Download the file ANOVA1.sas from my SAS programs page and run it. The contrived data (I created them with SAS' normal random number generator) are within the program. We shall imagine that we are evaluating the effectiveness of a new drug (Athenopram HBr) for the treatment of persons with depressive and anxiety disorders. Our independent variable is the daily dose of the drug given to such per...

Analysis of variance (ANOVA) is one of the most frequently used statistical methods in medical research. The need for ANOVA arises from the error of alpha level inflation, which increases Type 1 error probability (false positive) and is caused by multiple comparisons. ANOVA uses the statistic F, which is the ratio of between and within group variances. The main interest of analysis is focused o...

Eta squared is a popular effect size, but contains positive bias. Bootstrapping can be used to remove the bias from eta squared. Compared epsilon and omega squared, bootstrap correction does not make distributional assumption, it easy implement. A real example computer simulations are included illustrate its application. The bias-corrected shows very...

Spam is commonly defined as unwanted e-mails and it became a global threat against e-mail users. Although, Support Vector Machine (SVM) has been commonly used in e-mail spam classification, yet the problem of high data dimensionality of the feature space due to the massive number of e-mail dataset and features still exist. To improve the limitation of SVM, reduce the computational complexity (e...

For one-way fixed effects ANOVA, it is well known that the conventional F test of the equality of means is not robust to unequal variances, and numerous methods have been proposed for dealing with heteroscedasticity. On the basis of extensive empirical evidence of Type I error control and power performance, Welch's procedure is frequently recommended as the major alternative to the ANOVA F test...

The use of the fuzzy scale ofmeasurement to describe an important number of observations from real-life attributes or variables is first explored. In contrast to other well-known scales (like nominal or ordinal), a wide class of statistical measures and techniques can be properly applied to analyze fuzzy data. This fact is connected with the possibility of identifying the scale with a special s...