Multivariate analysis definition, and objectives

Multivariate Analysis 

Definition

Multivariate analysis are set of statistical techniques used by a researcher to test the hypothesis set on  multiple variables of sampling unit or sampling units of his experiment or research design.

Examples:
1. Sampling unit : Learners
variables: Grades in mathematics, statistics, big data analytics, database management.
2. Sampling unit; Patient
Variables: heart rate, Body mass index, weight, height

 Learning Objectives:

After studying this chapter learner will be able to :

1. know the purposes, assumptions , and limitations of multiple techniques.

2. Identify appropriate techniques for data analysis using multivariate techniques.

3. Interpret the output of software to gain meaningful insights.

Objectives of Multivariate analysis:

1. To understand the relationship between several dependent variables and several independent variables.

2. It identify the data structures of multiple variables.

3. It helps in classifying and categorizing the data.

4. Multivariate techniques helps in data reduction.

Multivariate data analysis questions for examinations

1.  Discuss the importance of factor analysis in data reduction.
2. What is the difference between varimax and equimax in factor analysis.
3. Explain rotated component matrix in factor analysis
4. What do you mean by Eigenvalue?
5. Define communalities.
6. Explain principle component analysis.
7. Discuss the use of maximum likelihood function in the factor analysis.
8. Explain multivariate normal distribution.
9. Discuss tests of covariance matrices.
10. Explain the importance of discriminant analysis.
11. Elaborate the application of canonical correlation.
12. Explain multiple regression with an example.
13. Discuss the cluster analysis application in segmentation.
14. Distinguish between hierarchical clustering and tw ostep clustering.
15. Explain K- mean clustering.
16. Write a note on MANOVA.
17. What is Ginearal linear model and how it is different from Genaralized linear model.
18. How wilki's lambda used in multivariate analysis?
19. What do you mean by bootstrapping?
20. explain the latent structure discovery.
21. List any five tools of data mining.
22. Distinguish OLS and PLS regression.
23. write a note on SIMCA