Briefly, the variance for a variable is a measure of the dispersion or spread of scores. Covariance indicates how two variables vary together.
The variance-covariance matrix is a compact way to present data for your variables. The variance is presented on the diagonal (where the column and row intersect for the same variable), while the covariances reside above or below the diagonal.
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A variance-covariance matrix is a square matrix that contains the variances of variables on the diagonal and the covariances between each pair of variables off-diagonal. It is used to describe the relationships and variability between multiple variables in a dataset.
Calculating the mean helps to understand the central tendency of a data set, while calculating the variance provides information about the spread or dispersion of the data points around the mean. Together, the mean and variance provide a summary of the data distribution, enabling comparisons and making statistical inferences.
Variance, range, assortment, variety, medley, distinction... multiculturism
The term "matrix of domination" was coined by sociologist Patricia Hill Collins in her book "Black Feminist Thought." It refers to the interlocking systems of oppression such as race, gender, and class that shape and constrain individuals' experiences and identities.
The three conditions necessary for causation between variables are covariance (relationship between variables), temporal precedence (the cause must precede the effect in time), and elimination of plausible alternative explanations (other possible causes are ruled out).
assortment, dissimilarity, distinction, distinctiveness, divergence, diverseness, diversification, heterogeneity, medley, mixed bag*, multeity, multifariousness, multiformity, multiplicity, range, unlikeness, variance, variegation, variousness