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...

I. Definition and Applications...............................................................................2 II. Two-Way ANOVA prerequisites.......................................................................2 III. How to use the Two-Way ANOVA tool?...........................................................3 A. Parametric test, assume variances equal. .........................................

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...

ANOVA is one of the most important tools in comparing treatment means among different groups repeated measurements. The classical F test routinely used to if are same across groups. However, it inefficient when number or dimension gets large. We propose a smoothing truncation deal with this problem. It shown theoreticall...

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...

In this paper, we assume the error distribution of one-way ANOVA as alpha-skew normal distribution. Alpha-skew gives us exibility for modelling data which has heavy tailness, skewness, bimodality and also symmetricity. We obtain maximum likelihood estimator model parameters test statistics based on these estimators. Monte Carlo simulation study states that estimators interest are more efficient...