Statistical Size

The analytics software developer SAS is pleased to announce the availability of JMP Pro 15.2.1 is a maintenance release that contains bug fixes, addresses reproducible crashes, and improves performance. Applying this maintenance release is recommended for all users.

SAS JMP has released JMP Statistical Discovery Pro 18.0 is the data analysis tool of choice that scientists and engineers rely on for powerful analytic capabilities and dynamic statistical discovery.

SAS JMP has released JMP Statistical Discovery Clinical 18 is an analytical software application that makes it easy to review clinical trial data.

The analytics software developer SAS is pleased to announce the availability of JMP Pro 15.2.1 is a maintenance release that contains bug fixes, addresses reproducible crashes, and improves performance. Applying this maintenance release is recommended for all users.

Your Complete Guide to Statistical Data Analysis and Visualization For Practical Applications in R

Learn the foundation of Data Science, Analytics and Data interpretation using statistical tests with real world examples

Provides state-of-the-art coverage for the researcher confronted with designing and executing a simulation study using continuous multivariate distributions. Concise writing style makes the book accessible to a wide audience. Well-known multivariate distributions are described, emphasizing a few representative cases from each distribution. Coverage includes Pearson Types II and VII elliptically contoured distributions, Khintchine distributions, and the unifying class for the Burr, Pareto, and logistic distributions. Extensively illustrated–the figures are unique, attractive, and reveal very nicely what distributions ``look like.'' Contains an extensive and up-to-date bibliography culled from journals in statistics, operations research, mathematics, and computer science.

In the last few decades the accumulation of large amounts of in­ formation in numerous applications. has stimtllated an increased in­ terest in multivariate analysis. Computer technologies allow one to use multi-dimensional and multi-parametric models successfully. At the same time, an interest arose in statistical analysis with a de­ ficiency of sample data. Nevertheless, it is difficult to describe the recent state of affairs in applied multivariate methods as satisfactory. Unimprovable (dominating) statistical procedures are still unknown except for a few specific cases. The simplest problem of estimat­ ing the mean vector with minimum quadratic risk is unsolved, even for normal distributions. Commonly used standard linear multivari­ ate procedures based on the inversion of sample covariance matrices can lead to unstable results or provide no solution in dependence of data. Programs included in standard statistical packages cannot process 'multi-collinear data' and there are no theoretical recommen­ dations except to ignore a part of the data. The probability of data degeneration increases with the dimension n, and for n > N, where N is the sample size, the sample covariance matrix has no inverse. Thus nearly all conventional linear methods of multivariate statis­ tics prove to be unreliable or even not applicable to high-dimensional data.