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Books on the topic 'Multiple linear regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 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

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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13

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

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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

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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18

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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19

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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20

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

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21

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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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

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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24

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

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25

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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26

Khan, Aman, and Kenneth A. Scorgie. Forecasting Government Budgets. The Rowman & Littlefield Publishing Group, 2022. https://doi.org/10.5040/9781666990355.

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Forecasting is integral to all governmental activities, especially budgetary activities. Without good and accurate forecasts, a government will not only find it difficult to carry out its everyday operations but will also find it difficult to cope with the increasingly complex environment in which it has to operate. This book presents, in a simple and easy to understand manner, some of the commonly used methods in budget forecasting, simple as well as advanced. The book is divided into three parts: It begins with an overview of forecasting background, forecasting process, and forecasting metho

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