Statistical Inference: Statistical Inference is concerned with the various tests of significance for testing hypothesis in order to determine with what validity data can be said to indicate some conclusion or conclusions. It is also concerned with the estimation of values. It is mainly on the basis of inferential analysis that the task of interpretation is performed. In modern times, with the availability of computer facilities, there has been a rapid development of multivariate analysis which may be defined as all statistical methods which simultaneously analyze more than two variables on a sample of observations. Usually the following analyses are involved when we make a reference of multivariate analysis:

i) Multiple Regression Analysis

ii) Multiple Discriminant Analysis

iii) Multivariate Analysis of Variance

iv) Canonical Analysis

a) Chi-Square Test: The chi-square test is an important test amongst the several tests of significance developed by statisticians. Chi-square, symbolically written as X2 is a statistical measure used in the context of sampling analysis for comparing variance to a theoretical variance. As a “non-paramatric test”, it can be used to determine if categorical data shows dependency or the two classifications are independent. It can also be used to make comparisons between theoretical population and actual data when categories are used. Thus Chi square is applicable in large number of problems.