Academic Year: 2023/2024
Semester : Semester I
Semester : Semester I
- Teacher: Sachith Abeysundara
- Teacher: Malki Gunawardhana
- Teacher: Sachini Kulanayake
Academic Year: 2023/2024
Semester : Semester I
Semester : Semester I
- Teacher: Fathima Aqeelah
- Teacher: Seya Pehesarage
- Teacher: Sahasri Sewwandi
- Teacher: Imangi Wickramasinghe
- Teacher: Roshan Yapa
Academic Year: 2023/2024
Semester : Semester I
Semester : Semester I
- Teacher: Sachith Abeysundara
- Teacher: Fathima Aqeelah
- Teacher: Malima Atapattu
- Teacher: Mahasen Dehideniya
- Teacher: Head - Department of Statistics and Computer Science
- Teacher: Malki Gunawardhana
- Teacher: Lakshika Nawarathna
- Teacher: Ruwan Punchi-Manage
- Teacher: Jagath Senarathne
- Teacher: Roshan Yapa
- Teacher: Sachith Abeysundara
- Teacher: Sahasri Sewwandi
Academic Year: 2024/2025
Semester: Semester II
Elementary Theorems on Linear and Matrix Algebra, Partitioned Matrices, Nonnegative Matrices; Generalized Inverses of Matrix; Solutions of Linear Equations; Idempotent Matrices, Trace of Matrices; Derivatives of Quadratic Forms, Expectation of Matrix, Multivariate Normal Distribution, Distribution of Quadratic Forms; General Linear Model, Optimal Estimation and Hypothesis Testing Procedures for the General Linear Model, Applications to Regression Models. Application of Optimal Inference Procedures for the General Linear Model to Multifactor Analysis of Variance, Experimental Design Models, Analysis of Covariance, Split-Plot Models, Repeated Measures Models, Mixed Models,
Variance Component Models.
Prerequisites: ST A2033Recommended Texts:
1. Linear Models, S. R. Searle (1971) - John Wiley.
2. Linear Models, Least Squares and Alternatives, C.R. Rao and H. Toutenburg - Springer
3. Theory and Application of the Linear Model, F. A. Graybill (1976) - Duxbury.
4. Linear Regression Analysis, G.A.F. Seber (1977) - John Wiley.
To refresh your knowledge,
1. follow the Coursera course (https://www.coursera.org/learn/regression-models)
2. watch videos on YouTube.
Some recommended videos:
Regression Analysis:
Inference:
Tentative Course Evaluation
Tutorials and Assignments - 30%
End Semester Examination - 70%
Semester: Semester II
Elementary Theorems on Linear and Matrix Algebra, Partitioned Matrices, Nonnegative Matrices; Generalized Inverses of Matrix; Solutions of Linear Equations; Idempotent Matrices, Trace of Matrices; Derivatives of Quadratic Forms, Expectation of Matrix, Multivariate Normal Distribution, Distribution of Quadratic Forms; General Linear Model, Optimal Estimation and Hypothesis Testing Procedures for the General Linear Model, Applications to Regression Models. Application of Optimal Inference Procedures for the General Linear Model to Multifactor Analysis of Variance, Experimental Design Models, Analysis of Covariance, Split-Plot Models, Repeated Measures Models, Mixed Models,
Variance Component Models.
Prerequisites: ST A2033Recommended Texts:
1. Linear Models, S. R. Searle (1971) - John Wiley.
2. Linear Models, Least Squares and Alternatives, C.R. Rao and H. Toutenburg - Springer
3. Theory and Application of the Linear Model, F. A. Graybill (1976) - Duxbury.
4. Linear Regression Analysis, G.A.F. Seber (1977) - John Wiley.
To refresh your knowledge,
1. follow the Coursera course (https://www.coursera.org/learn/regression-models)
2. watch videos on YouTube.
Some recommended videos:
Regression Analysis:
Inference:
Tentative Course Evaluation
Tutorials and Assignments - 30%
End Semester Examination - 70%
- Teacher: Malki Gunawardhana
- Teacher: Pushpa Wijekoon