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Books on the topic 'Multiple Linear Stepwise Regression'

Author: Grafiati

Published: 5 June 2025

Last updated: 11 July 2025

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1

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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2

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

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3

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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4

Booth, Gordon D. Identifying proxy sets in multiple linear regression: An aid to better coefficient interpretation. U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1994.

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5

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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6

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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7

Scott Jones, Julie. Learn to Clean and Prepare Data for a Multiple Linear Regression in SPSS Using Data From the Health Survey for England (2011) (Teaching Dataset). SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529605327.

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8

Multiple Linear Regression. Starttech Educational Services LLP, 2020. http://dx.doi.org/10.4135/9781529630138.

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9

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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10

Creating Multiple Linear Regression Model. Starttech Educational Services LLP, 2020. http://dx.doi.org/10.4135/9781529630251.

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11

Econometrics I: The multiple linear regression. Fundación Universitaria San Pablo CEU, 2019.

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12

Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models In. CRC Press LLC, 2024.

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13

Roback, Paul, and Julie Legler. Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R. Taylor & Francis Group, 2021.

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14

Roback, Paul, and Julie Legler. Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R. Taylor & Francis Group, 2021.

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15

Roback, Paul, and Julie Legler. Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R. Taylor & Francis Group, 2021.

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16

Roback, Paul, and Julie Legler. Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R. Taylor & Francis Group, 2021.

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17

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 results would have to differ in order for the study’s conclusions to change. The chapter includes examples that revisit the simple linear model of height and income, involve the class in models of exam scores, and fit a nonlinear model (for more advanced classes) for golf putting.

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18

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 be taken to build a valid model, as certain parameters can only be meaningfully included if other specific parameters are definitely present. A systematic way of building valid nonlinear models of increasing complexity is described and illustrated by two examples using real data. A brief review of non-nested model building is also given.

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19

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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20

Siddiqui, Aqsa, Nausheen Shakeel, and Sahar Butt. Determining Factors Causing Child Labor by Using Multiple Linear and Logistic Regression Analysis. GRIN Verlag GmbH, 2016.

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21

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

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22

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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23

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 binary outcomes, is discussed. Particular attention is paid to the ability of regression to be used to estimate the effect of a predictor on an outcome while statistically “controlling” for the values of other observed variables.

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24

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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25

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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26

Fieberg, John. Statistics for Ecologists: A Frequentist and Bayesian Treatment of Modern Regression Models. University of Minnesota Libraries Publishing, 2024. http://dx.doi.org/10.24926/9781959870029.

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Ecological data pose many challenges to statistical inference. Most data come from observational studies rather than designed experiments; observational units are frequently sampled repeatedly over time, resulting in multiple, non-independent measurements; response data are often binary (e.g., presence-absence data) or non-negative integers (e.g., counts), and therefore, the data do not fit the standard assumptions of linear regression (Normality, independence, and constant variance). This book will familiarize readers with modern statistical methods that address these complexities using both frequentist and Bayesian frameworks.

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27

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 series of stepwise hierarchical regression models, showed that choral singing benefited the choir members’ physical and psychological health and well-being through social engagement and a sense of positive identity. Choral singing also impacted social health and well-being positively by promoting feelings of excitement and importance to life, as well as longer duration of involvement in the choir. This study will contribute to developing targeted group singing or social activities to promote continued physical, psychological, and social health.

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28

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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