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Examples of the the word, multivariate , in a Sentence Context

The word ( multivariate ), is the 16727 most frequently used in English word vocabulary

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  1. Methods employed by some I–O psychologists include logistic regression, multivariate ,analysis of variance, structural equation modeling, and hierarchical linear
  2. Predictor variables should lie among the dominant principal components of the, multivariate ,distribution of the predictor variables. The partial least squares regression
  3. Mathematics,Hilbert's basis theorem, states that every ideal in the ring of, multivariate ,polynomials over a Northern ring is finitely generated. This can be
  4. Analysis, and how they relate to each other. The practical implementation of, multivariate ,statistics to a particular problem may involve several types of univariate and
  5. In mathematical number theory. * In statistics,Wilks's lambda is used in, multivariate ,analysis of variance (ANOVA analysis) to compare group means on a
  6. Analysis when the normal distribution is appropriate to a dataset. These, multivariate ,distributions are:::: The Inverse-Wishart distribution is important in Bayesian
  7. Of two variables on a regular grid ** Lances resampling (" Santosh" ): a, multivariate ,interpolation method used to compute new values for any digitally sampled data
  8. Of using this parameterization is in the study of conditional distributions in, multivariate ,normal case. The question of which normal distribution should be called the "
  9. Distribution, generalising Student's t-distribution, that is used in, multivariate ,hypothesis testing. Software & Tools There are an enormous number of software
  10. Test statistic needs to be modified if a similar test is to be applied to, multivariate ,data. This is not necessarily straightforward because one may note that the
  11. Polynomials can involve more than one variable, in which they are called, multivariate , Rings of polynomials in a finite number of variables are of fundamental
  12. Mathematics, such as linear algebra, complex analysis, differential geometry, multivariate ,calculus, group theory, and more. A familiar example is the concept of the
  13. i. e., independence or non-interaction). Basic mathematical definition For a, multivariate ,function y = f (x_1,x_2,\dots, x_n),functional decomposition generally
  14. Cumulative distribution functions are also used to specify the distribution of, multivariate ,random variables. Definition For every real number x, the cumulative
  15. Bicycling to work decreased risk of mortality in approximately 40 % after, multivariate ,adjustment, including leisure time physical activity ". Injuries (to cyclists
  16. A univariate polynomial, a polynomial in more than one variable is called a, multivariate ,polynomial. These notions refer more to the kind of polynomials one is
  17. Variables and their relevance to the actual problem being studied. In addition, multivariate ,statistics is concerned with multivariate probability distributions, in terms
  18. Map will no doubt engage a reader. Information richness or a map that is, multivariate ,shows relationships within the map. Showing several variables allows comparison
  19. Been generalized further to other mathematical structures, such as knots and, multivariate ,polynomials. The Euclidean algorithm has many theoretical and practical
  20. If both its real and imaginary components jointly possess a 2k-dimensional, multivariate , normal distribution. The variance-covariance structure of X is described by two
  21. Neural networks extend regression and clustering methods to non-linear, multivariate ,models. Important probability distributions There is a set of probability
  22. For example simple linear regression and correlation, are special cases of, multivariate ,statistics in which two variables are involved. Multivariate statistics
  23. The different aims and background of each of the different forms of, multivariate ,analysis, and how they relate to each other. The practical implementation of
  24. Case in general the iso-density contours are ellipsoids. A special case is the, multivariate ,normal distribution. The elliptical distributions are important in finance
  25. Then or equal to x and that Y takes on a value less than or equal to y. Every, multivariate ,CDF is: # Monotonically non-decreasing for each of its variables #
  26. Hat\zeta. Multivariate Cauchy distribution A random vector is said to have the, multivariate ,Cauchy distribution if every linear combination of its components Y = a1X1 +
  27. Been used extensively in the statistical literature: * Iris flower data set -, multivariate ,data set introduced by Ronald Fisher (1936). * Categorical data analysis -
  28. Probability distributions There is a set of probability distributions used in, multivariate ,analyses that play a similar role to the corresponding set of distributions
  29. Tools There are an enormous number of software packages and other tools for, multivariate ,analysis, including: * http://tmva.sourceforge.net: MVA - Toolkit for
  30. Explain as much variance as possible in another (independent) set. It is a, multivariate ,analogue of regression. # Correspondence analysis (CA),or reciprocal
  31. OLD estimate. GLS estimates are maximum likelihood estimates when ε follows a, multivariate ,normal distribution with a known covariance matrix. Adaptive estimation. If we
  32. Should have a univariate Cauchy distribution. The characteristic function of a, multivariate ,Cauchy distribution is given by:: \phi_X (t) = ex, \! Where \, x_0\, ( t)
  33. And analysis of more than one statistical variable. The application of, multivariate ,statistics is multivariate analysis. Methods of bivariate statistics, for
  34. Then on the joint probability distribution of y and X, which is the domain of, multivariate ,analysis. Linear regression was the first type of regression analysis to be
  35. Do not contain any variables at all. It is possible to further classify, multivariate ,polynomials as bivariate, trivariate, and so on, according to the maximum
  36. Less coronary heart disease. The same study also found no correlation (with, multivariate ,adjustment) between animal fat intake and coronary heart disease (table 4).
  37. Of the N_j to be treated independently, and it can be shown using the tools of, multivariate ,calculus that the equilibrium condition is given by: \franc 0 and \franc 0 (For
  38. Problem being studied. In addition, multivariate statistics is concerned with, multivariate ,probability distributions, in terms of both:: *how these can be used to
  39. Twice as many losses would therefore be charged twice as much. More complex, multivariate ,analyses are sometimes used when multiple characteristics are involved and a
  40. Is to present survey results by means of statistical models in the form of a, multivariate ,distribution mixture. The statistical information in the form of conditional
  41. A particle without electrical charge * Neutral vector (statistics),a, multivariate ,random variable is neutral if it exhibits a particular type of statistical
  42. Of some infinite-dimensional Hilbert space H, and thus are the analogues of, multivariate ,normal vectors for the case. A random element is said to be normal if for any
  43. Example. Hilbert's tenth problem asked for an algorithm to determine whether a, multivariate ,polynomial equation with integer coefficients has a solution in the integers.
  44. Then one statistical variable. The application of multivariate statistics is, multivariate ,analysis. Methods of bivariate statistics, for example simple linear regression
  45. To its location. Sets of central moments can be defined for both univariate and, multivariate ,distributions. Univariate moments The KTM moment about the mean (or KTM
  46. And Karl Mar that the roots of any polynomial may be expressed in terms of, multivariate ,hypergeometric functions. Ferdinand von Lineman and Hiroshi Memory showed
  47. Respectively; the tenth was to produce a method that could decide whether a, multivariate ,polynomial equation over the integers has a solution. Subsequent work to
  48. Distribution is important in Bayesian inference, for example in Bayesian, multivariate ,linear regression. Additionally,Hotelling's T-squared distribution is a
  49. Linear interpolation (see below) is almost as easy, but in higher dimensional, multivariate ,interpolation, this could be a favorable choice for its speed and simplicity.
  50. Statistics to a particular problem may involve several types of univariate and, multivariate ,analysis in order to understand the relationships between variables and their

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