Multivariate statistics are data analysis procedures that simultaneously consider more than two variables. Such procedures can be descriptive (e.g. examining the joint distribution of a group of variables) or inferential. Inferential procedures may examine differences in two or more variables across groups (e.g. multivariate analysis of variance), predict one variable using two or more independent variables (e.g. multiple regression, structural equation modeling), estimating relationships between a set of variables where two or more variables are dependent (e.g. path analysis, structural equation modeling). Multivariate analyses can be conducted at the observed level (e.g. multiple regression analysis, cluster analysis), or at the latent level (latent variable modeling). Procedures conducted at the observed level use the entire measured value of a variable (the manifest value), whereas latent procedures estimate the error of measurement of observed variables and takes this parameter into account in further analyses. Latent variable modeling relies on the assumption that a latent (unobservable) variable, or construct (e.g. intelligence), is underlying the data and explains the relationships between the observed variables (e.g. test scores).