Sentence examples for generalized regression analysis from inspiring English sources

We put forward GRASP as a new name encapsulating an existing concept that aims at making spatial predictions using generalized regression analysis.

We present generalized regression analysis and spatial prediction (GRASP) conceptually as a method for producing spatial predictions using statistical models, and introduce and demonstrate a specific implementation in Splus that facilitates the process.

As shown in Table  6, the generalized regression analysis displayed that BMI, ALT, and PLT were positively associated with the presence of NAFLD.

As shown in Table  3, the generalized regression analysis demonstrated that BMI, ALT, age, and PLT were positively associated with FLD in both genders, and MS was positively associated only in males.

Next, we will establish hypothesis on the basis of combing political efficacy, political participation, and relative literature, followed by analyzing influence of political efficacy and political participation on police trust via linear regression and generalized logistic regression analysis.

Thereafter, a generalized linear regression analysis was conducted using the functional approach: FAV = β 0 + β 1NDVId + μ, where perceived forage availability (FAV) was regressed against NDVI dynamics (NDVId) and μ represents the error term.

The effect of surgical diagnosis group on EQ-us/EQ-VAS was assessed by multivariable generalized linear regression analysis.

Potential risk factors on farm level were analysed by logistic regression (PROC LOGISTIC, SAS Institute Inc., 2004), and on animal level by generalized linear regression analysis accounting for farm effect (PROC GENMOD, SAS Institute Inc., 2004).

To test the relationship between having been in psychiatric inpatient treatment with the number of previous imprisonments found in bivariate analyses, we conducted a Poisson generalized linear regression analysis with previous imprisonments as the dependent variable.

The relationship was established by using generalized regression neural network analysis.

Because of different nature of data from each state, a different statistical analysis approach was employed for each state: an empirical Bayes, before-after analysis of Kansas data, an interrupted time series design and generalized linear segmented regression analysis of Michigan data, and a cross sectional analysis of Illinois data.