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Books on the topic 'Spatial analysis (Statistics) Regression analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 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, and 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. 3rd 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., and Vining G. Geoffrey 1954-, eds. Introduction to linear regression analysis. 5th 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. 2nd ed. Hoboken, NJ: Wiley, 2012.

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17

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

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18

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

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19

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

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20

R, Wolf Paul, ed. Adjustment computations: Spatial data analysis. 5th 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, and Clark Virginia 1928-, eds. Applied statistics: Analysis of variance and regression. 3rd ed. Hoboken, N.J: Wiley-Interscience, 2004.

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23

Dunn, Olive Jean. Applied statistics: Analysis of variance and regression. 2nd 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. 2nd ed. Boca Rataon, Fla: CRC Press, 2011.

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27

Ghilani, Charles D. Adjustment computations: Spatial data analysis. 4th 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. 6th ed. Upper Saddle River, N.J: Prentice Hall, 2003.

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30

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

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31

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

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32

G, Amrhein Carl, and 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, and 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, and 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, and 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, and 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, and 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, and 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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Abstract:

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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