What Is CHI- SQUARE AS ANON-PARAMETRIC TEST ?

CHI-SQUARE AS ANON-PARAMETRIC TEST
 As a test of independence, χ2 test enables us to explain whether two attributes are associated. For case, we may be interested in knowing whether a new drug is effective in controlling fever or not, χ2 test will help us in deciding this issue. In such a situation, we do with the null thesis that the two attributes(viz., new drug and control of fever) are independent, which means that new drug isn't effective in controlling fever. On this base we first calculate the anticipated frequenters and also work out the value ofχ2. However, we conclude that null thesis stands which means that the two attributes are that the two attributes are independent or not associated, If the advised value of χ2 is lower than the table value at a certain position of significance for given degrees of freedom. X2 is also calculated as follows
 
 Styles of factor analysis
The following are the system of factor analysis are generally used
 i) The Centroid Method
 ii) The Principle element system
 iii) The Maximum liability Method
(i) Principal Component Method This system was developed by H. Hotelling and utmost of the computer software support this system. This system is simple to understand when compared to other styles.
 ii) Centroid Method This system was cooked by Thurston.
 iii) R- type and Q- type  Factor analysis may be of R- type or Q- type. In the former case, there's high correlation when replies who score grandly on variable also score grandly on variable 2 and replies who score downward on variable 1 also score downward on variable 2. A factor emerges when there's high correlation within a group of variables. In the after case, correlations are reckoned between brace of replies rather of brace of variables. High correlation occurs when replies 1’s pattern of responses on all variables is important, like replies 2’s. Factors also emerges when there 1s high correlation within the groups of people and this type of analysis is useful when the ideal is to sort out people into groups grounded on the contemporaneous responses to all the variables.
 introductory languages used in factor analysis
(a) Factor It's an underpinning dimension of several affiliated variables.
 (b) Factor lading Values which explain how nearly the variables are related to each one of the factors discovered simple factor variable correlations. The absolute size of the lading is important in the interpretation of a factor.
 (c) Commonality (h ²) This shows how important of each variable is reckoned for by underpinning factors taken together.
 d) Eugen value/ idle Root It's the sum of the squared value of factor ladings relating to a factor and indicates relative significance of each factor.
(e) Total sum of places Sum of Eugen values of all factors are the total sum of places.
 (f) reels It reveals the structure of the data. It may have either orthogonal or oblique. Orthogonal
 gyration is made when the factors are independent and an oblique gyration is made when the factors are identified.
(g) Factor Score It represents the degree to which each replies gets high scores on the group of particulars that load high on each factor. Factor scores are also used in other multivariate analysis.