Using EDA, ANOVA and Regression ot Optimise some Microbiology Data

Regression and ANOVA: An Integrated Approach Using SAS Software

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Using Behaviour Inference to Optimise Regression Test Sets

Where a software component is updated or replaced regression testing is required. Regression test sets can contain considerable redundancy. This is especially true in the case where no formal regression test set exists and the new component must instead be compared against patterns of behaviour derived from in-use log data from the previous version. Previous work has applied search-based techni...

Topic 3: ANOVA, Regression, and Forecasting

Using ANOVA to analyze microarray data.

ANOVA provides a general approach to the analysis of single and multiple factor experiments on both one- and two-color microarray platforms. Mixed model ANOVA is important because in many microarray experiments there are multiple sources of variation that must be taken into consideration when constructing tests for differential expression of a gene. The genome is large, and the signals of expre...

Functional ANOVA Models for Generalized Regression

Functional ANOVA models are considered in the context of generalized regression, which includes logistic regression, probit regression and Poisson regression as special cases. The multivariate predictor function is modeled as a speci ed sum of a constant term, main e ects and interaction terms. Maximum likelihood estimates are used, where the maximizations are taken over suitably chosen approxi...