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MS4216 Applied Multivariate Methods
Part I
Course Duration : One Semester
Credit Units : 3
Level: B4
Medium of Instruction: English
Prerequisite(s) : Nil
Precursors(s) : MS3215 Linear Models
Equivalent Course(s) : Nil
Exclusive Course(s) : Nil
Part II
Course Aims:
- Develop students’ ability to analyse real-life multivariate data and solve business problems which are multivariate in nature especially in marketing research area.
Course Intended Learning Outcomes (CILOs)
Upon successful completion of this course, students should be able to:
| No. | CILOs | Weighting | | 1 | Describe the purpose and the procedure of conducting commonly used multivariate techniques and the difference among the techniques | - | | 2 | Apply the concepts and methods of multivariate analysis to analyse multivariate data and solve problems in marketing research area and company problems related to marketing research and survey data | - |
Teaching and Learning Activities (TLAs)
(Indicative of likely activities and tasks designed to facilitate students’ achievement of the CILOs. Final details will be provided to students in their first week of attendance in this course)
| CILO No. | TLAs | Hours/week (if applicable) | | 1,2 | 1. Interactive Lectures
Students listen to the concepts and methods of multivariate analysis, with emphasis on real-life applications primarily in the marketing research area. Students discuss the major issues arising from the applications and case study problems | - | | 1,2 | 2. Computer Lab
Students practice the use of computer software to solve problems which are multivariate in nature and discuss the major issues arising from the applications and the use of computer software | - |
Constructive Alignment of CILOs and TLAs
| | TLA 1 | TLA 2 | Hours/week | | CILO 1 | ü | ü | - | | CILO 2 | ü | ü | - |
Assessment Tasks
(Indicative of likely activities and tasks designed to assess how well the students achieve the CILOs. Final details will be provided to students in their first week of attendance in this course)
| CILO No | Type of Assessment Tasks/Activities | Assessment Details | Weighting (if applicable) | | 1,2 | 1. Homework Assignments | Students work on homework assignments based on the concepts and the techniques they learn each week. In the last assignment, students write and reflect upon their learning experiences and challenges. | 0-50% | | 1,2 | 2. Group project | Students work in teams to apply multivariate methods to solve problems which are multivariate in nature and submit a written report. | 0-50% | | 1,2 | 3. Test | A mid-term test is designed to assess students’ professional knowledge of the concepts, the techniques and the applications they have learned in the past weeks. | 0-50% | | 1,2 | 4. Written examination (2 hours) | The examination is designed to assess students’ professional knowledge of the concepts, the techniques and the applications they have learned in the whole course. | 50% |
Constructive Alignment of CILOs and Assessment Tasks
| | AT1 | AT2 | AT3 | AT4 | | CILO 1 | ü | ü | ü | ü | | CILO 2 | ü | ü | ü | ü |
Grading of Student Achievement :Refer to Grading of Courses in the Academic Regulations (Attachment) and to the Explanatory Notes.
AT1: HomeworkAssignments
| Letter Grade | Grade Point | Grade Definitions | | A+
A
A- | 4.3
4.0
3.7 | Excellent: | Strong evidence of knowing how to apply the key concepts and techniques, and ability to use the appropriate computer software in performing data analysis and model building. | B+
B
B- | 3.3
3.0
2.7 | Good: | Evidence of knowing how to apply the key concepts and techniques, and ability to use the appropriate computer software in performing data analysis and model building. | C+
C
C- | 2.3
2.0
1.7 | Adequate: | Some evidence of knowing how to apply the key concepts and techniques, and ability to use the appropriate computer software in performing data analysis and model building. | | D | 1.0 | Marginal: | Sufficient familiarity with the subject matter to enable the student to progress without repeating the assignment | | F | 0.0 | Failure: | Little evidence of familiarity with the subject matter; |
AT2: Group Project
| Letter Grade | Grade Point | Grade Definitions | | A+
A
A- | 4.3
4.0
3.7 | Excellent: | Strong evidence of original thinking; good organization, capacity to analyse and synthesize; superior application of subject matter; evidence of extensive knowledge base. Highly effective use of language and excellent presentation skills. | B+
B
B- | 3.3
3.0
2.7 | Good: | Evidence of being able to apply the subject matter; evidence of critical capacity and analytic ability; reasonable understanding of issues; relevant use of literature. Effective use of language and good presentation skills. | C+
C
C- | 2.3
