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Books on the topic 'Multiple linear regression mode'

Author: Grafiati

Published: 8 September 2021

Last updated: 1 February 2022

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1

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

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2

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

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3

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

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4

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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5

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

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6

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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7

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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8

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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9

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. Ogden, UT (324 25th St., Ogden 84401): U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1993.

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10

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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11

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

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12

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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13

Wright, A. G. Linear performance. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199565092.003.0009.

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This chapter is concerned with a single consideration: the degree of proportionality between a light signal and its resulting electrical output. This is formally referred to as linearity, which depends on the suitability of the chosen PMT and the mode of operation (pulsed or analogue). Applications fall into two groups: analogue operation (DC) and transient applications. Linearity in a pulsed mode of operation concerns both pulse height (charge) and the rate of events. Generally, in the DC mode, only the mean anode current is relevant. Methods for determining both forms of non-linearity are presented, based on actual measurements. Test methods using multiple light sources, bootstrapping, single step (piggyback), and shot noise are investigated. A method for uncovering non-linearity in high-Z scintillators by using coincident gamma emissions (60Co) is demonstrated. An analytical means for correcting results at the 1 % level is provided.

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14

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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15

Alexander, Peter D. G., and Malachy O. Columb. Presentation and handling of data, descriptive and inferential statistics. Edited by Jonathan G. Hardman. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0028.

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The need for any doctor to comprehend, assimilate, analyse, and form an opinion on data cannot be overestimated. This chapter examines the presentation and handling of such data and its subsequent statistical analysis. It covers the organization and description of data, measures of central tendency such as mean, median, and mode, measures of dispersion (standard deviation), and the problems of missing data. Theoretical distributions, such as the Gaussian distribution, are examined and the possibility of data transformation discussed. Inferential statistics are used as a means of comparing groups, and the rationale and use of parametric and non-parametric tests and confidence intervals is outlined. The analysis of categorical variables using the chi-squared test and assessing the value of diagnostic tests using sensitivity, specificity, positive and negative predictive values, and a likelihood ratio are discussed. Measures of association are covered, namely linear regression, as is time-to-event analysis using the Kaplan–Meier method. Finally, the chapter discusses the statistical analysis used when comparing clinical measurements—the Bland and Altman method. Illustrative examples, relevant to the practice of anaesthesia, are used throughout and it is hoped that this will provide the reader with an outline of the methodologies employed and encourage further reading where necessary.

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16

Lin, Erika T. Festivity. Edited by Henry S. Turner. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199641352.013.11.

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This chapter locates festivity within the early modern theatre. Through an analysis of Thomas Dekker’sThe Shoemaker’s Holiday, it considers how holidays functioned not as communal rituals but as commodified entertainments; how one-off experiences tied to the cyclical rhythms of the seasons came to be understood as performances that could be enacted year-round—that is, rendered intelligible as theatre within linear models of historical time; and how playing came to be imagined not only as a mode of sociality but also as a vendible commodity. The chapter shows how the commercialization of theatre altered the economic exchanges at the heart of traditional festivity and argues that the professional stage was engaged in a complex project to situate its own performances in relation to existing festive practices. By focusing on early modern contexts, it highlights the ways in which theatricality serves and produces multiple—and, from a modern perspective, often unexpected—cultural functions and effects.

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17

Hingston, Kylee-Anne. Articulating Bodies. Liverpool University Press, 2019. http://dx.doi.org/10.3828/liverpool/9781789620757.001.0001.

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Articulating Bodies investigates the contemporaneous developments of Victorian fiction and disability’s medicalization by focusing on the intersection between narrative form and the body. The book examines texts from across the century, from Frederic Shoberl’s 1833 English translation of Victor Hugo’s Notre-Dame de Paris (1831) to Arthur Conan Doyle’s Sherlock Holmes story “The Adventure of the Crooked Man” (1893), covering genres that typically relied upon disabled or diseased characters. By tracing the patterns of focalization and narrative structure across six decades of the nineteenth century and across six genres, Articulating Bodies shows the mutability of the Victorians’ understanding of the human body’s centrality to identity—an understanding made mutable by changes in science, technology, religion, and class. It also demonstrates how that understanding changed along with developing narrative styles: as disability became increasingly medicalized and the soul increasingly psychologized, the mode of looking at deviant bodies shifted from gaping at spectacle to scrutinizing specimen, and the shape of narratives evolved from lengthy multiple-plot novels to slim case studies. Moreover, the book illustrates that, despite this overall linear movement from spectacle to specimen in literature and culture, individual texts consistently reveal ambivalence about categorizing the body, positioning some bodies as abnormally deviant while also denying the reality or stability of normalcy. Bodies in Victorian fiction never remain stable entities, in spite of narrative drives and the social, medical, or scientific discourses that attempted to control and understand them.

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