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Books on the topic 'Coefficient of neutrosophic data'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Xiao, Zheng. Random coefficient panel data models. IZA, 2004.

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2

Li, Jun Corser. Bayesian statistical inference for coefficient alpha. ACT, Inc., 2002.

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3

Lee, Jihui. Varying-Coefficient Models and Functional Data Analyses for Dynamic Networks and Wearable Device Data. [publisher not identified], 2018.

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4

A, Spencer E., ed. The analysis of coefficient data relating to the EEC 100mm orifice plates. Commission of theEuropean Communities, 1986.

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5

Bederman, S. Samuel. Estimation methods in random coefficient regression for continuous and binary longitudinal data. National Library of Canada = Bibliothèque nationale du Canada, 1999.

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6

Shi, Feng. Learn About Assortativity Coefficient in R With Data From UK Faculty Dataset (2008). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526487568.

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7

Shi, Feng. Learn About Assortativity Coefficient in Python With Data From UK Faculty Dataset (2008). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526499059.

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8

Wiesen, Christopher. Learn About the Phi Coefficient in SPSS With Data From the Southern Opinion Research Survey (1990). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526489388.

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9

Wiesen, Christopher. Learn About the Eta Squared Coefficient With Data From the “Do Social Rights Affect Social Outcomes?” Dataset (2015). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526484871.

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10

Scott Jones, Julie. Learn to Use the Eta Coefficient Test in SPSS With Data From the NIOSH Quality of Worklife Survey (2014). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526474681.

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11

Scott Jones, Julie. Learn to Use the Eta Coefficient Test in R With Data From the NIOSH Quality of Worklife Survey (2014). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526495433.

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12

Scott Jones, Julie. Learn to Use the Phi Coefficient Measure and Test in SPSS With Data From the Welsh Health Survey (Teaching Dataset) (2009). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526473837.

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13

Scott Jones, Julie. Learn to Use Cronbach’s Coefficient Alpha Test in SPSS With Data From the British Crime Survey (Unrestricted Teaching Dataset) (2007–08). SAGE Publications Ltd., 2019. http://dx.doi.org/10.4135/9781526486592.

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14

Scott Jones, Julie. Learn to Use Cronbach’s Coefficient Alpha Test in R With Data From the British Crime Survey (Unrestricted Teaching Dataset) (2007–08). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526497055.

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15

Scott Jones, Julie. Learn to Use the Phi Coefficient Measure and Test in R With Data From the Welsh Health Survey (Teaching Dataset) (2009). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526498236.

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16

Pontolillo, James. The search for reliable aqueous solubility (Sw) and octanol-water partition coefficient (Kow) data for hydrophobic organic compounds: DDT and DDE as a case study. U.S. Dept. of the Interior, U.S. Geological Survey, 2001.

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17

Leonovich, Sergey, Evgeniy Shalyy, Elena Polonina, Elena Sadovskaya, Lev Kim, and Valentin Dorkin. Durability of port reinforced concrete structures (Far East and Sakhalin). INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1816638.

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Section I of the monograph is devoted to an urgent problem - forecasting the durability of port reinforced concrete structures, the destruction of which is associated with corrosion of steel reinforcement caused by chloride aggression and carbonation of concrete. The analysis of models for calculating the service life of structures and experimental data is carried out, the life cycles for the main degradation processes in concrete and reinforcement, the periods of initiation and propagation of corrosion are considered, the influence of environmental factors (temperature, humidity) and the qual

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18

Pereyaslovec, Vladimir, and Lev Erdakov. Dynamics of the number of wolves and large ungulates in some regions of Western Siberia. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1984075.

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In the monograph, based on the authors' long—term observations in the Yugansky Nature Reserve (KhMAO — Yugra), as well as open literature data, the dynamics of the number of large ungulates living together: forest reindeer, elk, and also the wolf predator associated with them are analyzed. The authors used traditional statistical and dynamic methods of analysis, as well as some ecological indices: compensation coefficient and habitat fidelity indices. Landscape features of the long—term course of abundance and its cycles in deer and wolf, synchronization of dynamics in the predator-prey system

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19

Serikov, Sergey. Impact on impact strength. INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/2161513.

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The main purpose of the monograph is to identify the main patterns in assessing the operational reliability of metals on the basis of a mathematical model of unsteady deformation of an isotropic viscoplastic medium, with specified boundary, initial conditions and energy criterion of destruction. A physically based computational and experimental method for metal identification is formulated. Practical examples of the efficiency of the method for a wide class of materials are given: structural steels, titanium, aluminum and copper alloys. The assessment of the operational reliability of metals i

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20

Razavi, Shahra, Umberto Cattaneo, Helmut Schwarzer, and Andrea Visentin. Combating inequalities. ILO, 2024. http://dx.doi.org/10.54394/eoay4970.

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The primary aim of this study is to provide evidence regarding the impact of social protection benefits, taxes and social security contributions in reducing income inequalities. The study employs a well-established methodology to estimate the partial redistributive effect of contributory and non-contributory pensions, family benefits, unemployment benefits, sickness and employment injury benefits, disability benefits, social security contributions, as well as income and property taxes. The partial redistributive effect corresponds to the percentage decrease in the Gini coefficient that is attr

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21

Consortium for Mathematics and Its Applications (U.S.), Chedd-Angier Production Company, American Statistical Association, and Annenberg Media, eds. Against all odds--inside statistics: Disc 3, programs 9-12. Annenberg Media, 2011.

