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Books on the topic 'Modelo de regresión'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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1

Borowiak, Dale S. Model discrimination for nonlinear regression models. M. Dekker, 1989.

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2

Breen, Richard. Regression Models. SAGE Publications, Inc., 1996. http://dx.doi.org/10.4135/9781412985611.

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3

Ward, Michael, and Kristian Gleditsch. Spatial Regression Models. SAGE Publications, Inc., 2008. http://dx.doi.org/10.4135/9781412985888.

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4

Marsh, Lawrence, and David Cormier. Spline Regression Models. SAGE Publications, Inc., 2002. http://dx.doi.org/10.4135/9781412985901.

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5

Ward, Michael Don. Spatial regression models. Sage Publications, 2008.

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6

R, Cormier David, ed. Spline regression models. Sage Publications, 2002.

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7

Hilbe, Joseph. Logistic regression models. Chapman & Hall/CRC, 2009.

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8

Newbold, Paul, and Theodore Bos. Stochastic Parameter Regression Models. SAGE Publications, Inc., 1985. http://dx.doi.org/10.4135/9781412985994.

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9

Allison, Paul. Fixed Effects Regression Models. SAGE Publications, Inc., 2009. http://dx.doi.org/10.4135/9781412993869.

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10

William, Wasserman, and Kutner Michael H, eds. Applied linear regression models. 2nd ed. Irwin, 1989.

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11

Theodore, Bos, ed. Stochastic parameter regression models. Sage Publications, 1985.

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12

Inc, SPSS. SPSS regression models 12.0. Prentice Hall College Division, 2004.

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13

Kutner, Michael H. Applied linear regression models. 4th ed. McGraw-Hill, 2003.

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14

Theodore, Bos, ed. Stochastic parameter regression models. Sage Publications, 1985.

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15

Chris, Nachtsheim, and Neter John, eds. Applied linear regression models. 4th ed. McGraw-Hill/Irwin, 2004.

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16

Inc, SPSS, ed. SPSS regression models 9.0. SPSS Inc., 1999.

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17

Inc, SPSS, ed. SPSS regression models 12.0. SPSS Inc., 2003.

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18

John, Neter, ed. Applied linear regression models. 3rd ed. Irwin, 1996.

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19

Inc, SPSS, ed. SPSS regression models 13.0. SPSS Inc., 2004.

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20

John, Neter, ed. Applied linear regression models. 3rd ed. Irwin, 1996.

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21

Godfrey, Leslie. Bootstrap Tests for Regression Models. Palgrave Macmillan UK, 2009. http://dx.doi.org/10.1057/9780230233737.

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22

Caroni, Chrysseis. First Hitting Time Regression Models. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119437260.

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23

Pankratz, Alan. Forecasting with Dynamic Regression Models. John Wiley & Sons, Inc., 1991. http://dx.doi.org/10.1002/9781118150528.

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24

Fahrmeir, Ludwig. Regression: Models, Methods and Applications. Springer Berlin Heidelberg, 2013.

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25

Harvey, Andrew. Seasonality in dynamic regression models. London School of Economics Centre for Economic Performance, 1994.

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26

Harvey, A. C. Seasonality in dynamic regression models. Suntory-Toyota International Centre for Economics and Related Disciplines, London School of Economics, 1993.

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27

Godfrey, L. G. Bootstrap tests for regression models. Palgrave Macmillan, 2009.

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28

Fry, John M. (John Michael), 1980-, ed. Regression: Linear models in statistics. Springer, 2010.

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29

Moser, Barry Kurt. Linear models: A mean model approach. Academic Press, 1996.

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30

Politis, Dimitris N. Model-Free Prediction and Regression. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21347-7.

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31

Longford, Nicholas T. Random coefficient models. Clarendon Press, 1993.

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32

Draper, Norman Richard. Model selection problems. University of Toronto, Dept. of Statistics, 1986.

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33

Speelman, Dirk, Kris Heylen, and Dirk Geeraerts, eds. Mixed-Effects Regression Models in Linguistics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69830-4.

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34

Green, Peter J., and Bernard W. Silverman. Nonparametric Regression and Generalized Linear Models. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-4473-3.

