Grade A+ 2023
PERMANOVA - -Multivariate test using permutations to cope with large sets of complex data
-MANOVA - -ANOVA with several dependent variables
-Overall multivariate test result - -If this test is significant, then we can assume that the effect of
the whole set of independent variables is significant
-Homogeneity of variances and Covariances - -An assumption for when you have a multivariate
design and you need to test variance between the different variables
-SIMPER - -What you do if a PERMANOVA delivers a significant overall result to find out
which variable is the most influential one yo
-nMDS plot - -Way of visually representing samples so you can see how far they are form each
other
-f-distribution - -
-1-way ANOVA - -Test to determine whether there are any significant differences between the
means of two or more independent groups
-2-way ANOVA - -Test to determine if there is an interaction between two independent variables
on the dependent variable
-Homodasticity - -Variances remaining even along the line of best fit
-Heterodasticity - -Variances being skewed along the line of best fit
-Model summary - -Used in linear regression analysis, shows R value which is simple correlation
and R^2 value which is variance correlation
-Linear regression analysis - -Test to observe and predict the relationship between two
continuous variables
-Varimax rotation - -Simplifies the pairings of variables in a component matrix so it's easier to
read
-Clustering, ordination, statistical test of hypotheses - -Key elements of multivariate analysis in
biology