Last week we learned about one-way designs. They examine several levels of a single factor. For instance, we might examine the effect of StudyType (independent variable) on LanguagePerformance (dependent variable). Classroom study Self-study Control Perhaps we think that the StudyType manipulation might only affect one gender or the other. We could do a two-way design whether a second factor, G...

Multiple contrast tests can be used to test arbitrary linear hypotheses by providing local and global test decisions as well as simultaneous confidence intervals. The ANOVA-F-test on the contrary can be used to test the global null hypothesis of no treatment effect. Thus, multiple contrast tests provide more information than the analysis of variance (ANOVA) by offering which levels cause the si...

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...