Results 241 to 250 of about 3,554,144 (293)

Nontraditional Regression Analyses

Ecology, 1993
Least—squares linear regression and multiple regression are among the most commonly used analytical techniques of ecologists. However, these techniques only address a portion of the possible applications of regression methods. We discuss two less commonly used regression analyses that could find wide application in ecology, logistic regression and ...
Joel C. Trexler, Joseph Travis
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Sensitivity Analyses for Ecological Regression

Biometrics, 2003
Summary.  In many ecological regression studies investigating associations between environmental exposures and health outcomes, the observed relative risks are in the range 1.0–2.0. The interpretation of such small relative risks is difficult due to a variety of biases—some of which are unique to ecological data, since they arise from within‐area ...
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Interpretation of Regressive Logistic Regression Coefficients in Analyses of Familial Data

Biometrics, 1998
Summary: Regressive logistic regression was introduced by \textit{G. E. Bonney} [ibid. 42, 611--625 (1986; Zbl 0625.62097)] and, due to its easy implementation using standard logistic regression computer packages, has been widely used in the analysis of binary familial disease traits.
FitzGerald, Patrick E. B., Knuiman, Matthew W.   +1 more
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Regression Analyses with Multiple Variables

2015
In the multivariate regression analysis we used the main risk-related variables (size, industry, age and market) to explain differences in volatility. Our results suggested that AIM stocks are significantly more volatile, although the difference is far smaller than that given by the simple ratio analysis.
John Board, Alfonso Dufour, Yusuf Hartavi, Charles Sutcliffe, Stephen Wells   +4 more
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On multiple regression analysis

Statistica Neerlandica, 1962
SummaryThe sums of squares associated with the independent variables in a multiple regression equation depend on the order in which these variables are introduced. Two methods have been proposed in the literature to avoid this inconvenience: “forward selection” or “backward elimination”.With forward selection the independent variables are introduced in
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Advances in Methodology for Random Regression Analyses*

Australian Journal of Experimental Agriculture, 2005
Random regression analyses have become standard methodology for the analysis of traits with repeated records that are thought of as representing points on a trajectory. Modelling curves as a regression on functions of a continuous covariable, such as time, for each individual, random regression models are readily implemented in standard, linear mixed ...
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Correlative and Regression Analyses

2014
Everything happens for a reason. That does not mean that some supernatural force or metaphysical entity is governing the events in our lives in some predestined game to which we respond, as if each person is the focal point of their own narcissistic universe, custom-tailored to guide us through existence.
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RADTI: regression analyses of diffusion tensor images

SPIE Proceedings, 2009
Diffusion tensor image (DTI) is a powerful tool for quantitatively assessing the integrity of anatomical connectivity in white matter in clinical populations. The prevalent methods for group-level analysis of DTI are statistical analyses of invariant measures (e.g., fractional anisotropy) and principal directions across groups.
Yimei Li, Hongtu Zhu, Yasheng Chen, Joseph G. Ibrahim, Hongyu An, Weili Lin, Colin Hall, Dinggang Shen   +7 more
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Regression analyses of prognostic factors in colorectal cancer

Journal of Surgical Oncology, 1988
AbstractIn a follow‐up study of 110 patients with colorectal cancer, age, sex, erythrocyte sedimentation rate (ESR), hemoglobin (Hb), leukocyte count, emergency operation, tumor site, Dukes' stage, and histologic grade were tested in survival analyses. Dukes' stage was a highly superior prognostic discriminator.
E, Hannisdal, G, Thorsen
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Problems with Regression Analyses

1984
There are many problems which can occur with econometric analyses. The problems arise when the basic assumptions upon which all the statistical calculations are based are violated. Autocorrelation, discussed at some length in the last chapter in the context of the Durbin-Watson statistic, is one of the three most often discussed data problems.
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