| Description |
Program: BS
Course Code STAT-6118
Credit Hours 4 CR
Course Coordinator Qasim Ramzan
Introduction
This course will introduce the basic principlesof multivariate analysis by entertaining the both mathematical and applied approaches of problems. The course impart skills on the data collection, description measures of data, interpretation of results, model selection, decision making in the context of multivariate analysis. Course also provides the simultaneous model structure, their assumptions and mathematical derivations of multivariate statistical designs.
Pre requisites: -
Learning outcomes
- Provide rational bases for data collection, description measures of data, decision making, interpretation of results and model selection.
- Utilize this understanding to predict system behavior and improve system performance.
- Solving complex problems and improving quantitative decisions.
Textbooks
- Anderson, T.W. (2003). “An Introduction to Multivariate Statistical Analysis”, John Wiley, New York.
- Afifi, A. A. and Clark Virginia (2000). “Computer Aided Multivariate Analysis”, Lifetime learning publications, Belmont California
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Week
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Topics
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Books
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1
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Aspects of multivariate analysis, Assumptions behind multivariate structure.
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Anderson, T.W. (2003). “An Introduction to Multivariate Statistical Analysis”(1-4)
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2
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Organization of Data, Difference between univariate and multivariate statistical techniques.
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Anderson, T.W. (2003). “An Introduction to Multivariate Statistical Analysis”(5-10),
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3
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Matrix algebra and vector algebra
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Anderson, T.W. (2003). “An Introduction to Multivariate Statistical Analysis”(49-60),
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| 4 |
Mean vector and covariance matrix
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Anderson, T.W. (2003). “An Introduction to Multivariate Statistical Analysis”(68-75), |
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5
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maximum inequalities and maximization methods.
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Anderson, T.W. (2003). “An Introduction to Multivariate Statistical Analysis”(78-81),
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6
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Multivariate normal distribution, inferences about mean vector
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Anderson, T.W. (2003). “An Introduction to Multivariate Statistical Analysis”(149-160)
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7
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Estimation of the mean vector and covariance matrix.
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Anderson, T.W. (2003). “An Introduction to Multivariate Statistical Analysis”(168-177) + Assignment |
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8
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comparisons of several multivariate means
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Anderson, T.W. (2003). “An Introduction to Multivariate Statistical Analysis”(273-300)
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9
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Multivariate analysis of variance (MANOVA).
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Anderson, T.W. (2003). “An Introduction to Multivariate Statistical Analysis”(301-315)
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10
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Principal components analysis,
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Anderson, T.W. (2003). “An Introduction to Multivariate Statistical Analysis”(430-450)
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11
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Factor analysis,
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Anderson, T.W. (2003). “An Introduction to Multivariate Statistical Analysis”(481-500)
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12
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Discriminant analysis,
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Anderson, T.W. (2003). “An Introduction to Multivariate Statistical Analysis”(575-600)
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13
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Cluster analysis.
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Anderson, T.W. (2003). “An Introduction to Multivariate Statistical Analysis”(273-300) |
| 14 |
Multidimensional scaling. |
Assignment
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15
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Canonical Correlation
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Assignment |
| 16 |
Software work |
R software application |
Description of system of Evaluation
Exam: Mid (30%), Final (50%), Sessional (20%): Assignments, Presentations, Class Participation, Quizzes
Time Table: BS Regular:
| BS (8th Regualr) |
| monday |
12:00 PM to 1:00 PM |
| wednesday |
10:00 AM to 11:00 AM |
| thursday |
12:00 PM to 1:00 PM |
| friday |
9:00 AM to 10:00 AM |
BS Self :
| BS (8th SS) |
| monday |
2:00 PM to 3:00 PM |
| wednesday |
4:00 PM to 5:00 PM |
| thursday |
3:00 PM to 4:00 PM |
| friday |
3:00 PM to 4:00 PM |
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