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In statistics, the coefficient of multiple correlation is a measure of how well a given variable can be predicted using a linear function of a set of other variables. It is the correlation between the variable's values and the best predictions that can be computed linearly from the predictive variables. [1]
This is a list of statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables.
[Note 1] Descriptive statistics Nonparametric statistics Quality control Survival analysis Data processing Base stat. [Note 2] Normality tests [Note 3] CTA [Note 4] Nonparametric comparison, ANOVA: Cluster analysis Discriminant analysis BDP [Note 5] Ext. [Note 6]
The maximum number of independent variables in a model is 65,532 variables in Stata/MP, 10,998 variables in Stata/SE, and 798 variables in Stata/BE. [18] The pricing and licensing of Stata depends on its intended use: business, government/nonprofit, education, or student. Single user licenses are either renewable annually or perpetual.
Pearson's correlation coefficient is the covariance of the two variables divided by the product of their standard deviations. The form of the definition involves a "product moment", that is, the mean (the first moment about the origin) of the product of the mean-adjusted random variables; hence the modifier product-moment in the name.
In statistics, multiple correspondence analysis (MCA) is a data analysis technique for nominal categorical data, used to detect and represent underlying structures in a data set. It does this by representing data as points in a low-dimensional Euclidean space. The procedure thus appears to be the counterpart of principal component analysis for ...
In statistics, bivariate data is data on each of two variables, where each value of one of the variables is paired with a value of the other variable. [1] It is a specific but very common case of multivariate data. The association can be studied via a tabular or graphical display, or via sample statistics which might be used for inference.
Canonical analysis is a multivariate technique which is concerned with determining the relationships between groups of variables in a data set. The data set is split into two groups X and Y, based on some common characteristics. The purpose of canonical analysis is then to find the relationship between X and Y, i.e. can some form of X represent Y.