Advanced Statistical Analysis for Soil Scientists
In the evolving field of soil science, advanced statistical analysis has become essential for understanding complex soil processes. Handbook of Advanced Statistical Analysis Applied offers a comprehensive guide to modern statistical methods such as PERMANOVA, ANOSIM, PCA, NMDS, RDA, and SEM, tailore...
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| المؤلف الرئيسي: | |
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| مؤلف مشترك: | |
| التنسيق: | الكتروني كتاب الكتروني |
| اللغة: | الإنجليزية |
| منشور في: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
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| الطبعة: | 1st ed. 2025. |
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://doi.org/10.1007/978-3-031-88161-9 |
| الوسوم: |
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| 250 | |a 1st ed. 2025. | ||
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| 505 | 0 | |a PERMANOVA -- Analysis of Similarities (ANOSIM) -- Multiple Regression -- Principal component analysis (PCA) -- Redundancy analysis (RDA) -- Non-metric muldimensional scaling (NMDS) -- Canonical correspondence analysis (CCA) -- Structural equation modelling (SEM). | |
| 520 | |a In the evolving field of soil science, advanced statistical analysis has become essential for understanding complex soil processes. Handbook of Advanced Statistical Analysis Applied offers a comprehensive guide to modern statistical methods such as PERMANOVA, ANOSIM, PCA, NMDS, RDA, and SEM, tailored specifically for soil science research.Through practical examples and case studies, the book demonstrates how these techniques can be applied to analyze complex datasets, identify patterns, and model interactions within soil ecosystems. It provides step-by-step guidance on data preparation, analysis, and interpretation, making it a valuable resource for students, early-career researchers, and professionals in soil science. By combining theoretical foundations with practical applications, Handbook of Advanced Statistical Analysis Applied presentes analytical tools needed to conduct rigorous research, contribute to scientific knowledge, and address challenges in sustainable land management. This book serves as a critical resource for mastering advanced statistical approaches in soil science. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Soil science. | |
| 650 | 0 | |a Artificial intelligence |x Data processing. | |
| 650 | 0 | |a Multivariate analysis. | |
| 650 | 0 | |a Statistics . | |
| 650 | 0 | |a Robust statistics. | |
| 650 | 1 | 4 | |a Soil Science. |
| 650 | 2 | 4 | |a Data Science. |
| 650 | 2 | 4 | |a Multivariate Analysis. |
| 650 | 2 | 4 | |a Statistical Theory and Methods. |
| 650 | 2 | 4 | |a System Robustness. |
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| 950 | |a Focus Bundle Environmental Sciences (SpringerNature-85400) | ||