A Robust Root Mean Square Standardized Effect Size in One-Way Fixed-Effects ANOVA

A Robust Root Mean Square Standardized Effect Size in One-Way Fixed-Effects ANOVA

Coverage Performance of the Non-Central F-based and Percentile Bootstrap Confidence Intervals for Root Mean Square Standardized Effect Size in One-Way Fixed-Effects ANOVA

One−way Anova Example

In this example, we reanalyze the soybean yield data, but treat the explanatory variable as a categorical (ordinal) variable with 4 levels and use one−way anova to test for a significant SO2 effect. We can think of the data as samples from four different populations, corresponding to the SO2 concentration the chambers received, with possibly different means. In each treatment group we have 3 ob...

Robust One-Way ANOVA under Possibly Non-Regular Conditions

Consider the one-way ANOVA problem of comparing the means m1; m2; . . . ; mc of c distributions F1…x† ˆ F…x ÿ m1†; . . . ; Fc…x† ˆ F…x ÿ mc†. Solutions are available based on (i) normal-theory procedures, (ii) linear rank statistics and (iii) M-estimators. The above model presupposes that F1; F2; . . . ; Fc have equal variances (ˆ homoscedasticity). However practising statisticans content that ...

Understanding the One-way Anova

The One-way Analysis of Variance (ANOVA) is a procedure for testing the hypothesis that K population means are equal, where K > 2. The One-way ANOVA compares the means of the samples or groups in order to make inferences about the population means. The One-way ANOVA is also called a single factor analysis of variance because there is only one independent variable or factor. The independent vari...