ST407 : Applied Multivariate Analysis

Department

Department of Statistics

Academic Program

Bachelor in Statistics

Type

Compulsory

Credits

03

Prerequisite

ST305ST400

Overview

Introduce students to a variety of basic ideas in multivariate analysis.

2- Focus on intuitive understanding and applications of multivariate methods for datasets using R, Matlab, Minitab

3- Introducing the student to basic compounds, factor analysis and cluster analysis.

4- Introducing the student to the legal association.

Intended learning outcomes

1. (Course intended learning outcomes)

a. Knowledge (& understand)

A.1

Interpret mathematical terms related to multivariate analysis.

A.2

Understand the importance of normal distribution in the analysis of multiple variables.

A.3

The student's understanding of cluster analysis and legal analysis.

A.4

Understand basic meanings and applications using statistical software.

In. Mental skills)

B.1

Ability to link some statistical concepts to multivariate analysis.

B.2

The student becomes able to deduce scientific indicators that determine the dimensions of the phenomenon under study.

B.3

Ability to understand cluster analysis and legal analysis.

B.4

Expand the student's mental perceptions and know how to use appropriate data analysis.

c. Practical & Professional Skills)

C.1

The use of multiple analysis methods in applied research.

C.2

The student performs work of an analytical and theoretical nature.

C.3

The student will be able to use cluster and legal analysis.

C.4

The use of ready-made computer programs in multivariate analysis.

W. Generic and transferable skills)

D.1

The ability to understand the methods of multivariate analysis.

D.2

Ability to use ready-made computer programs in multivariate analysis.

D.3

The ability to understand the concept of main compounds and analyze factors theoretically and practically.

D.4

Work as a team to solve specific problems when analyzing multivariates.

Teaching and learning methods

...Lectures

· Exercises

· Ready-made statistical programs

Methods of assessments

(Assessment table)

Rating No.

Evaluation methods

Evaluation Duration

Evaluation weight

Percentage

Rating Date (Week)

Reviews

First Assessment

Midterm exam

20

20%

7

Second Assessment

Half Exercises

10

10%

Third Assessment

Second midterm exam

20

20%

14

Fourth Assessment

Final Exam

50

50%

16

Total

100 degree

100%

(Course contents )

Scientific topic

Number of Hours

Lecture

laboratory

Exercises

discussion

Number of weeks

مقدمة في البيانات متعددة المتغيرات ، R و Matlab.

8

4

3

1

2

التوزيع الطبيعي متعد المتغيرات.

8

4

2

2

2

المركبات الرئيسة.

8

4

3

1

2

تحليل عوامل.

8

2

5

1

2

التحليل العنقودي

8

4

3

1

2

MANOVA والتحليل التمييزي.

8

3

4

1

2

تحليل الارتباط القانوني.

8

4

2

1

2

(References )

Bibliography

Publisher

Version

Author

Where it is located

Rapporteur notes

Explanatory note prepared by the course instructor

Textbooks

An R and S-plus companion to multivariate analysis.

Brain Everitt,

Library