![]() In such cases ANOVA becomes more reliable and effective. It is not reliable when there are more than two samples to be compared. The t-test compares the means between two samples and is also easy to conduct. There are only two tests which can yield the same results – t-test and ANOVA. This is because it is easy to compute manually using simple algebra rather than complex calculations. ![]() Though there are different methods like hypothesis testing, partitioning of sum of squares and others ANOVA is given the first preference these days. The null hypothesis of ANOVA states that all means are equal and the alternative hypothesis states that at least one is different. ANOVA assesses the importance of one or more factors by comparing the variable means at different factor levels. It is used to test the specific differences between the means. It is a statistical method which is used to test the difference between two or more means. Every ANOVA table I’ve ever generated would list the 5 factors and give me a P value for each one instead of lumping them all together and giving me a P value for the Main Effects as though they were just a single factor with 5 degrees of freedom.ANOVA stands for Analysis of Variance. The other thing I find disturbing (this may be nothing more than the fact that I don’t know Minitab) is the ANOVA table. Since you don’t the only way I know to check for term significance (given that you want to look at interactions and main effects) is to run a forward selection with replacement. My understanding of Eric’s post was that he was recommending a modification of the backward elimination procedure which would work if you had df for error. ![]() for the error are identical to those of the two way in your earlier post. If you take a look at your two tables all you have done is arbitrarily declared all two way interactions to be error terms – notice the df, SS Adj SS, etc. ![]() I suppose you could say that you have solved the degrees of freedom issue for error by using this approach but I don’t think it is a particularly satisfactory solution. ![]()
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