DEFINATION:

“A collection of procedure for analyzing the association between two or more sets of measurement that were made of each object in one or more sample of objects”.

{Paul E Green}

These technique are empirical in nature. They analysis complex data collected from real life.

This technique crystallize large volume of data into smaller and more meaning scores that convey all relevant information.

This technique involves complex calculation.

Metric data:

Data measurement in An interval or ratio scale.

Non- Metric data:

Data measurement in nominal or ordinal scale.

Dependence Technique:

These are the technique that are used in situation where one or more then one variable are dependent on independent variables.

Interdependence Technique.

Explanatory variable and criterion variable.

Observable variable and latent variable.

Dummy Variable.

TYPE OF MULTIVARIATE TECHNIQUE

•Multivariate Regression Technique

•Multiple Discriminate Analyze

•Multiple Analysis of Variance

•LISREL

•Canonical Correlation Analysis

•Conjoint Analysis

•Factor Analysis

•Cluster Analysis

•Multidimensional Scaling

Latent Structure analysis

MRA is a measure of relationship and it involve a single dependent variable and two or more then two independent variable.

Form of multiple regression analysis modal is:

Y= a+ b 1 X1+ b2 X2 + b3 X3 +………….+b k X k +E

Y= Dependent Variable

X1, X2,………= Independent Variable

b1,…………….=Parameters

A=Constant

E= Error

Discriminate analysis is used for used for following purposes:

Classification of a group of people .

Examining if there are any significant differences between the group created.

Develop discriminate function that explain between the different categories.

Lastly to evaluate how accurate the classification has been.

The groups must be mutually exclusive with every case belonging to only one group.

All cases must be independent.

Group sizes of the dependent variable are not grossly different.

Independent variable are interval.

There should be absence of multi co linearity.

The discriminate function is represented by the following linear equation…….

Di = b0 +b1 X1 + b2 X 2 +……….+b k X k

Di = Score on discriminate function I .

b1, b 2…..= Discriminate coefficients.

b0 …..=Constant

X1, X2…..= Independent variable.