2.0
1.7 | Adequate: | Some evidence of being able to apply the subject matter; some evidence of critical capacity and analytic ability; some evidence of understanding the issues; ability to develop solutions to simple problems. Adequate command of the language and presentation skills. | | D | 1.0 | Marginal: | Sufficient ability to apply the subject matter to enable the student to progress without repeating the project. Inadequate command of the language and little presentation skills. | | F | 0.0 | Failure: | Little or no evidence of being able to apply the subject matter; weakness in critical and analytic skills; limited or irrelevant use of literature. Poor use of the language and presentation skills. |
AT3: Test
| Letter Grade | Grade Point | Grade Definitions | | A+
A
A- | 4.3
4.0
3.7 | Excellent: | Strong evidence of original thinking; good organization, capacity to analyse and synthesize; superior grasp of subject matter; evidence of extensive knowledge base. | B+
B
B- | 3.3
3.0
2.7 | Good: | Evidence of grasp of subject, some evidence of critical capacity and analytic ability; reasonable understanding of issues; evidence of familiarity with literature. | C+
C
C- | 2.3
2.0
1.7 | Adequate: | Some evidence of understanding of the subject; ability to perform basic statistical model building and data analysis for marketing research. | | D | 1.0 | Marginal: | Adequate familiarity with the subject matter; shows marginal ability to perform basic statistical model building and data analysis for marketing research. | | F | 0.0 | Failure: | Little evidence of familiarity with the subject matter; weakness in critical and analytic skills; limited or irrelevant use of literature. |
AT4: Written Examination
| Letter Grade | Grade Point | Grade Definitions | | A+
A
A- | 4.3
4.0
3.7 | Excellent: | Strong evidence of original thinking; good organization, capacity to analyse and synthesize; superior grasp of subject matter; evidence of extensive knowledge base. | B+
B
B- | 3.3
3.0
2.7 | Good: | Evidence of grasp of subject, some evidence of critical capacity and analytic ability; reasonable understanding of issues; evidence of familiarity with literature. | C+
C
C- | 2.3
2.0
1.7 | Adequate: | Some evidence of understanding of the subject; ability to perform basic statistical model building and data analysis for marketing research. | | D | 1.0 | Marginal: | Adequate familiarity with the subject matter to enable the student to progress without repeating the course. | | F | 0.0 | Failure: | Little evidence of familiarity with the subject matter; weakness in critical and analytic skills; limited or irrelevant use of literature. |
Part III
Keyword Syllabus:
1. Introduction
An overview of multivariate methods. The structure of data matrix. Summary statistics.
2. Multivariate Normal Distribution
Multivariate normal distribution and its properties. Methods of checking multivariate normal distribution assumption. Inference about mean vectors.
3. Multivariate Analysis of Variance
One-way multivariate analysis of variance (MANOVA).
4. Principal Components Analysis
Basic concepts of principal components. Estimation of principal components. Determining the number of principal components. How to perform principal components analysis using computer package. Interpretation of computer output. Business applications such as index construction.
5. Factor Analysis
Basic concepts of factor analysis. Methods of parameter estimation. Orthogonal and oblique rotations of factors. Estimation of factor scores. How to perform factor analysis using computer package. Interpretation of computer output. Factor analysis versus principal components analysis. Business applications such as attitude measurement.
6. Discriminant Analysis
Fisher's linear discriminant function. Optimal classification rules for two multivariate populations. Expected cost and total probability of misclassification. Quadratic discriminant function. Classification with several populations. Stepwise discriminant analysis. How to perform discriminant analysis using computer package. Interpretation of computer output. Business applications such as credit analysis, bankruptcy prediction.
7. Cluster Analysis
Distance and similarity measures. Hierarchical clustering methods. Non-hierarchical methods. Dendrogram. How to perform cluster analysis using computer package. Interpretation of computer output. Business applications such as market segmentation.
8. Multidimensional Scaling
Proximity measures. Metric and nonmetric methods. Geometrical representation. Optimal properties and goodness of fit measures. How to perform multidimensional scaling using computer package. Interpretation of computer output. Business applications such as product positioning.
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