. Cooley, P. R. Lohnes
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I Introduction 1 Introduction II Preparing For a MV Analysis 2 Examining Your Data 3 Factor Analysis III Dependence Techniques 4 Multiple Regression Analysis 5 Multiple Discriminate Analysis and Logistic Regression 6 Multivariate Analysis of Variance 7 Conjoint Analysis IV Interdependence Techniques 8 Cluster Analysis 9 Multidimensional Scaling and ...
J. C. Gower, W. W. Cooley, P. R. Lohnes
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Multivariate data analysis of NMR data
Journal of Pharmaceutical and Biomedical Analysis, 1991Multivariate methods based on principal components (PCA and PLS) have been used to reduce NMR spectral information, to predict NMR parameters of complicated structures, and to relate shift data sets to dependent descriptors of biological significance.
U, Edlund, H, Grahn
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Multivariate Data Analysis of Proteome Data
2006We present the background for multivariate data analysis on proteomics data with a hands-on section on how to transfer data between different software packages. The techniques can also be used for other biological and biochemical problems in which structures have to be found in a large amount of data. Digitalization of the 2D gels, analysis using image
Kåre, Engkilde +2 more
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Multivariate Data and Multivariate Analysis
2005Multivariate data arise when researchers record the values of several random variables on a number of subjects or objects or perhaps one of a variety of other things (we will use the general term “units”) in which they are interested, leading to a vector-valued or multidimensional observation for each.
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2016
This short chapter shows how the statistical properties of higher-dimensional data can be visualized: with 3D-surfaces, Scatterplot Matrices, and Correlation Matrices.
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This short chapter shows how the statistical properties of higher-dimensional data can be visualized: with 3D-surfaces, Scatterplot Matrices, and Correlation Matrices.
openaire +1 more source