There are three methods, which are most commonly used to assess the bivariate normality of paired data, two of which are also used to assess the multivariate normality. Nevertheless, none of the methods is very efficient or conclusive in their assessment of bivariate normality. In this thesis we are proposing a new method to test bivariate normality. This new method makes use of a set of if and only if conditions inherent in the theory of bivariate normal distribution. The proposed new method is highly efficient, accurate, and very easy to apply using any available standard statistical software
Statistical analysis frequently relies on the assumption of normality. Though normality may often be...
The normal distribution is a widely used distribution and has a very important probability distribut...
Testing normality is one of the most studied areas in inference. Many methodologies have been propos...
The purposes of the thesis were to review some of the existing methods for testing normality and to ...
AbstractBased upon the idea of construction of data driven smooth tests for composite hypotheses pre...
Two statistics are considered to test the population correlation for non-normally distributed bivari...
Most parametric methods rely on the assumption of normality. Results obtained from these methods are...
This paper presents a new test for normality which is based on a complete characterization of the no...
Let (X, Y) be bivariate normal random vectors which represent the responses as a result of Treatment...
In this paper, we propose a test for bivariate normality in imperfectly observed models, based on th...
In many statistical studies the relationship between two random variables X and Y is investigated an...
A new method to conduct a right-tailed test for the correlation on bivariate non-normal distribution...
The importance of checking the normality assumption in most statistical procedures especially parame...
Many samples in the real world are very small in size and often do not follow a normal distribution....
Many inferential statistical tests require that the observed variables have a normal distribution. M...
Statistical analysis frequently relies on the assumption of normality. Though normality may often be...
The normal distribution is a widely used distribution and has a very important probability distribut...
Testing normality is one of the most studied areas in inference. Many methodologies have been propos...
The purposes of the thesis were to review some of the existing methods for testing normality and to ...
AbstractBased upon the idea of construction of data driven smooth tests for composite hypotheses pre...
Two statistics are considered to test the population correlation for non-normally distributed bivari...
Most parametric methods rely on the assumption of normality. Results obtained from these methods are...
This paper presents a new test for normality which is based on a complete characterization of the no...
Let (X, Y) be bivariate normal random vectors which represent the responses as a result of Treatment...
In this paper, we propose a test for bivariate normality in imperfectly observed models, based on th...
In many statistical studies the relationship between two random variables X and Y is investigated an...
A new method to conduct a right-tailed test for the correlation on bivariate non-normal distribution...
The importance of checking the normality assumption in most statistical procedures especially parame...
Many samples in the real world are very small in size and often do not follow a normal distribution....
Many inferential statistical tests require that the observed variables have a normal distribution. M...
Statistical analysis frequently relies on the assumption of normality. Though normality may often be...
The normal distribution is a widely used distribution and has a very important probability distribut...
Testing normality is one of the most studied areas in inference. Many methodologies have been propos...