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Livros sobre o tema "Multiple statistical analysis"

Autor: Grafiati

Publicado: 22 de fevereiro de 2025

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1

Booth, Gordon D. Identifying proxy sets in multiple linear regression: An aid to better coefficient interpretation. Ogden, UT: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1994.

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2

Stanley, Feldman, ed. Multiple regression in practice. Beverly Hills: Sage Publications, 1985.

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3

Jaccard, James. Interaction effects in multiple regression. 2^a ed. Thousand Oaks, Calif: Sage Publications, 2003.

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4

Robert, Turrisi, e Wan Choi K, eds. Interaction effects in multiple regression. Newbury Park: Sage Publications, 1990.

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5

Orme, John G. Multiple regression with discrete dependent variables. New York: Oxford University Press, 2009.

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6

1923-, Cohen Jacob, e Cohen Jacob 1923-, eds. Applied multiple regression/correlation analysis for the behavioral sciences. 3^a ed. Mahwah, N.J: L. Erlbaum Associates, 2003.

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7

Bechhofer, Robert E. Design and analysis of experiments for statistical selection, screening, and multiple comparisons. New York: Wiley, 1995.

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8

Sheldon, Zedeck, ed. Data analysis for research designs: Analysis-of-variance and multiple regression/correlation approaches. New York: W.H. Freeman, 1989.

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9

K, Wan Choi, ed. LISREL approaches to interaction effects in multiple regression. Thousand Oaks, Calif: Sage Publications, 1996.

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10

Lin, Nancy Pei-ching. A new approach to sample size determination of replicated Latin square designs and analysis of multiple comparison procedures. [Tʻai-pei shih: Ching sheng wen wu kung ying kung ssu, 1985.

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11

Miller, N. L. An analysis of simulated California climate using multiple dynamical and statistical techniques: Final paper. Sacramento, Calif.]: California Energy Commission, 2009.

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12

Roodman, Gary M. Multiple calculated figures: A set of fifteen English country dances : with music, comments, and suggestions, and complete statistical analysis. Binghamton, NY: G. Roodman, 1999.

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13

Roux, Brigitte Le. Multiple correspondence analysis. Thousand Oaks, CA: Sage Publications, 2010.

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14

Roux, Brigitte Le. Multiple correspondence analysis. Thousand Oaks, Calif: Sage Publications, 2010.

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15

Goodrich, John W. An approach to the development of numerical algorithms for first order linear hyperbolic systems in multiple space dimensions: The constant coefficient case. [Washington, D.C.]: National Aeronautics and Space Administration, 1995.

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16

United States. National Aeronautics and Space Administration., ed. An approach to the development of numerical algorithms for first order linear hyperbolic systems in multiple space dimensions: The constant coefficient case. [Washington, D.C.]: National Aeronautics and Space Administration, 1995.

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17

J, Greenacre Michael, e Blasius Jörg 1957-, eds. Multiple correspondence analysis and related methods. Boca Raton: Chapman & Hall/CRC, 2006.

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18

Paul, Reise Steven, e Duan Naihua 1949-, eds. Multilevel modeling: Methodological advances, issues, and applications. Mahwah, N.J: Lawrence Erlbaum Associates, 2003.

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19

Koster, Jan T. A. Mathematical aspects of multiple correspondence analysis for ordinal variables. Leiden: DSWO Press, 1989.

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20

Whelan, Christopher T. Multiple deprivation and multiple disadvantage in Ireland: An analysis of EU-SILC. Dublin: Economic and Social Research Institute, 2007.

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21

Dattalo, Patrick. Analysis of Multiple Dependent Variables. Oxford University Press, Incorporated, 2013.

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22

Multiple Correspondence Analysis for the Social Sciences. Taylor & Francis Group, 2018.

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23

Hjellbrekke, Johs. Multiple Correspondence Analysis for the Social Sciences. Taylor & Francis Group, 2018.

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24

Hjellbrekke, Johs. Multiple Correspondence Analysis for the Social Sciences. Taylor & Francis Group, 2018.

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25

Hjellbrekke, Johs. Multiple Correspondence Analysis for the Social Sciences. Taylor & Francis Group, 2018.

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26

Turrisi, Rob. Interaction Effects in Multiple Regression. 2003.

