The phrase "analysis of deviance test" is correct and usable in written English.
It can be used in contexts related to statistical analysis or research methodologies, particularly in social sciences or psychology.
Example: "The analysis of deviance test revealed significant differences between the groups in the study."
Alternatives: "deviance analysis test" or "test of deviance analysis".
A reduced model was compared with a non-factor model with only interception using the analysis of deviance test by the procedure "anova".
In adjusted analysis, the successive columns provide the HR of elevated GP88 adjusted for each indicator with its P-value and 95% CI and the P-value of the analysis of deviance test statistics for interaction between GP88 and that indicator.
In adjusted analysis, the successive columns provide the HR of elevated GP88 adjusted for each indicator along with its P-value and 95% CI and the interaction P-value for the analysis of deviance test statistics for interaction between GP88 and that indicator.
We then used an analysis-of-deviance approach to test whether a nonlinear function improved the model fit compared with a linear term.
We tested the effect of different lag times on NHL risk through analysis of deviance (ANODEV), testing for significant differences between model deviances from the model with no smoothing term and from the five models with smoothing terms (one for time at diagnosis, four with lag times).
We tested the influence of the explanatory variables by means of deviance tests.
As indicator for the improvement from the null model to the two-level model, we calculated McFadden's (1973) adjusted R-squared (= 1−[−2LL2-level model/−2LLnull model] = 1−[9,827.192−10,039.848]) and found an error reduction of only 2.1percentt, but which remains significant by analysis of deviance (χ2-test).
The effects of stage or storage period were analysed using an analysis of deviance (type II test) with F values.
We used analysis of deviance residuals to test for possible violations of the proportional hazards assumption for all Cox models.
Energetic status was the most important factor in predicting the cause of death, with regional and seasonal differences (analysis of deviance, chi-square test for differences between models, terms added sequentially, p < 0.05).
Consequently, we used analysis of deviance to formally test the hypothesis that the slopes and intercepts of the regressions did not vary across quarters, using a Generalized Linear Model (GLM) framework (Crawley, 2007, p. 516).
