Quantifying the Relative Importance of Predictors in Multiple Linear Regression Analyses for Public Health Studies

Chao, Yi Chun E; Zhao, Yue; Kupper, Lawrence L.; Nylander‐French, Leena A.

doi:10.1080/15459620802225481

Cited by 60 publications

(39 citation statements)

References 41 publications

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“…Nevertheless, for every individual for which data were available multiple regression models were calculated with log 10 total cycle duration as the dependent variable and the durations of log 10 FC, log 10 SC, log 10 SO, and log 10 FO phases as the independent variables. The relative importance of the predictor variables in each model was assessed using Johnson's 'relative weight' (Chao et al, 2008;Johnson, 2000), "the proportionate contribution each predictor makes to the squared multiple correlation coefficient when that C. F. Ross and others coefficient is expressed as the sum of contributions from the separate predictors" (Johnson, 2000). To calculate this relative weight, the independent variables (in his case the gape cycle phase durations) were replaced with a set of variables that are highly correlated with the original independents but which are not correlated with each other.…”

Section: Discussionmentioning

confidence: 99%

Chewing variation in lepidosaurs and primates

Ross

Baden

Georgi

et al. 2010

Journal of Experimental Biology

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SUMMARY Mammals chew more rhythmically than lepidosaurs. The research presented here evaluated possible reasons for this difference in relation to differences between lepidosaurs and mammals in sensorimotor systems. Variance in the absolute and relative durations of the phases of the gape cycle was calculated from kinematic data from four species of primates and eight species of lepidosaurs. The primates exhibit less variance in the duration of the gape cycle than in the durations of the four phases making up the gape cycle. This suggests that increases in the durations of some gape cycle phases are accompanied by decreases in others. Similar effects are much less pronounced in the lepidosaurs. In addition, the primates show isometric changes in gape cycle phase durations, i.e. the relative durations of the phases of the gape cycle change little with increasing cycle time. In contrast, in the lepidosaurs variance in total gape cycle duration is associated with increases in the proportion of the cycle made up by the slow open phase. We hypothesize that in mammals the central nervous system includes a representation of the optimal chew cycle duration maintained using afferent feedback about the ongoing state of the chew cycle. The differences between lepidosaurs and primates do not lie in the nature of the sensory information collected and its feedback to the feeding system, but rather the processing of that information by the CNS and its use feed-forward for modulating jaw movements and gape cycle phase durations during chewing.

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Section: Discussionmentioning

confidence: 99%

Chewing variation in lepidosaurs and primates

Ross

Baden

Georgi

et al. 2010

Journal of Experimental Biology

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“…The importance of the predictors indicates the influence of the parameters on the model's efficiency coefficients (NSE, NSElog). The regression technique is used among other studies for evaluation of parameter influence on the results [44,101,102]. In addition, [44] noticed that parameter sensitivity analyses contribute to output uncertainty reduction and identifiability of parameters which require additional research.…”

Section: Resultsmentioning

confidence: 99%

Simulating Water Resource Availability under Data Scarcity—A Case Study for the Ferghana Valley (Central Asia)

Radchenko

Forkutsa

Frede

2014

Water

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Glaciers and snowmelt supply the Naryn and Karadarya rivers, and about 70% of the water available for the irrigated agriculture in the Ferghana Valley. Nineteen smaller catchments contribute the remaining water mainly from annual precipitation. The latter will gain importance if glaciers retreat as predicted. Hydrological models can visualize such climate change impacts on water resources. However, poor data availability often hampers simulating the contributions of smaller catchments. We tested several data pre-processing methods (gap filling, MODAWEC (MOnthly to DAily WEather Converter), lapse rate) and their effect on the performance of the HBV (Hydrologiska Byråns Vattenavdelning)-light model. Monte Carlo simulations were used to define parameter uncertainties and ensembles of behavioral model runs. Model performances were evaluated by constrained measures of goodness-of-fit criteria (cumulative bias, coefficient of determination, model efficiency coefficients (NSE) for high flow and log-transformed flow). The developed data pre-processing arrangement can utilize data of relatively poor quality (only monthly means or daily data with gaps) but still provide model results with NSE between 0.50 and 0.88. Some of these may not be accurate enough to directly guide water management applications. However, the pre-processing supports producing key information that may initiate rigging OPEN ACCESSWater 2014, 6 3271 of monitoring facilities, and enable water management to respond to fundamentally changing water availability.

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“…Except for these four factors, the rest of the five factors were statistically analysed for their relative importance in contributing to absorbency by calculating Johnson's relative weight (ε) as some of the predictor variables are correlated with each other. The most suitable method from the viewpoint of computation and accuracy is to find the relative importance of the predictor variables in multiple regressions by using relative importance weights (Chao et al, 2010).…”

Section: Relative Importance Of Predictor Variablementioning

confidence: 99%

Factors Contributing to Absorbency Behaviour of Pile Fabrics

Behera

Singh

2014

Research Journal of Textile and Apparel

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The objective of this research paper is to investigate the important factors that contribute to the absorbency characteristics of terry fabric in order to produce highly absorbent terry towels by using suitable raw materials and changing the fabric constructional parameters. Yarns produced from two varieties of cotton (100% J-34 and MCU-5) and their blends with bamboo and poly vinyl alcohol (PVA) with different counts, twist and number of plies are used to prepare terry fabric of varying loop densities, loop lengths and loop shape factors. The water absorption rate and the total amount of water absorbed are measured by a gravimetric absorbency testing system (GATS). It is found that loop density is the most important parameter for water absorption rate followed by loop length and yarn twist. For the total amount of water absorbed, loop density is again the most important parameter followed by yarn twist, loop shape factor and number of ply in the pile yarn. Furthermore, a Box-Behnken statistical design with 3 factors and 3 levels is used to determine the optimum construction parameters to obtain the desired absorbency characteristics of terry towels and also to see the interaction effect of the various factors.

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Quantifying the Relative Importance of Predictors in Multiple Linear Regression Analyses for Public Health Studies

Cited by 60 publications

References 41 publications

Chewing variation in lepidosaurs and primates

Chewing variation in lepidosaurs and primates

Simulating Water Resource Availability under Data Scarcity—A Case Study for the Ferghana Valley (Central Asia)

Factors Contributing to Absorbency Behaviour of Pile Fabrics

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