Assessing the assumption of multivariate normality is required by many parametric multivariate statistical methods, such as MANOVA, linear discriminant analysis, principal component analysis, canonical correlation, etc. It is important to assess multivariate normality in order to proceed with such statistical methods. There are many analytical methods proposed for checking multivariate normalit...

Assumption for 1 sample T test: Data are normally distributed. We have discussed in the last article on how to check the normality assumption of a quantitative data. One issue being highlighted was that these formal normality tests are very sensitive to the sample size of the variable concerned. As seen here, table II shows that the normality assumptions for both the systolic and diastolic bloo...

Many statistical tests require data to be approximately normally distributed. Usually, the first step of data analysis is to test the normality. Also, we often test the normality of residuals after fitting a linear model to the data in order to ensure the normality assumption of the model is satisfied. SAS has offered four statistical tests that provide an easy way to test the normality. Howeve...

Many parametric statistical inferential procedures in finite samples depend crucially on the underlying normal distribution assumption. Dozens of normality tests are available in the literature to test the hypothesis of normality. Availability of such a large number of normality tests has generated a large number of simulation studies to find a best test but no one arrived at a definite answer ...

In this study, Wilks’ Λ (W), Hotelling-Lawley Trace (H) and Pillai’s (P) tests which are used in testing of statistically significance for canonical correlation coefficients were compared terms actual type I error rate. As a result 10000 simulation experiments conducted, when samples taken from multivariate distributions normal deviate slightly or moderately normality, the W test was conservati...

To fast approximate maximum likelihood estimators with massive data, this paper studies the Optimal Subsampling Method under A-optimality Criterion (OSMAC) for generalized linear models. The consistency and asymptotic normality of estimator from a general subsampling algorithm are established, optimal probabilities A- L-optimality criteria derived. Furthermore, using Frobenius norm matrix conce...

In this paper we define almost rg-normality and mild rg-normality, continue the study of further properties of rgnormality. We show that these three axioms are regular open hereditary. Also define the class of almost rg-irresolute mappings and show that rg-normality is invariant under almost rg-irresolute M-rg-open continuous surjection. AMS Subject Classification: 54D15, 54D10.

Using the results of several extremely large recent computations [17] we tested positively the normality of a prefix of roughly four trillion hexadecimal digits of π. This result was used by a Poisson process model of normality of π: in this model, it is extraordinarily unlikely that π is not asymptotically normal base 16, given the normality of its initial segment.

This paper considers the estimation and inference procedures for case of a logistic panel regression model with interactive fixed effects, where multiple individual effects are allowed is capable capturing high-dimensional cross-section dependence. The proposed also allows heterogeneous coefficients. New Bayesian non-Bayesian approaches introduced to estimate parameters. We investigate asymptot...