Sentence examples for models with outcome from inspiring English sources

Table 3 Variables in the final participation and performance models with outcome variables in italics Model Structure Variables Level   Participation Performance 1 Assessment Participation (0 or 1) Score 0 to 100%     Assessment condition Assessment     and timing timing 2 Students Course grade Assessment       condition     Gender   3 Sections Recommended practices (CBT only) Course type.

Bivariate and multivariate linear regression models with outcome of increased fibrosis score as a continuous variable were also examined.

The reliability and accuracy of predictions of the developed models were examined by using statistical and graphical methods as well as comparing the results of the models with outcomes of recently developed literature correlations.

There were four final models with outcomes of, respectively, borderline disease or worse; mild dyskaryosis or worse; moderate dyskaryosis or worse and severe dyskaryosis or worse.

To adjust for regression to the mean and allow effect estimates to be interpreted as outcome changes from baseline to follow-up, linear regression models with outcomes measured at follow-up were adjusted for the baseline value of the outcome.

Squares, 1T2k60 model with outcome measure V T; triangles, 1T2k10 model with outcome measure K 1; circles, 2T4kVTnsfix model with outcome measure BPND; crosses, 2T4kVTnsfix model with outcome measure V T. This study evaluated test-retest variability of (R -[11C]verapamil data using seveR -[11C]verapamilc modata.

Statistical analysis was done using STATA12™.Independent variables identified as significant in a bivariate analysis were included as co-variates in a logistic regression model with outcome as the dependent variable.

This is a generalised linear model with outcome d ki with a Poisson error structure and a link function ln(μ ki −d ki *) with offset ln y ki ).

This is an ordinary logistic regression model with outcome disease present (y/n) and one covariable (index test result, positive or negative), with weights for cases and controls.

Variables defined by discriminant analysis were entered as predictors in a multiple logistic regression model with outcome as the dependent variable (Table 3).

A multivariable random effects logistic regression model with outcome survival yes/no was constructed by manual forward selection by offering variables selected from the univariable analyses one-at-a-time to the model by ascending univariable P-value.