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27

West, Stephen G., Jacob Cohen, Patricia Cohen e Leona S. Aiken. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. 3^a ed. Lawrence Erlbaum, 2002.

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28

Combs-Orme, Terri, e John G. Orme. Multiple Regression with Discrete Dependent Variables. Oxford University Press, 2009.

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29

Pages, Jerome. Multiple Factor Analysis by Example Using R. Taylor & Francis Group, 2014.

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30

Multiple Factor Analysis by Example Using R. Apple Academic Press Inc., 2014.

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31

Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Taylor & Francis Group, 2002.

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32

Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Routledge, 2013.

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33

West, Stephen G., Jacob Cohen, Patricia Cohen e Leona S. Aiken. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Taylor & Francis Group, 2013.

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34

West, Stephen G., Jacob Cohen, Patricia Cohen e Leona S. Aiken. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Taylor & Francis Group, 2013.

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35

Cohen, Jacob, e Patricia Cohen. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Taylor & Francis Group, 1998.

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36

West, Stephen G., Jacob Cohen, Patricia Cohen e Leona S. Aiken. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Taylor & Francis Group, 2014.

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37

Moyé, Lemuel A. Multiple Analyses in Clinical Trials: Fundamentals for Investigators. Springer New York, 2006.

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38

Moyé, Lemuel A. Multiple Analyses in Clinical Trials: Fundamentals for Investigators. Springer New York, 2010.

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39

Multiple-choice problems set for basic statistical analysis I, Stat 1000. 2^a ed. Boston, MA: Pearson Learning Solutions, 2010.

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40

Wan, Chun. LISREL Approaches to Interaction Effects in Multiple Regression. 1996.

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41

Multiple Analyses in Clinical Trials: Fundamentals for Investigators (Statistics for Biology and Health). Springer, 2003.

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42

Neuhäuser, Markus, e Graeme D. Ruxton. The Statistical Analysis of Small Data Sets. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/oso/9780198872979.001.0001.

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Abstract We live in the era of big data. However, small data sets are still common for ethical, financial, and practical reasons. Small sample sizes can cause researchers to particularly seek the most powerful methods to analyse their data; but they may be wary that some methodologies rely on assumptions that may not be appropriate when samples are small. The book offers advice on the statistical analysis of small data sets for various designs and levels of measurement. This should help researchers to analyse such data sets, but also to evaluate and interpret others' analyses. Potential challenges associated with a small sample and how these challenges can be mitigated are discussed. Generally, approaches that are often not especially difficult to apply are preferred; a focus is on permutation tests and bootstrap methods. However, topics such as meta-analysis, sequential and adaptive designs, and multiple testing are also discussed. The focus is on frequentist methods, but Bayesian analyses are also covered. R code is presented to carry out the proposed methods; many of them are not limited to use on small data sets. Approaches for computing the power or the necessary sample size, respectively, are also given.

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43

Multiple and Generalized Nonparametric Regression (Quantitative Applications in the Social Sciences). Sage Publications, Inc, 2000.

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44

Tran, Thanh V., e Keith T. Chan. Applied Cross-Cultural Data Analysis for Social Work. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190888510.001.0001.

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Applied Cross-Cultural Data Analysis for Social Work is a research guide which provides a hands-on approach for learning and understanding data analysis techniques for examining and interpreting data for the purpose of cultural group comparisons. This book aims to provide practical applications in statistical approaches of data analyses that are commonly used in cross-cultural research and evaluation. Readers are presented with step-by-step illustrations in the use of descriptive, bivariate, and multivariate statistics to compare cross-cultural populations using large-scale, population-based survey data. These techniques have important applications in health, mental health, and social science research relevant to social work and other helping professions, especially in providing a framework of evidence to examine health disparities using population-health data. For each statistical approach discussed in this book, we explain the underlying purpose, basic assumptions, types of variables, application of the Stata statistical package, the presentation of statistical findings, and the interpretation of results. Unlike previous guides on statistical approaches and data analysis in social work, this book explains and demonstrates the strategies of cross-cultural data analysis using descriptive and bivariate analysis, multiple regression, additive and multiplicative interaction, mediation, and SEM and HLM for subgroup analysis and cross-cultural comparisons. This book also includes sample syntax from Stata for social work researchers to conduct cross-cultural analysis with their own research.

