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Libros sobre el tema "Spatial analysis (Statistics) Regression analysis"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 7 de febrero de 2022

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1

Ward, Michael Don. Spatial regression models. Thousand Oaks: Sage Publications, 2008.

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2

Griffith, Daniel A. Spatial regression analysis on the PC: Spatial statistics using Minitab. [Ann Arbor, Mich., U.S.A.] (2790 Briarcliff, Ann Arbor 48105): Institute of Mathematical Geography, 1989.

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3

Griffith, Daniel A. Spatial regression analysis on the PC: Spatial statistics using SAS. Washington, D.C: Association of American Geographers, 1993.

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4

Bailey, Trevor C. Interactive spatial data analysis. Harlow Essex, England: Longman Scientific & Technical, 1995.

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5

Chris, Brunsdon y Charlton Martin, eds. Geographically weighted regression: The analysis of spatially varying relationships. Chichester, England: Wiley, 2002.

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6

Thomas, Kneib, ed. Bayesian smoothing and regression for longitudinal, spatial and event history data. Oxford: Oxford University Press, 2011.

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7

Johnson, Laura D. Smoothing spatial data by estimating mean local variance. Monterey, Calif: Naval Postgraduate School, 1988.

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8

Petrucci, Alessandra. The application of a spatial regression model to the analysis and mapping of poverty. Rome: Food and Agriculture Organization of the United Nations, 2003.

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9

Understanding regression analysis. New York: Plenum Press, 1997.

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10

S, Hadi Ali, ed. Regression analysis by example. Hoboken, New Jersey: Wiley, 2012.

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11

Ripley, Brian D. Spatial statistics. Hoboken, N.J: Wiley-Interscience, 2004.

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12

Draper, N. R. Applied regression analysis. 3a ed. New York: Wiley, 1998.

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13

S. A. van de Geer. Regression analysis and empirical processes. Amsterdam, the Netherlands: Centrum voor Wiskunde en Informatica, 1988.

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14

1953-, Peck Elizabeth A. y Vining G. Geoffrey 1954-, eds. Introduction to linear regression analysis. 5a ed. Hoboken, NJ: Wiley, 2012.

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15

Boots, B. N. Point pattern analysis. Newbury Park, Calif: Sage Publications, 1988.

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16

Pardoe, Iain. Applied regression modeling. 2a ed. Hoboken, NJ: Wiley, 2012.

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17

John), Unwin D. (David, ed. Geographic information analysis. 2a ed. Hoboken, N.J: John Wiley & Sons, 2010.

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18

O'Sullivan, David. Geographic information analysis. 2a ed. Hoboken, N.J: J. Wiley & Sons, 2010.

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19

Rey, Sergio J. y Luc Anselin. Perspectives on spatial data analysis. Heidelberg: Springer, 2010.

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20

R, Wolf Paul, ed. Adjustment computations: Spatial data analysis. 5a ed. Hoboken, N.J: Wiley, 2010.

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21

1949-, Fingleton B., ed. Spatial data analysis by example. Chichester: Wiley, 1985.

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22

Jean, Dunn Olive y Clark Virginia 1928-, eds. Applied statistics: Analysis of variance and regression. 3a ed. Hoboken, N.J: Wiley-Interscience, 2004.

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23

Dunn, Olive Jean. Applied statistics: Analysis of variance and regression. 2a ed. New York: Wiley, 1987.

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24

Essential statistics, regression, and econometrics. Amsterdam: Academic Press, 2012.

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25

J, Wonnacott Ronald, ed. Regression, a second course in statistics. Malabar, Fla: R.E. Krieger Pub. Co., 1986.

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26

Local models for spatial analysis. 2a ed. Boca Rataon, Fla: CRC Press, 2011.

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27

Ghilani, Charles D. Adjustment computations: Spatial data analysis. 4a ed. Hoboken, N.J: John Wiley & Sons, 2006.

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28

Practical guide to logistic regression. Boca Raton: CRC Press/Taylor & Francis, 2016.

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29

Terry, Sincich, ed. A second course in statistics: Regression analysis. 6a ed. Upper Saddle River, N.J: Prentice Hall, 2003.

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30

Terry, Sincich y Mendenhall William, eds. A second course in statistics: Regression analysis. 5a ed. Upper Saddle River, N.J: Prentice Hall, 1996.

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31

Terry, Sincich, ed. A second course in statistics: Regression analysis. 7a ed. Boston: Pearson Education, 2012.

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32

G, Amrhein Carl y Desloges Joseph R, eds. Statistical analysis for geographers. Englewood Cliffs, N.J: Prentice Hall, 1991.

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33

An introduction to computational statistics: Regression analysis. Englewood Cliffs, N.J: Prentice Hall, 1995.

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34

Lawal, Bayo. Applied Statistics: Regression and Analysis of Variance. Lanham, Maryland, USA: University Press of America, 2013.

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35

Quantile Regression For Spatial Data. Springer, 2012.

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36

Gleditsch, Kristian Skrede y Michael D. Ward. Spatial Regression Models. SAGE Publications, Inc, 2018.

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37

Regression Modelling Wih Spatial and Spatial-Temporal Data: A Bayesian Approach. Taylor & Francis Group, 2020.

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38

Li, Guangquan y Robert P. Haining. Regression Modelling Wih Spatial and Spatial-Temporal Data: A Bayesian Approach. Taylor & Francis Group, 2020.

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39

Li, Guangquan y Robert P. Haining. Regression Modelling Wih Spatial and Spatial-Temporal Data: A Bayesian Approach. Taylor & Francis Group, 2020.

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40

Modelling Spatial Processes: The Identification and Analysis of Spatial Relationships in Regression Residuals by Means of Moran's I (Lecture Notes in Earth Sciences). Springer, 2000.

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41

Sherman, Michael. Spatial Statistics and Spatio-Temporal Data: Covariance Functions and Directional Properties. Wiley & Sons, Incorporated, John, 2011.

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42

Sherman, Michael. Spatial Statistics and Spatio-Temporal Data: Covariance Functions and Directional Properties. Wiley & Sons, Incorporated, John, 2011.

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43

Sherman, Michael. Spatial Statistics and Spatio-Temporal Data: Covariance Functions and Directional Properties. Wiley & Sons, Incorporated, John, 2010.

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44

Spatial Regression Models for the Social Sciences. SAGE Publications, Incorporated, 2019.

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45

Brunsdon, Chris, Martin Charlton y A. Stewart Fotheringham. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley & Sons, Incorporated, John, 2007.

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46

Brunsdon, Chris, Martin Charlton y A. Stewart Fotheringham. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley & Sons, Incorporated, John, 2003.

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47

Spatial Regression Models (Quantitative Applications in the Social Sciences). Sage Publications, Inc, 2008.

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48

Bailey, Trevor y Tony Gatrell. Interactive Spatial Data Analysis Including Disk. Prentice Hall, 1996.

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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

Introducing Regression Analysis. Taylor & Francis Group, 2019.

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