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Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable, i.e., multivariate random variables. Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to ...
Pseudoreplication due to correlation of samples: without accounting for correlation the 90% confidence interval for the sample mean is much too small. To get around this problem for example the blocking method can be applied where correlated samples are first grouped, then the (for each block) the corresponding sample means are computed. From ...
Replication (statistics) Representation validity; Reproducibility; Resampling (statistics) Rescaled range; Resentful demoralization – experimental design; Residual. See errors and residuals in statistics. Residual sum of squares; Response bias; Response rate (survey) Response surface methodology; Response variable; Restricted maximum likelihood
In statistics, multivariate adaptive regression splines (MARS) is a form of regression analysis introduced by Jerome H. Friedman in 1991. [1] It is a non-parametric regression technique and can be seen as an extension of linear models that automatically models nonlinearities and interactions between variables.
In statistics, response surface methodology (RSM) explores the relationships between several explanatory variables and one or more response variables. RSM is an empirical model which employs the use of mathematical and statistical techniques to relate input variables, otherwise known as factors, to the response.
In statistics, a central composite design is an experimental design, useful in response surface methodology, for building a second order (quadratic) model for the response variable without needing to use a complete three-level factorial experiment.
In the examples listed above, a nuisance variable is a variable that is not the primary focus of the study but can affect the outcomes of the experiment. [3] They are considered potential sources of variability that, if not controlled or accounted for, may confound the interpretation between the independent and dependent variables. To address ...
In statistics, the two-way analysis of variance (ANOVA) is an extension of the one-way ANOVA that examines the influence of two different categorical independent variables on one continuous dependent variable. The two-way ANOVA not only aims at assessing the main effect of each independent variable but also if there is any interaction between them.