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45

Hj Jubok, Zainodin, Khuneswari Gopal Pillay e Noraini Abdullah. Model Building Approach in Multiple Regression. UMS Press, 2018. http://dx.doi.org/10.51200/modelbuildingumspress2018-978-967-2166-14-6.

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This book is the outcome of our three years of research work. The problems faced by the undergraduate students in analysing the data on multiple regressions and the importance of this method had inspired the authors to come up with this book. Regression analysis is commonly used by most researchers in business, social and behavioural sciences, biological sciences and many other fields. But there is no proper procedure or approach of model-building in regression analysis. Therefore, this book is aimed at illustrating the procedures to find the best model and the model-building approach. The model-building approach is important in obtaining the best model that well describes the corresponding data set. This approach can be used in various research fields such as in economics, environmental, biological and medical sciences. The multiple regression model-building approach is applicable in various fields of research. It is very useful for researchers to obtain the best model that well describes the data. The approach is also useful in identifying the factors that will affect the dependent variable. The overall thrusts of the authors’ efforts have been geared in explaining explicitly the statistical method using regression analysis, the model building procedures and its applications, so as to meet the needs of today’s students. A substantial effort has gone in addressing multicollinearity issues and illustrating steps to overcome them. The authors hope that readers will find them helpful.

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46

Visualization and verbalization of data. Boca Raton: CRC Press, Taylor & Francis Group, 2014.

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47

Gelman, Andrew, e Deborah Nolan. Multiple regression and nonlinear models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.003.0010.

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This chapter covers multiple regression and links statistical inference to general topics such as lurking variables that arose earlier. Many examples can be used to illustrate multiple regression, but we have found it useful to come to class prepared with a specific example, with computer output (since our students learn to run the regressions on the computer). We have found it is a good strategy to simply use a regression analysis from some published source (e.g., a social science journal) and go through the model and its interpretation with the class, asking students how the regression results would have to differ in order for the study’s conclusions to change. The chapter includes examples that revisit the simple linear model of height and income, involve the class in models of exam scores, and fit a nonlinear model (for more advanced classes) for golf putting.

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48

Teetor, Paul. R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics. O'Reilly Media, Incorporated, 2019.

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49

Veech, Joseph A. Habitat Ecology and Analysis. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198829287.001.0001.

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Habitat is crucial to the survival and reproduction of individual organisms as well as persistence of populations. As such, species-habitat relationships have long been studied, particularly in the field of wildlife ecology and to a lesser extent in the more encompassing discipline of ecology. The habitat requirements of a species largely determine its spatial distribution and abundance in nature. One way to recognize and appreciate the over-riding importance of habitat is to consider that a young organism must find and settle into the appropriate type of habitat as one of the first challenges of life. This process can be cast in a probabilistic framework and used to better understand the mechanisms behind habitat preferences and selection. There are at least six distinctly different statistical approaches to conducting a habitat analysis – that is, identifying and quantifying the environmental variables that a species most strongly associates with. These are (1) comparison among group means (e.g., ANOVA), (2) multiple linear regression, (3) multiple logistic regression, (4) classification and regression trees, (5) multivariate techniques (Principal Components Analysis and Discriminant Function Analysis), and (6) occupancy modelling. Each of these is lucidly explained and demonstrated by application to a hypothetical dataset. The strengths and weaknesses of each method are discussed. Given the ongoing biodiversity crisis largely caused by habitat destruction, there is a crucial and general need to better characterize and understand the habitat requirements of many different species, particularly those that are threatened and endangered.

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50

Duval, Julien. Correspondence Analysis and Bourdieu’s Approach to Statistics. Editado por Thomas Medvetz e Jeffrey J. Sallaz. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199357192.013.23.

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Chapter abstract Since the mid-1970s, Bourdieu used multiple correspondence analysis (MCA) on a regular basis in order to construct fields and social spaces. After having been long neglected, this part of his work has spurred a new interest for some years. This chapter aims to highlight the very original and rich thought that lies behind Bourdieu’s use of MCA, but which can lead to misunderstandings. The chapter emphasizes three main points: the specific (French) sociological tradition in which Bourdieu’s statistical practices were rooted; the importance of the stage that consists in establishing the data to construct social spaces in an adequate way; and the dialectic relation between the thinking in terms of field and the use of MCA.

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