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|z (OCoLC)1416923857
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|a HCDD
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|a LISA 2020 Sustainability Symposium
|d (2022 :
|c Kumasi, Ghana).
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| 245 |
1 |
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|a Sustainable statistical and data science methods and practices :
|b reports from LISA 2020 Global Network, Ghana, 2022 /
|c O. Olawale Awe, Eric A. Vance, editors.
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| 264 |
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1 |
|a Cham :
|b Springer,
|c [2023]
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| 264 |
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|c ©2023
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| 300 |
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|a 1 online resource (xxiv, 415 pages) :
|b illustrations (chiefly color).
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a STEAM-H: science, technology, engineering, agriculture, mathematics & health,
|x 2520-1948
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| 500 |
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|a Conference proceedings.
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| 504 |
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|a Includes bibliographical references.
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| 520 |
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|a This volume gathers papers presented at the LISA 2020 Sustainability Symposium in Kumasi, Ghana, May 2-6, 2022. They focus on sustainable methods and practices of using statistics and data science to address real-world problems. From utilizing social media for statistical collaboration to predicting obesity among rural women, and from analyzing inflation in Nigeria using machine learning to teaching data science in Africa, this book explores the intersection of data, statistics, and sustainability. With practical applications, code snippets, and case studies, this book offers valuable insights for researchers, policymakers, and data enthusiasts alike. The LISA 2020 Global Network aims to enhance statistical and data science capability in developing countries through the creation of a network of collaboration laboratories (also known as "stat labs"). These stat labs are intended to serve as engines for development by training the next generation of collaborative statisticians and data scientists, providing research infrastructure for researchers, data producers, and decision-makers, and enabling evidence-based decision-making that has a positive impact on society. The research conducted at LISA 2020 focuses on practical methods and applications for sustainable growth of statistical capacity in developing nations.
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| 505 |
0 |
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|a Chapter. 1. Using social media and network services to promote statistical collaboration laboratories: A case study of LEA Brazil -- Chapter. 2. Renewable Energy Forecasting Using Deep Learning Models -- Chapter. 3. Exploring feature selection and supervised classification algorithms for predicting Obesity among rural women for policy decisions -- Chapter. 4. Re-examining Inflation and its drivers in Nigeria: A machine learning approach -- Chapter. 5. Estimating Relative Response Rates and Preferential Ranking of Subjects -- Chapter. 6. Wealth Creation and Poverty Alleviation in a Nigerian State: A Recent Evidence-Based Survey -- Chapter. 7. Effect of Statistics on Collaboration for Enhancing Institutional Sustainability: A Case of Mzumbe University-Tanzania -- Chapter. 8. Strategies for the Sustainability of Stat Labs: A Case Study of Laboratory of Interdisciplinary Statistical Analysis, Lahore College for Women University Lahore, Pakistan (LISA-LCWU) -- Chapter. 9. Advanced Mathematics and Computations for Innovation and Sustainability of Modern Statistics Laboratory -- Chapter. 10. A New Estimator for the GPD Parameters under the POT Approach -- Chapter. 11. A simple yet Robust Estimation of binned data: Egypt Income distribution and Geographical Inequality -- Chapter. 12. Supervised Machine Learning Classification Algorithms: Some Applications and Code Snippets for Practical Implementations in Python Programming -- Chapter. 13. Exploring the spatial variability and different determinants of co-existence of under-nutritional status among children in India through a Bayesian geo-additive multinomial regression model -- Chapter. 14. Predicting the Nature of Terrorist Attacks in Nigeria Using Bayesian Neural Network Model -- Chapter. 15. Salvage Value from Deterioration (SVD): An Optimal Inventory Model for Chicken Egg Marketing -- Chapter. 16. Structural Equation Modeling with Stata: Illustration using a Population-Based, Nationally-Representative Dataset -- Chapter. 17. Time series forecasting of seasonal non-stationary climate data: A comparative study -- Chapter. 18. Weighted Hard and Soft Voting Ensemble Machine Learning CLASIFIERS: Application to Anaemia Diagnosis -- Chapter 19. Machine Learning Approaches for Handling Imbalances in Health Data Classification -- Chapter. 20. The Intersection of Data and Statistics with Sustainable Development Goals -- Chapter. 21. Teaching Data Science in Africa via Online Team-Based Learning.
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| 588 |
0 |
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|a Online resource; title from PDF title page (SpringerLink, viewed January 11, 2024).
|
| 650 |
|
0 |
|a Statistics
|v Congresses.
|
| 650 |
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0 |
|a Statistics
|x Methodology
|v Congresses.
|
| 655 |
|
7 |
|a proceedings (reports)
|2 aat
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| 655 |
|
7 |
|a Conference papers and proceedings.
|2 lcgft
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| 655 |
|
7 |
|a Actes de congrès.
|2 rvmgf
|
| 700 |
1 |
|
|a Awe, O. Olawale,
|e editor.
|
| 700 |
1 |
|
|a Vance, Eric Alan,
|d 1975-
|e editor.
|
| 776 |
0 |
8 |
|c Original
|z 3031413512
|z 9783031413513
|w (OCoLC)1390123151
|
| 830 |
|
0 |
|a STEAM-H (Series)
|x 2520-1948
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-031-41352-0
|y Click for online access
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| 903 |
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|a SPRING-ALL2023
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| 994 |
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|a 92
|b HCD
|