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Libros sobre el tema "Data analysis and interpretation techniques"

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Autor: Grafiati
Publicado: 6 de septiembre de 2023

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1

Taylor, John K. Statistical techniques for data analysis. Chelsea, Mich: Lewis Publishers, 1990.

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2

Cheryl, Cihon, ed. Statistical techniques for data analysis. 2a ed. Boca Raton: Chapman & Hall/CRC, 2004.

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3

S, Guy Christopher, Brown Michael L y American Fisheries Society, eds. Analysis and Interpretation of freshwater fisheries data. Bethesda, Md: American Fisheries Society, 2007.

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4

Matrix population models: Construction, analysis, and interpretation. Sunderland, Mass: Sinauer Associates, 1989.

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5

Digital analysis of remotely sensed imagery. New York: McGraw-Hill, 2009.

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6

Dua, Sumeet. Computational analysis of the human eye with applications. Singapore: World Scientific, 2011.

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7

Cressie, Noel A. C. Statistics for spatial data. New York: Wiley, 1991.

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8

Cressie, Noel A. C. Statistics for spatial data. New York: Wiley, 1993.

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9

Gideon, Keren y Lewis Charles 1943-, eds. A Handbook for data analysis in the behavioral sciences: Methodological issues. Hillsdale, N.J: L. Erlbaum Associates, 1993.

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10

Data analysis techniques. Harlow: Pearson Education Limited, 2006.

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11

Brace, Nicola. SPSS for psychologists: A guide to data analysis using SPSS for Windows. 2a ed. Houndmills, Basingstoke, Hampshire: Palgrave Macmillan, 2003.

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12

1960-, Kemp Richard y Snelgar Rosemary, eds. SPSS for psychologists: A guide to data analysis using SPSS for Windows (versions 12 and 13). 3a ed. Houndmills, Basingstoke, Hampshire: Palgrave Macmillan, 2006.

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13

1963-, Singh Ajit, Goldgof Dmitry B y Terzopoulos Demetri, eds. Deformable models in medical image analysis. Los Alamitos, Calif: IEEE Computer Society, 1998.

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14

William, Hewitt. Microbiological assay for pharmaceutical analysis: A rational approach. Boca Raton: Interpharm/CRC, 2004.

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15

Karjalainen, E. J. Data analysis for hyphenated techniques. Amsterdam: Elsevier, 1996.

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16

Richard, Kemp y Snelgar Rosemary, eds. SPSS for psychologists: A guide to data analysis using SPSS for Windows, versions 9, 10 and 11. 2a ed. Mahwah, N.J: Lawrence Erlbaum Associates, 2003.

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17

Habib, Zaidi, ed. Quantitative analysis of nuclear medicine images. New York: Springer, 2005.

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18

Segers, Jorge, ed. Analysis Techniques for Racecar Data Acquisition. Warrendale, PA: SAE International, 2008. http://dx.doi.org/10.4271/0768043794.

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19

Segers, Jorge. Analysis Techniques for Racecar Data Acquisition. Warrendale, PA: SAE International, 2008. http://dx.doi.org/10.4271/r-367.

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20

Segers, Jorge. Analysis techniques for racecar data acquisition. Warrendale, Pa., USA: SAE International, 2008.

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21

Rogers, Melissa J. B. Analysis techniques for residual acceleration data. [Marshall Space Flight Center, Ala.]: National Aeronautics and Space Administration, George C. Marshall Space Flight Center, 1990.

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22

Analysis techniques for racecar data acquisition. Warrendale, Pa: SAE International, 2014.

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23

B, Rogers Melissa J. y United States. National Aeronautics and Space Administration., eds. Accelerometer data analysis and presentation techniques. [Washington, D.C: National Aeronautics and Space Administration, 1997.

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24

Israëls, A. Z. Eigenvalue techniques for qualitative data. Leiden: DSWO Press, 1987.

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25

1954-, Engel Uwe y Reinecke Jost, eds. Analysis of change: Advanced techniques in panel data analysis. Berlin: Walter de Gruyter, 1996.

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26

Fraud analysis techniques using ACL. Hoboken, N.J: John Wiley & Sons, 2009.

