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Books on the topic 'Stepwise multiple linear regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

On stepwise procedures for some multiple inference problems. Alqvist & Wiksell International, 1989.

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2

Shelton, Katherine Lesley. An illustration of heteroscedasticity in the multiple linear regression model. The author], 1985.

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3

Karim, Samsul Ariffin Abdul, and Nur Fatonah Kamsani. Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3485-0.

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4

Zorn, Troy G. Utility of species-specific, multiple linear regression models for prediction of fish assemblages in rivers of Michigan's lower peninsula. Michigan Dept. of Natural Resources, Fisheries Division, 2004.

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5

Roback, Paul, and Julie Legler. Beyond Multiple Linear Regression. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429066665.

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6

Cheng, Russell. Nested Nonlinear Regression Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0015.

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Stepwise fitting of nonlinear nested regression models is considered in this chapter. The forward stepwise method of linear model building is used as far as possible. With linear models this is straightforward as there is in principle a free choice of the order that individual terms or factors are selected for inclusion. The only real issue is that sufficient submodels are examined to ensure that those finally selected really are amongst the best. The nonlinear case is not so straightforward, as embeddedness and parameter indeterminacy issues impose restrictions on the order in which steps can

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7

Gelman, Andrew, and 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 resul

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8

J, Niccolucci Michael, Schuster Ervin G, and Intermountain Research Station (Ogden, Utah), eds. Identifying proxy sets in multiple linear regression: An aid to better coefficient interpretation. U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1993.

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9

Robertson, Rob. Effects of collinearity, sample size, multiple correlation, and predictor-criterion correlation salience on the order of variable entry in stepwise regression. 1997.

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10

Higham, Ronald P. A multiple linear regression model for predicting zone A retention by military occupational specialty. 1986.

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11

Miksza, Peter, and Kenneth Elpus. Regression. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0010.

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This chapter presents the logic and technique of analyzing data using simple linear regression and multiple linear regression. Regression is a remarkably versatile statistical procedure that can be used not only to understand whether or not variables are related to each other (as in correlation) but also for providing estimates of the direction of the relationship and of the degree to which the variables are related. Beginning with a simple bivariate case analyzing a single predictor on a single outcome, the flexibility and ability for regression to analyze increasingly complex data, including

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12

Karim, Samsul Ariffin Abdul, and Nur Fatonah Kamsani. Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model: Case Study in Perak River, Malaysia. Springer, 2020.

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13

Center, Lewis Research, ed. A multiple linear regression analysis of hot corrosion attack on a series of nickel base turbine alloys. National Aeronautics and Space Administration, Lewis Research Center, 1985.

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14

Crafton, R. Eliot. Using Multiple Linear Regression Models to Identify Factors Underlying Avian Species Imperilment in Sub-Saharan Africa and Europe. INTECH Open Access Publisher, 2011.

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15

Miles, Jeremy. General and generalised linear models. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0017.

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This chapter discusses general and generalised linear models (GLM and GLZ respectively). It outlines GLMs (mean, properties of GLMs and the mean), samples and populations, comparison of two groups of data, multiple regression and the GLM, analysis of variance (ANOVA) and the GLM, GLM in SPSS, and the GLZ).

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16

Lee, Patricia, Donald Stewart, and Stephen Clift. Group Singing and Quality of Life. Edited by Brydie-Leigh Bartleet and Lee Higgins. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190219505.013.22.

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International research has broadly reported positive effects of singing on health. Choral singing, a social activity, can contribute to health and social and emotional well-being through enhancing individual and social variables, such as a sense of motivation, personal worth, concentration, and social engagement. This cross-sectional study aimed to establish a quantitative model to explain how multiple attributes of choral singing interact to impact on different dimensions of health and well-being. Using data from an Australian subsample within a multinational project, the results, from a seri

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17

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

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