calculating degrees of freedom mixed factor anova

Even if its not, look at the number of degrees of freedom from a conventional ANOVA calculation here using the number of experiments you have. Your F may be larger than that but I could be wrong. Lastly, in a repeated measures ANOVA with one factor and one subject term, the df are: df(factor) = number of levels - 1 df(subject) = number of subjects - 1 df(error) = df(factor) x df(subject) and df(total) = total number of observations - 1. The cutoff there is going to be around 20. Similarly, in a two-way ANOVA with two factors and one error term, the df are: df(factor 1) = number of levels of factor 1 -1 df(factor 2) = number of levels of factor 2 -1 df(interaction) = df(factor 1) x df(factor 2) and df(error) = total number of observations - number of levels of factor 1 x number of levels of factor 2. For example, in a one-way ANOVA with one factor and one error term, the df are: df(factor) = number of levels - 1 df(error) = total number of observations - number of levels and df(total) = total number of observations - 1. If you have interactions or other sources of variation, such as error or subject, you need to adjust the formula accordingly. However, this formula only applies to the main effects of each factor. For instance, if you have a factor with 3 levels, such as treatment A, B, and C, then the df for that factor is 2. We are focusing conceptually on calculating the 3 Fs for a two-way factorial ANOVA. The basic formula for degrees of freedom (df) in ANOVA is df = number of levels - 1, where levels are the categories or groups within a factor or source of variation. degrees of freedom Factor B dfB (kB -1) where kB is number of levels.

Calculating degrees of freedom mixed factor anova