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27

Boscoe, F. P., ed. Geographic health data: fundamental techniques for analysis. Wallingford: CABI, 2013. http://dx.doi.org/10.1079/9781780640891.0000.

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28

Mishra, Bhabani Shankar Prasad, Satchidananda Dehuri, Euiwhan Kim y Gi-Name Wang, eds. Techniques and Environments for Big Data Analysis. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27520-8.

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29

Jinfeng, Wang y SpringerLink (Online service), eds. Spatial Data Analysis: Models, Methods and Techniques. Berlin, Heidelberg: Manfred M. Fischer, 2011.

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30

R, Frühwirth y Regler M, eds. Data analysis techniques for high-energy physics. 2a ed. Cambridge, U.K: Cambridge University Press, 2000.

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31

Graphing data: Techniques for display and analysis. Thousand Oaks, Calif: Sage Publications, 1995.

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32

Analysis and Interpretation of Freshwater Fisheries Data. Amer Fisheries Society, 2007.

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33

Fielding, Alan H. Cluster and Classification Techniques for the Biosciences. Cambridge University Press, 2006.

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34

Fielding, Alan H. Cluster and Classification Techniques for the Biosciences. Cambridge University Press, 2007.

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35

Fielding, Alan H. Cluster and Classification Techniques for the Biosciences. Cambridge University Press, 2006.

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36

Cluster and Classification Techniques for the Biosciences. Cambridge University Press, 2006.

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37

Fielding, Alan H. Cluster and Classification Techniques for the Biosciences. Cambridge University Press, 2007.

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38

Fielding, Alan H. Cluster and Classification Techniques for the Biosciences. Cambridge University Press, 2010.

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39

Matrix Population Models: Construction, Analysis, and Interpretation. 2a ed. Sinauer Associates, 2000.

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40

Willis, Jackie. Data Analysis and Presentation Skills. Wiley & Sons, Incorporated, John, 2005.

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41

Dey, Sandipan. Hands-On Image Processing with Python: Expert techniques for advanced image analysis and effective interpretation of image data. Packt Publishing, 2018.

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42

Tran, Thanh V. y 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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43

Bhopal, Raj S. Epidemiological study designs and principles of data analysis: A conceptually integrated suite of methods and techniques. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198739685.003.0009.

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Epidemiological studies are unified by their common goals and by their basis in defined populations. The case series (or register-based study) includes examination of trends in deaths, cancers, notifiable diseases, and hospitalizations. Case–control studies are analysed by comparing the exposure to risk factors in cases to those in controls. In a population studied at a specific time and place (a cross-sectional study), measurements can be made of disease, the factors which may cause disease, or both simultaneously. Cohort studies produce data on disease incidence and are especially good on associations between risk factors and disease outcomes. Trials compare treated and untreated populations and are used, primarily, for information on effectiveness of health interventions. Natural experiments, including Mendelian randomization studies, may provide causal evidence. The principles for the analysis of all studies are similar. The design and interpretation should be in the context of traditional, systematic, and meta-analytic reviews.
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44

Ng, Eddie Yin-Kwee, Sumeet Dua y Rajendra Acharya U. Computational Analysis of the Human Eye with Applications. World Scientific Publishing Co Pte Ltd, 2011.

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45

Zaidi, Habib. Quantitative Analysis in Nuclear Medicine Imaging. Springer, 2006.

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46

Zaidi, Habib. Quantitative Analysis in Nuclear Medicine Imaging. Springer, 2010.

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47

(Editor), Gideon Keren y Charles Lewis (Editor), eds. Handbook for Data Analysis in the Behavioral Sciences: Methodological Issues. Lawrence Erlbaum, 1992.

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48

Quantitative analysis in nuclear medicine images. New York: Springer, 2006.

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49

Zaidi, Habib. Quantitative Analysis in Nuclear Medicine Imaging. Springer, 2005.

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50

Kemp, Richard, Nicola Brace y Rosemary Snelgar. SPSS for Psychologists: A Guide to Data Analysis Using Spss for Windows. LEA, Inc., 2000.

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