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22

Cai, Zongwu. Functional Coefficient Models for Economic and Financial Data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.6.

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This article discusses the use of functional coefficient models for economic and financial data analysis. It first provides an overview of recent developments in the nonparametric estimation and testing of functional coefficient models, with particular emphasis on the kernel local polynomial smoothing method, before considering misspecification testing as an important econometric question when fitting a functional (varying) coefficient model or a trending time-varying coefficient model. It then describes two major real-life applications of functional coefficient models in economics and finance

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23

Coefficient of Variation and Machine Learning Applications. Taylor & Francis Group, 2019.

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24

Coefficient of Variation and Machine Learning Applications. Taylor & Francis Group, 2019.

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25

Dey, Nilanjan, K. Hima Bindu, Raghava Morusupalli, and C. Raghavendra Rao. Coefficient of Variation and Machine Learning Applications. Taylor & Francis Group, 2021.

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26

Dey, Nilanjan, K. Hima Bindu, Raghava Morusupalli, and C. Raghavendra Rao. Coefficient of Variation and Machine Learning Applications. Taylor & Francis Group, 2019.

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27

Dey, Nilanjan, K. Hima Bindu, Raghava Morusupalli, and C. Raghavendra Rao. Coefficient of Variation and Machine Learning Applications. Taylor & Francis Group, 2019.

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28

Pearson's Correlation Coefficient and the Global Health Observatory Data (2012): Disaster Preparedness and Food Safety. SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473948167.

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29

Pearson's Correlation Coefficient and the Integrated Postsecondary Education Data System (2012): Graduation Rates by Type of College. SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473937901.

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30

Spearman’s Rank-Order Correlation Coefficient and the Global Health Observatory Data (2012): Life Expectancy and Per Capita Government Health Spending. SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473948181.

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31

Ferraty, Frédéric, and Yves Romain, eds. The Oxford Handbook of Functional Data Analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.001.0001.

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This handbook presents the state-of-the-art of the statistics dealing with functional data analysis. With contributions from international experts in the field, it discusses a wide range of the most important statistical topics (classification, inference, factor-based analysis, regression modeling, resampling methods, time series, random processes) while also taking into account practical, methodological, and theoretical aspects of the problems. The book is organised into three sections. Part I deals with regression modeling and covers various statistical methods for functional data such as li

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32

Statistical issues in microarray data analysis: Array-to-array normalization, empirical Bayes batch adjustment and Pearson’s correlation coefficient in the context of replicated experiments. 2006.

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33

Beck, Nathaniel. Time‐Series Cross‐Section Methods. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0020.

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This article outlines the literature on time-series cross-sectional (TSCS) methods. First, it addresses time-series properties including issues of nonstationarity. It moves to cross-sectional issues including heteroskedasticity and spatial autocorrelation. The ways that TSCS methods deal with heterogeneous units through fixed effects and random coefficient models are shown. In addition, a discussion of binary variables and their relationship to event history models is provided. The best way to think about modeling single time series is to think about modeling the time-series component of TSCS

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34

Paeglīte, Ilze. Kustīgās slodzes dinamiskās iedarbes uz autoceļu tiltiem eksperimentāla izpēte un novērtējums. RTU Press, 2021. http://dx.doi.org/10.7250/9789934227028.

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Using data obtained from the dynamic load testing of bridges a method was developed to evaluate level of the dynamic performance without performing a dynamic load test. In this method a dynamic index of the bridge is calculated. Dynamic index allows to evaluate the dynamic performance level of existing and new structures taking into account such bridge parameters as span length / height ratio, natural frequency, vibration damping coefficient, relative deflection and international roughness index IRI. Dynamic index method can be used by bridge owners and maintainers to determine the dynamic pot

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35

Skiba, Grzegorz. Fizjologiczne, żywieniowe i genetyczne uwarunkowania właściwości kości rosnących świń. The Kielanowski Institute of Animal Physiology and Nutrition, Polish Academy of Sciences, 2020. http://dx.doi.org/10.22358/mono_gs_2020.

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Bones are multifunctional passive organs of movement that supports soft tissue and directly attached muscles. They also protect internal organs and are a reserve of calcium, phosphorus and magnesium. Each bone is covered with periosteum, and the adjacent bone surfaces are covered by articular cartilage. Histologically, the bone is an organ composed of many different tissues. The main component is bone tissue (cortical and spongy) composed of a set of bone cells and intercellular substance (mineral and organic), it also contains fat, hematopoietic (bone marrow) and cartilaginous tissue. Bones a

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36

Zydroń, Tymoteusz. Wpływ systemów korzeniowych wybranych gatunków drzew na przyrost wytrzymałości gruntu na ścinanie. Publishing House of the University of Agriculture in Krakow, 2019. http://dx.doi.org/10.15576/978-83-66602-46-5.

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The aim of the paper was to determine the influence of root systems of chosen tree species found in the Polish Flysch Carpathians on the increase of soil shear strength (root cohesion) in terms of slope stability. The paper's goal was achieved through comprehensive tests on root systems of eight relatively common in the Polish Flysch Carpathians tree species. The tests that were carried out included field work, laboratory work and analytical calculations. As part of the field work, the root area ratio (A IA) of the roots was determined using the method of profiling the walls of the trench at a

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