Analysis of variance (ANOVA), the workhorse analysis of experimental designs, consists of F-tests of main effects and interactions. Yet, testing, including traditional ANOVA, has been recently critiqued on a number of theoretical and practical grounds. In light of these critiques, model comparison and model selection serve as an attractive alternative. Model comparison differs from testing in t...

One of the hallmarks of statistical thinking is the importance of measuring and understanding variability. The Shewhart Control Chart, which separates special cause from common cause variation, is one of the most important tools for understanding the current state of a process. The analysis of variance (ANOVA) is another statistical tool for splitting variability into component sources. These c...

Stroke and heart disease are among the most com-mon outcomes of hypertension. Each year, disease, stroke, other cardiovascular disorders claim lives more than 877,500 people in United States, making them first fifth leading causes death, so being able to pre- dict early helps save lives. A lot research has been done reach this goal. Machine learning models mostly used for purpose. For time stud...

This Handout illustrates traditional mixed-model ANOVA analyses of the data from my study of host effects on parasitoid larvae (Data Set 6) when the host species is treated as a random factor. Note: Treating species as random would not be reasonable in reality, given that these two moth species are the only common hosts for this parasitoid in North Carolina, where I conducted this study. It als...

Location S1A1 S1A2 S2A1 S2A2 Fig. 1 Design for the estimation of the random components of uncertainty arising from both the sampling and the analytical procedures. (A) Balanced design. (B) Unbalanced design. An empirical estimation of the random components of measurement uncertainty arising from the sampling and analytical processes can be made via an experiment involving replication. Analysis ...