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35

Kedem, Benjamin, and Konstantinos Fokianos. Regression Models for Time Series Analysis. John Wiley & Sons, Inc., 2002. http://dx.doi.org/10.1002/0471266981.

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36

Ejercicios resueltos de econometría: El modelo de regresión múltiple. Delta Publicaciones, 2006.

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37

López Naranjo, José Isabel, and Rodolfo Osorio Osorio. Análisis de regresión aplicado. Universidad Juárez Autónoma de Tabasco, 2018. http://dx.doi.org/10.19136/book.93.

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El análisis de regresión es una herramienta de la inferencia estadística aplicada que permite ajustar conjunto de datos proveniente de trabajos de investigación con propósitos de predicción. En esta obra se trata una diversidad de técnicas útiles en esta tarea y se proporcionan las estrategias que permiten la selección de las variables, el tamaño de muestra y el mejor modelo; aquél que explique de mejor manera la relación entre las variables estudiadas. Esta técnica es aplicable en cualquier área de las ciencias: agropecuarias, biológica, económica, salud, genética, matemátic

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38

Jones, Herbert. Ciencia de Datos para Empresas: Modelo Predictivo, Minería de Datos, análisis de Datos, análisis de Regresión, Consulta de Bases de Datos y Aprendizaje Automático para Principiantes. Independently Published, 2019.

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39

Borowiak, Dale S. Model Discrimination for Nonlinear Regression Models. Taylor & Francis Group, 2020.

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40

Borowiak, Dale S. Model Discrimination for Nonlinear Regression Models. Taylor & Francis Group, 2020.

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41

Borowiak, Dale S. Model Discrimination for Nonlinear Regression Models. Taylor & Francis Group, 2020.

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42

Borowiak, Dale S. Model Discrimination for Nonlinear Regression Models. Taylor & Francis Group, 2020.

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43

Rueda Barrios, Gladys Elena, Jairo Alexander González Bueno, and Marianela Luzardo Briceño. Factores determinantes de la competitividad y sostenibilidad de las empresas del sector agrícola en Santander. Editorial Universidad Pontificia Bolivariana, 2022. http://dx.doi.org/10.18566/978-628-500-034-8.

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El presente trabajo analiza los factores asociados con la estructura empresarial, la productividad y la innovación relacionados con la competitividad y sostenibilidad del sector agrícola en Santander, Colombia. La metodología aplicada en el estudio es de tipo cuantitativo, con un alcance descriptivo, exploratorio y correlacional, y un diseño de corte no experimental, a partir de la aplicación de una encuesta que caracterizó las variables generales, independientes y dependientes del modelo propuesto en una muestra de 70 empresas agrícolas que representan las provincias de Santander, y el anális

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44

Quintero, Helmer. Religiosidad, espiritualidad y salud mental. Sedunac, 2020. http://dx.doi.org/10.35997/libroreligiosidadespiritualidad.

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El interés principal del estudio lo constituye la relación entre la espiritualidad, la religiosidad y la orientación religiosa con la salud mental y el bienestar psicológico, en las ciudades de Medellín (Colombia), y Buenos Aires (Argentina). Se estimaron las correlaciones en una muestra de 306 participantes pertenecientes a tres grupos religiosos: pentecostales (trinitarios y unitarios), adventistas y católicos. En la muestra, el 40% fueron hombres y el 60% mujeres, con edades comprendidas entre los 25 y los 80 años (M=41 años). Con el fin de conocer las variables predictoras, tanto para la s

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45

Econometria I: Modelos de Regresión. Independently Published, 2017.

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46

Hilbe, Joseph M. Logistic Regression Models. Chapman and Hall/CRC, 2009. http://dx.doi.org/10.1201/9781420075779.

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47

Spatial Regression Models. 2008.

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48

Logistic Regression Models. Chapman & Hall/CRC, 2009.

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49

Hilbe, Joseph M. Logistic Regression Models. Taylor & Francis Group, 2009.

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50

Hilbe, Joseph M. Logistic Regression Models. Taylor & Francis Group, 2017.

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