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ANCOVA evaluates whether the means of a dependent variable (DV) are equal across levels of one or more categorical independent variables (IV) and across one or more continuous variables. For example, the categorical variable(s) might describe treatment and the continuous variable(s) might be covariates (CV)'s, typically nuisance variables; or ...
The aim of such techniques as ANCOVA is to remove the effects of such uncontrolled variation, in order to increase statistical power and to ensure an accurate measurement of the true relationship between independent and dependent variables. [8] An example is provided by the analysis of trend in sea-level by Woodworth (1987). [9]
Technology files and design rules are essential building blocks of the integrated circuit design process. Their accuracy and robustness over process technology, its variability and the operating conditions of the IC—environmental, parasitic interactions and testing, including adverse conditions such as electro-static discharge—are critical in determining performance, yield and reliability.
In statistics, one-way analysis of variance (or one-way ANOVA) is a technique to compare whether two or more samples' means are significantly different (using the F distribution). This analysis of variance technique requires a numeric response variable "Y" and a single explanatory variable "X", hence "one-way".
For example, inversion-layer mobility modeling, or the modeling of mobility and its dependence on physical parameters, ambient and operating conditions is an important topic both for TCAD (technology computer aided design) physical models and for circuit-level compact models. However, it is not accurately modeled from first principles, and so ...
When a one-way ANOVA is performed, samples are assumed to have been drawn from distributions with equal variance. If this assumption is not valid, the resulting F-test is invalid. The Brown–Forsythe test statistic is the F statistic resulting from an ordinary one-way analysis of variance on the absolute deviations of the groups or treatments ...
One can also test if there is a group effect after adjusting for covariates. For this, follow the procedure above but substitute ^ with the predictions of the general linear model, containing the group and the covariates, and substitute ¯ with the predictions of the general linear model containing only the covariates (and an intercept).
3D rendering of a car in CAD software with boundary representation. Also important to the development of CAD was the development in the late 1980s and early 1990s of B-rep solid modeling kernels (engines for manipulating geometrically and topologically consistent 3D objects), Parasolid (ShapeData), and ACIS (Spatial Technology Inc.). These ...